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Everything you need to know about Computed Tomography (CT) & CT Scanning

Pancreas: Artificial Intelligence Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Pancreas ❯ Artificial Intelligence

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  • Results: A total of 546 patients with pancreatic cancer (mean age, 65 years 6 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm.”
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • “A deep learning tool for pancreatic cancer detection that was developed using contrast-enhanced CT scans obtained in 546 patients with pancreatic cancer and in 733 healthy control subjects achieved 89.9% sensitivity and 95.9% specificity in the internal test set (109 patients, 147 control subjects), which was similar to the sensitivity of radiologists (96.1%; P = .11). N In a validation set comprising 1473 individual CT studies (669 patients, 804 control subjects) from institutions throughout Taiwan, the deep learning tool achieved 89.7% sensitivity and 92.8% specificity in distinguishing pancreatic cancer, with 74.7% sensitivity for pancreatic cancers smaller than 2 cm.”  
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • “In a validation set comprising 1473 individual CT studies (669 patients, 804 control subjects) from institutions throughout Taiwan, the deep learning tool achieved 89.7% sensitivity and 92.8% specificity in distinguishing pancreatic cancer, with 74.7% sensitivity for pancreatic cancers smaller than 2 cm.”  
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • “In conclusion, this study developed an end-to-end deep learning–based computer-aided detection (CAD) tool that could accurately and robustly detect pancreatic cancers (PCs) on contrast-enhanced CT scans. The CAD tool may be a usefulsupplement for radiologists to enhance detection of PC. Ourresults also suggest that the classification convolutional neura networks might have learned the secondary signs of PC, whichwarrants further investigation. While the results of this studyprovide strong support for the generalizability of the CAD tool in the Taiwanese and perhaps Asian populations, the performance of the CAD tool in other populations needs to be evaluated further.”  
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • Summary
    A deep learning–based approach showed high diagnostic performance for identifying patients with solid and cystic pancreatic neoplasms at contrast-enhanced CT.
    Key Results  
    * In a retrospective study of 852 patients for training and two independent test sets comprising 1192 patients for validation, a deep learning (DL)–based approach to identify solid or cystic pancreatic lesions at CT showed an area under the receiver operating characteristic curve of 0.87–0.91.
    * The DL-based approach showed high sensitivity in identifying solid lesions of any size (98% [63 of 64 patients] to 100% [58 of 58 patients]) or cystic lesions measuring 1.0 cm or larger (92% [34 of 37 patients] to 93% [52 of 56 patients]).
    Deep Learning–based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT
    Hyo Jung Park, et al.
    Radiology 2022; 000:1–11 

  • Deep Learning–based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT
    Hyo Jung Park, et al.
    Radiology 2022; 000:1–11 
  • “As the future step, DL applications for pancreatic imaging should aim for accurate segmentation or detection of pancreatic lesions even in pancreases with diffuse abnormalities, suchas pancreatitis. Accurate and robust classification of pancreatic lesions (ie, differentiation of malignancy and benignity or classification among several common pancreatic tumors) should also become available, and such algorithms should be developed to perform as a standalone or second reader to facilitate the reading processes of radiologists. In addition, as the incorporation of DL algorithms in clinical practice is an important issue, the clinical feasibility of the DL algorithms should be further evaluated.”
    Deep Learning–based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT
    Hyo Jung Park, et al.
    Radiology 2022; 000:1–11 
  • “In conclusion, the deep learning–based approach demonstrated high diagnostic performance in identifying patients with various solid or cystic neoplasms at CT. Our approach has the potential to facilitate timely diagnoses and management of pancreatic lesions encountered in routine clinical practice.”
    Deep Learning–based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT
    Hyo Jung Park, et al.
    Radiology 2022; 000:1–11 
  • Background: Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT.
    Purpose: To develop and to validate a deep learning (DL)–based tool able to detect pancreatic cancer at CT.
    Conclusion: The deep learning–based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm.
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • Materials and Methods: Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test.
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • Results: In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm.”
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • “An automatic end-to-end deep learning–based detection tool could detect pancreatic cancer on CT scans in a nationwide real-world test data set with 91% accuracy, without requiring manual image labeling or preprocessing.”  
    Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study
    Po-Ting Chen et al.
    Radiology 2022; 000:1–11
  • Purpose
    A wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist.
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • Results
    214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist.  
    Conclusion
    Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.  
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • “A total of 488 radiomics features from the segmented volume were extracted to define cystic lesion and pancreas phenotypes based on venous phase images. Radiomics features used in this study included 14 first-order statistics of the volumetric CT intensities, 8 shape features of the target structure, 33 texture features from a gray-level co-occurrence matrix and a gray-level run-length matrix, 376 texture features from the 8 filtered volumes by wavelets, and an additional 47 texture features form the filtered volume by Laplacian of Gaussian (LoG). Ten image features were extracted from the whole pancreatic region. Table 2 represents the whole feature set used for cyst classification in this study. Two demographic features, age and gender, were also incorporated into the final model.”
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • “This study showed that a radiomics-based model can achieve equivalent performance as an experienced academic radiologist in the classification of a wide array of pancreatic cysts with variable malignant potential. This model has the potential to refine pancreatic cyst management by improving diagnostic accuracy of cystic lesions, which can minimize healthcare utilization while maximizing detection of malignant lesions. This study confirms the ability of a radiomic based model to accurately classify pancreatic cystic neoplasms. Further validation and clinical integration of this model could help optimize management of pancreatic cysts by maximizing the rate of detection of malignant lesions while reducing healthcare utilization.”
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • “Among the whole 490 features (488 radiomics features plus age and gender), thirty features were found to reduce redundancy by the minimum-redundancy maximum-relevancy feature selection based on mutual information, which showed the best classification performance, with AUC of 0.940. Age and gender were included in the model due to the known gender and gender associations for pancreatic cysts. These demographic features would be available to the radiologist at the time of exam, and this would simulate the real-world application. Age, median and mean intensities of the original images and wavelets, and fractal dimension were highly ranked for the classifications. Gender was ranked as 29th feature for the classification."
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • “In this study, the performance of the radiomics featurebased classification achieved AUC of 0.940 in distinguishing among five types of pancreatic cystic neoplasms. The performance was similar to previous studies with multi-class pancreatic cyst classifications that included three or four cyst types, with accuracy of 79.6–83.6%. Previous studies on radiomics-based pancreatic cyst classification did not include a direct comparison with a radiologist, therefore, it was difficult to assess if the radiomics-based classification reported provided any added value relative to the standard of care. The current study showed that the radiomics- based pancreatic cyst classification achieved equivalent performance as an academic radiologist with more than 25 years of experience.”
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • "Secondly, the performance of the radiomics-based model was compared to the performance of a single-academic radiologist. The experienced academic radiologist in this study may be more accurate at pancreatic cyst classification than an average radiologist in the community, which may underestimate the incremental value of the radiomics-based model. Future reader studies should also recruit multiple readers with a wide range of experience to measure the real-world impact of these radiomics tools. Thirdly, the current radiomics model only used CT-based features plus patient age and demographics. Other important clinical features such as symptoms, family history, laboratory values, and cyst fluid molecular markers  were not included in the current model, which should be incorporated into future models. Our prior experience has demonstrated that the predictive power offered by multiple features is often additive.”
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdom Radiol (NY). 2022 Sep 13. doi: 10.1007/s00261-022-03663-6. Online ahead of print.
  • Research Agenda for Clinical AI in PDAC Imaging
    - To acquire more, good quality data coming from large, well-curated, multi-institutional private and public PDAC datasets
    - To switch focus towards state-of-the-art, entirely data-driven deep learning models
    - To use better quality ground truths that represent actual clinical endpoints such as overall survival and disease-free survival as the gold standard for model development
    - To investigate the use of multimodal AI, combining information from imaging, histopathology, genetics and clinical records
  • “Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.”
    Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.
    Schuurmans, M et al.
    Cancers 2022, 14, 3498. https://doi.org/10.3390/cancers14143498
  • “Early detection, arguably the most pressing issue in PDAC management, is closely linked to identifying small lesions and secondary anatomical signs. However, our results show this is still not considered in AI-based detection research, as there are no studies on pre-diagnostic detection of secondary signs, and most studies do not disaggregate performance based on tumour size/stage. Additionally, there is a lack of research on lesion localization and a general absence of well-curated datasets, with positive and negative cases being retrieved from completely different populations, which does not reflect the clinical landscape and can introduce bias. For AI to improve PDAC detection, it is crucial to acquire and make publicly available well-curated, multimodal datasets that contain a significant proportion of small (<2 cm or even <1 cm) tumours, which should be treated as a subgroup of interest when reporting model performance.”
    Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.
    Schuurmans, M et al.
    Cancers 2022, 14, 3498. https://doi.org/10.3390/cancers14143498
  • “Current research separates detection, which is defined as distinction between PDAC patients and healthy controls, from differential diagnosis, defined as distinction between PDAC and other types of pancreatic lesions. Only one study developed AI for simultaneous detection and characterisation of pancreatic lesions on CECT. The remaining publications focused on binary distinction between PDAC and one other malignancy, limiting the proposed models’ clinical use. Furthermore, it is important to consider that PDAC diagnosis currently relies on high-quality, adequate imaging with multi-phasic scanning protocols, which may not be widely available due to resource limitations. In the future, research should strive towards a single-use case for radiology-based AI in PDAC diagnosis that includes both the detection of a lesion and its correct classification among a variety of pancreatic diseases in accessible, standard-of-care imaging. The current priority is the curation of large datasets with representative percentages of each lesion type and the integration of different imaging modalities that offer complementary information regarding lesion characterisation.”  
    Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.
    Schuurmans, M et al.
    Cancers 2022, 14, 3498. https://doi.org/10.3390/cancers14143498
  • Objective: To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).
    Methods: A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist’s diagnostic choices that were made with and without the nomogram’s assistance were evaluated.
    Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
    Ming He et al.
    Acad Radiol 2022;&:1–9
  • Results: A seven-feature combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).
    Conclusion: The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.
    Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
    Ming He et al.
    Acad Radiol 2022;&:1–9
  • “In the present study, we obtained encouraging data when using radiomics to analyze enhanced CT scan images recorded 3 months after surgery. The resulting nomogram, which combines the radiomics signatures and postoperative elevation of CA 19-9, is expected to serve as a reference indicator for clinicians planning postoperative follow-up strategies. Patients for whom the nomogram shows a high probability of postoperative local recurrence may be better candidates for regular follow-ups, facilitating earlier confirmation of recurrence and prompt treatment. In patients for whom the nomogram indicates a relatively low probability of recurrence, a symptom-driven follow-up strategy can be used to alleviate the patients’ financial and psychological burdens.”
    Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
    Ming He et al.
    Acad Radiol 2022;&:1–9
  • “In the present study, the sensitivity and specificity of radiomics analysis for characterizing postoperative soft tissue were 70.8% and 63.3%, respectively, in the validation cohort; both of these values were significantly higher than those of the postoperative CA 19-9 (54.2% and 52.4%), respectively, (p < 0.05, both). Furthermore, the combination of radiomics signature and clinicoradiological features further improved the sensitivity and specificity to 76.3% and 66.7%, respectively, in the validation cohort. The combined model (postoperative elevation of CA 19-9 combined with the radiomics signatures) performed well both in the primary and validation cohort, showing its robustness and reliability for early diagnosis of postoperative local recurrence.”
    Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma
    Ming He et al.
    Acad Radiol 2022;&:1–9
  • “Due to the growth pattern of pancreatic cancer, the tumor may not be always visible as a hypodense lesion, therefore experts refer to the visibility of secondary external features that may indicate the presence of the tumor. We propose a method based on a U-Net-like Deep CNN that exploits the following external secondary features: the pancreatic duct, common bile duct and the pancreas, along with a processed CT scan. Using these features, the model segments the pancreatic tumor if it is present. This segmentation for classification and localization approach achieves a performance of 99% sensitivity (one case missed) and 99% specificity, which realizes a 5% increase in sensitivity over the previous state-of-the-art method. The model additionally provides location information with reasonable accuracy and a shorter inference time compared to previous PDAC detection methods. These results offer a significant performance improvement and highlight the importance of incorporating the knowledge of the clinical expert when developing novel CAD methods.”
    Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
    Christiaan G.A. Viviers et al.
    arXiv:2208.03581v1 [cs.CV] 6 Aug 2022
  • “In this research, we propose a PDAC segmentation model that utilizes the same visual cues in the surrounding anatomy that experts use when looking for the presence of PDAC. This focus and way of working is to maximally lever- age easily accessible external information and fully exploit clinical expertise, to ultimately optimize classification and localization performance. Since we start from the radiologists' reasoning, our method becomes clinically meaningful. For instance, a clinician pays close attention to pancreatic ductal size as a large (potentially dilated) duct could be indicative of tumor. Compared to normal pancreatic tissue in a CT scan, pancreatic cancer appears less visible as an ill-defined mass. It enhances poorly and is hypodense between 75% and 90% of arterial phase CT cases. For this reason, experts utilize secondary features which may be predictive of pancreatic cancer. These include, but are not limited to: ductal dilatation, hypo-attenuation, ductal interruption, distal pancreatic atro- phy, pancreatic contour anomalies and common bile duct dilation.”
    Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
    Christiaan G.A. Viviers et al.
    arXiv:2208.03581v1 [cs.CV] 6 Aug 2022
  • “Despite the eminent success of deep learning networks, even for detection of PDAC, the method presented in this work demonstrates that external tumor- indicative features can significantly boost CAD performance. We optimize a segmentation for classification and localization approach, by adding the easily obtainable and clinically valuable external secondary features used by the radiologist, to considerably improve segmentation performance. The proposed approach consists of a 3D U-Net that takes the CT scan, along with a segmentation map of the pancreas, pancreatic duct and common bile duct as input, in order to finally segment the pancreatic tumor. By integrating these indicative secondary features into the detection process, the proposed method achieves a sensitivity of 99 2% (one cased missed), yielding 5% gain over the previous state-of-the-art method. The proposed method also achieves a specificity of 99% and ultimately requires no sacrifice of specificity in favor of sensitivity.”
    Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
    Christiaan G.A. Viviers et al.
    arXiv:2208.03581v1 [cs.CV] 6 Aug 2022
  • “Generally, this research reveals the important value of explicitly including clinical knowledge into the detection model. We suggest that future CAD methods integrate higher orders of feature information, particularly valuable clinical features, into their domain-specific problem to improve performance when such information can be identified. This method paves the way for equipping clinicians with the necessary tools to enable early PDAC detection, with the aim to ultimately improve patient care.”
    Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features
    Christiaan G.A. Viviers et al.
    arXiv:2208.03581v1 [cs.CV] 6 Aug 2022

  • Pancreatic Cancer Detection using Machine and Deep Learning Techniques 
    Gupta A et al.
    2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) 
  • “Chen et al. tested a machine learning technique for detecting people having cancer in their pancreas at an early phase using medical data that had been collected from digital health records. As shown in eq. 1, they utilized eXtreme Gradient Boosting (XGBoost) to create a prediction model to detect early-stage patients based on 18,220 EHR variables, including diagnoses, procedures, clinical note information, and medicines.”
    Pancreatic Cancer Detection using Machine and Deep Learning Techniques  
    Gupta A et al.
    2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
  • "Artificial intelligence (AI) can speed up pancreatic cancer identification, boost tumor clearance, and detect recurrent tumors during postoperative surveillance as a feasible treatment. The capabilities of AI in identifying tumor resectability, differentiating between borderline and locally progressed pancreatic ductal adenocarcinoma (PDAC), and calculating pancreatic fatty infiltration should be further investigated and developed in the future study.”
    Pancreatic Cancer Detection using Machine and Deep Learning Techniques  
    Gupta A et al.
    2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
  • "According to the findings of this study, there is a lot of interest in using machine and deep learning algorithms to predict pancreatic cancer progression. When compared to other techniques, machine learning outperformed well for various datasets. As demonstrated in figure 3, The Bayesian model produced the most significant auc of 0.94, the genetic algorithm had the best sensitivity and specificity for detecting the pancreatic tumor, with 96.7% and 82.5%, respectively.”
    Pancreatic Cancer Detection using Machine and Deep Learning Techniques  
    Gupta A et al.
    2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)
  • OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans.
    METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans.
    RESULTS: The system achieved an average classification accuracy of 86% on the external dataset.
    CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.  
    Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images  
    Qureshia TA et al.
    Cancer Biomarkers 33 (2022) 211–217 
  • OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans.
    CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.  
    Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images  
    Qureshia TA et al.
    Cancer Biomarkers 33 (2022) 211–217 
  • “In this study, we identified unique features in pre- diagnostic CT scans that are not appreciated by human eyes but are potentially predictive of PDAC and developed a classifier that performed PDAC prediction by automatically identifying pre-diagnostic scans when mixed with healthy control scans. The proposed model is highly stable, and results are satisfactory, encoring researchers to replicate the model for further validation on large dataset.”
    Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images  
    Qureshia TA et al.
    Cancer Biomarkers 33 (2022) 211–217 
  • – Diagnostic: A CT scan of a patient with histopatho- logically confirmed PDAC and visible tumor on a CT scan. Patients with any type of pancreatectomy were excluded from this group.  
    – Pre-diagnostic: A CT scan from the same patient (as in the Diagnostic group), acquired between 6 months to 3 years prior to PDAC diagnosis, when no sign of PDAC or tumor was present.  
    – Healthy control: A contrast-enhanced abdominal CT scan of a different subject whose pancreas was healthy. The age and gender of each subject in the healthy control group and the year of their scan were matched to those of exactly one unique pa- tient in the pre-diagnostic group to limit morpho- logical and instrumentation variabilities, respec- tively. 
  • “Eleven studies (19%) evaluated differentiation of PCNs by classifying them into their respective subtypes based on their characteristics on imaging. Springer et al developed a multimodality ML model that integrated clinical, radiological and genetic/biochemical markers data to determine whether patients with pancreas cyst should undergo surgery, monitoring, or no further surveillance. The model correctly identified serous cystic neoplasms in 65% of the cases with 99% specificity, clearly outperforming the current standard of care of clinical identification in only 18% of cases. The authors conclude that these systems may serve an adjunct role in clinical practice, enabling the clinician to take better-informed clinical decisions[.”
    Artificial intelligence in gastroenterology: A state-of-the-art review  
    Kröner, Paul T et al.  
    World journal of gastroenterology vol. 27,40 (2021): 6794-6824.
  • The latest advances in AI in gastroenterology and hepatology are promising for aspect many fields of clinical care, from detection of neoplastic lesions on endoscopic assessment and improving current survival models to predicting treatment response. The application of AI to large and complex datasets may assist in the identification of new associations between variables, potentially leading to changes in clinical practice. Furthermore, the use of AI-assisted technologies has the potential to dramatically improve the quality of care. Finally, the time for assisted precision medicine is at hand, with the AI being able to tailor a treatment regimen or potentially predict the response to treatment in a specific patient based on extensive amounts of clinical data from large patient datasets. It is important to realize that, while AI currently does not substitute human clinical reasoning, it has a bright future in the betterment of patient care.
    Artificial intelligence in gastroenterology: A state-of-the-art review  
    Kröner, Paul T et al.  
    World journal of gastroenterology vol. 27,40 (2021): 6794-6824
  • “In our study, the 2017 Fukuoka criteria performed slightly worse for selecting HRI for surgery compared to patients with sporadic cysts, missing 60% of cysts with invasive carcinoma, or IPMN with HGD, with a low sensitivity of 40%. Furthermore, the 2017 Fukuoka criteria might have resulted in unnecessary surgery of low-grade IPMN in our high risk population, with a modest specificity of 85%, which translates to ~15% of patients undergoing unnecessary or premature pancreatic surgery with its attendant morbidity and mortality. Similarly, the 2019 CAPS criteria missed 40% of resected IPMNs harboring advanced neoplasia while also recommending surgery for 15% of HRI that did not need it.”
    Guidelines on management of pancreatic cysts detected in high-risk individuals: An evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements  
    Mohamad Dbouk , Olaya I. Brewer Gutierrez , Anne Marie Lennon , Miguel Chuidian , Eun Ji Shin , Ihab R. Kamel , Elliot K. Fishman , Jin He , Richard A. Burkhart , Christopher L. Wolfgang , Ralph H. Hruban , Michael G. Goggins, Marcia Irene Canto  
    Pancreatology 21 (2021) 613-621 
  • “One such approach is the detection of biomarkers in secretin- stimulated pancreatic juice at the time of EUS surveillance. The mutation profile and DNA concentration of pancreatic juice have been shown to be useful in the detection of high-grade PanIN le- sions and early PDAC in the CAPS cohort. Importantly, Yu et al. described the detection of low-abundance SMAD4/TP53 mu- tations from the cancer in the juice of patients under surveillance more than one year prior to the diagnosis of pancreatic mass on imaging. Overall, the analysis of pancreatic juice SMAD4/TP53 mutations could distinguish patients with PDAC or HGD from controls with a sensitivity and specificity of 72.2% and 89.4%, respectively.”
    Guidelines on management of pancreatic cysts detected in high-risk individuals: An evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements  
    Mohamad Dbouk , Olaya I. Brewer Gutierrez , Anne Marie Lennon , Miguel Chuidian , Eun Ji Shin , Ihab R. Kamel , Elliot K. Fishman , Jin He , Richard A. Burkhart , Christopher L. Wolfgang , Ralph H. Hruban , Michael G. Goggins, Marcia Irene Canto  
    Pancreatology 21 (2021) 613-621 
  • “Finally, artificial intelligence and deep learning technologies applied to multi-detector pancreatic protocol CT may improve the early detection of pancreatic cancer or its precursor lesions.”
    Guidelines on management of pancreatic cysts detected in high-risk individuals: An evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements  
    Mohamad Dbouk , Olaya I. Brewer Gutierrez , Anne Marie Lennon , Miguel Chuidian , Eun Ji Shin , Ihab R. Kamel , Elliot K. Fishman , Jin He , Richard A. Burkhart , Christopher L. Wolfgang , Ralph H. Hruban , Michael G. Goggins, Marcia Irene Canto  
    Pancreatology 21 (2021) 613-621 
  • “In conclusion, we report that the performance characteristics of the 2019 CAPS and 2017 Fukuoka ICG criteria for managing screen- detected pancreatic cysts are modestly specific but not sufficiently sensitive for selecting HRI for surgical treatment. New approaches, including multimodality algorithms that consider molecular cyst fluid analysis, clinical and genetic patient characteristics, and radiological pancreatic features, are needed to guide the surgical management of cystic lesions in individuals at high risk for pancreatic cancer.”
    Guidelines on management of pancreatic cysts detected in high-risk individuals: An evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements  
    Mohamad Dbouk , Olaya I. Brewer Gutierrez , Anne Marie Lennon , Miguel Chuidian , Eun Ji Shin , Ihab R. Kamel , Elliot K. Fishman , Jin He , Richard A. Burkhart , Christopher L. Wolfgang , Ralph H. Hruban , Michael G. Goggins, Marcia Irene Canto  
    Pancreatology 21 (2021) 613-621 

  • Guidelines on management of pancreatic cysts detected in high-risk individuals: An evaluation of the 2017 Fukuoka guidelines and the 2020 International Cancer of the Pancreas Screening (CAPS) consortium statements  
    Mohamad Dbouk , Olaya I. Brewer Gutierrez , Anne Marie Lennon , Miguel Chuidian , Eun Ji Shin , Ihab R. Kamel , Elliot K. Fishman , Jin He , Richard A. Burkhart , Christopher L. Wolfgang , Ralph H. Hruban , Michael G. Goggins, Marcia Irene Canto  
    Pancreatology 21 (2021) 613-621 
  • “Major advances in medical AI have had a tremendous impact at two main levels: (1) image recognition and (2) big data analysis. AI can detect very small changes that are difficult for humans to perceive. For example, AI can detect lung cancer up to a year before a physician [3], and AI can correctly diagnose skin cancer with superior diagnostic performance compared to that of a physician [4]. In addition, AI can reach the desired output within seconds and with more “consistent” performance. Doctors may have “inconsistent” performance due to insufficient training or exhaustion from busy clinical demands. A visual assessment by imaging physicians is qualitative, subjective, and prone to errors, and subject to intra-observer and inter-observer variability. AI may have better performance than physicians in some cases [5], and it has great promise to reduce clinician workload and the cost of medical care. However, it is necessary for clinicians to verify the output from AI for patient care.”
    A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 

  • A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719 
  • "Pancreatic cystic lesions, particularly IPMN, are the precursors of pancreatic cancer. Kuwahara et al. successfully established an AI-aided EUS using deep learning to distinguish malignant IPMNs from benign ones. The AI-aided EUS could diagnose malignant probability with a high sensitivity of 95.7% and a high accuracy of 94.0%, which was much greater than that of experts’ diagnoses (56.0%). AI-aided diagnosis is under development not only for IPMNs but also for other cystic lesions of the pancreas, such as serous cystic neoplasms, mucinous cystic neoplasms, solid pseudopapillary neoplasms, and cystic pancreatic neuroendocrine neoplasms.”
    A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
    Akihiko Oka  , Norihisa Ishimura and Shunji Ishihara  
    Diagnostics 2021, 11, 1719. https://doi.org/10.3390/diagnostics11091719  
  • “This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CEA), cytology, and artificial intelligence (AI) by deep learning using cyst fluid in differentiating malignant from benign cystic lesions. We retrospectively reviewed 85 patients who underwent pancreatic cyst fluid analysis of surgical specimens or endoscopic ultrasound-guided fine-needle aspiration specimens. AI using deep learning was used to construct a diagnostic algorithm. CEA, carbohydrate antigen 19-9, carbohydrate antigen 125, amylase in the cyst fluid, sex, cyst location, connection of the pancreatic duct and cyst, type of cyst, and cytology were keyed into the AI algorithm, and the malignant predictive value of the output was calculated. Area under receiver-operating characteristics curves for the diagnostic ability of malignant cystic lesions were 0.719 (CEA), 0.739 (cytology), and 0.966 (AI). In the diagnostic ability of malignant cystic lesions, sensitivity, specificity, and accuracy of AI were 95.7%, 91.9%, and 92.9%, respectively. AI sensitivity was higher than that of CEA (60.9%, p = 0.021) and cytology (47.8%, p = 0.001). AI accuracy was also higher than CEA (71.8%, p < 0.001) and cytology (85.9%, p = 0.210). AI may improve the diagnostic ability in differentiating malignant from benign pancreatic cystic lesions.”
    Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions  
    Yusuke Kurita et al.
    Scientific Reports | (2019) 9:6893 
  • "Although cytology had excellent specificity, it has a limited role because of its lack of sensitivity in previous studies30–32. In the present study, the sensitivity of cytology in differentiating malignant from benign cystic lesions was 47.8%. Thus, we constructed AI using deep learning algorithm for differentiating malignant from benign pancreatic cystic lesions based on the analysis of pancreatic cyst fluid and clinical data.”  
    Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions  
    Yusuke Kurita et al.
    Scientific Reports | (2019) 9:6893 
  • "In this study, AI using deep learning analyzed pancreatic cyst fluid and clinical data. By using this deep learning method, AI learns the characteristics of malignant cystic lesions by combining cyst fluid analysis and clinical data, and AI can possibly exclude the bias generated by human judgment. Although it is difficult for clinicians to diagnose malignant pancreatic cystic lesions by cyst fluid analysis and clinical data, AI using deep learning achieved adequate diagnostic ability in differentiating malignant from benign cystic lesions compared to cyst fluid analysis such as CEA and cytology. AI and CEA were also significant factor in the multivariate analysis of malignant cystic lesion. Specifically, although it is generally a problem that cytology diagnosis has low sensitivity, AI using deep learning achieved high sensitivity (95.7%).”
    Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions  
    Yusuke Kurita et al.
    Scientific Reports | (2019) 9:6893 
  • “The multivariable logistic regression model included sex, size, location, shape, cyst characteristic, and cystic wall thickening. The individualized prediction nomogram showed good discrimination in the training sample (AUC 0.89; 95% CI 0.83–0.95) and in the validation sample (AUC 0.81; 95% CI 0.70–0.94). If the threshold probability is between 0.03 and 0.9, and > 0.93 in the prediction model, using the nomogram to predict SCN and MCN is more beneficial than the treat-all- patients as SCN scheme or the treat-all-patients as MCN scheme. The prediction model showed better discrimination than the radiologists’ diagnosis (AUC = 0.68).”
    A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm  
    Chengwei Shao et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-021-03038-3 
  • All tumors were evaluated for the following characteristics: (1) CT-reported tumor size (i.e., the maximum cross-sectional diameter of the tumor [13]); (2) tumor location: pancreatic head, body, or tail; (3) shape: round or lobulated (lobulation was defined as the presence of rounded contours that could not be described as the borders of the same circle [9]); (4) cyst characteristic: oligocystic or polycystic; (5) cystic wall: thin or thick (thin was defined as < 2 mm while thick was defined as ≥ 2 mm [9]); (6) calcification; (7) enhanced mural nodule; (8) parenchymal atrophy; (9) common bile duct cutoff and dila- tion (> 10 mm); (10) main pancreatic duct (MPD) cutoff and dilation (> 3 mm); (11) pancreatitis identified by stranding of the peripancreatic fat tissue, ill-defined parenchymal contours, and fluid collections in the peripancreatic region; (12) contour abnormality; and (13) number of lesions: 1 or ≥ 2.  
    A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm  
    Chengwei Shao et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-021-03038-3 
  • “There were several limitations to this study. First, the number of patients was relatively small. Second, this was a single-center, retrospective analysis. In the future, we will expand the number of cases and perform a multi-center validation of the model. Third, the predicted model in this study only focused on SCN and MCN, and did not include other cystic lesions of the pancreas such as IPMN, pseudocyst, and retention cyst. Lastly, we only used CT characteristics to develop the model. We did not combine radiomics features, although artificial intelligence is becoming a hot topic. In the future, we will combine the CT characteristics and radiomics features to develop a more accurate model.”
    A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm  
    Chengwei Shao et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-021-03038-3 
  • “Lastly, we only used CT characteristics to develop the model. We did not combine radiomics features, although artificial intelligence is becoming a hot topic. In the future, we will combine the CT characteristics and radiomics features to develop a more accurate model.”
    A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm  
    Chengwei Shao et al.
    Abdominal Radiology https://doi.org/10.1007/s00261-021-03038-3  
  • “Pancreatic cancer (pancreatic ductal adenocarcinoma [PDAC]) is associated with a dire prognosis and a 5-year survival rate of only 10%. This statistic is somewhat misleading given that 52% of the patients will develop metastatic disease, with a resulting 2.9%, 5-year relative survival rate. However, for those patients with localized cancer where the tumor is confined to the primary site, the 5-year relative survival rate is 39.4%. It is estimated that in 2020, there will be 57,600 new cases of PDAC  and an estimated 47,050 will die of this disease.”  
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "Pancreatic ductal adenocarcinoma has the poorest overall survival of all the major cancer types, with a 5-year relative  survival rate that just reached 10%. This is due in part to the latestage at presentation, so that 49.6% of cases of newly diagnosed PDAC present with distant metastases, 29.1% present with re- gional lymph node involvement, and only 10.8% have tumors that are localized solely within the pancreas.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279

  • Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "In this context, the big data field provides a conceptual framework for analysis across the full spectrum of disease that may better capture patient subcategories, in particular when considering longitudinal disease development in a lifelong perspective. Here, variation in “healthy” diagnosis-free routes toward disease and later differences in disease comorbidities are currently of high interest. Using health care sector, socioeconomic, and consumer data, the precision medicine field works increasingly toward such a disease spectrum-wide approach. Ideally, this involves data describing healthy individuals, many of whom will later become sick—to have long-range correlations that relate to outcomes available for analysis. This notion extends the traditional disease trajectory concept into healthy life-course periods potentially enabling stratification of patient cohorts by systematically observed differences present before the onset and diagnosis of disease.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "Ultimately, it is likely that AI will transform much of the practice of medicine. AI will be used to interpret radiographs, ultrasounds, CT, and MRI, either as an adjunct to the clinician's interpretation or as the standalone reading.88 Health care organizations will use AI systems to extract and analyze electronic health record (EHR) data to better allocate staff and other resources, identify patients at risk for acute decompensation, and prevent medication errors.148 Using sensors on commodity devices such as smartphones, wearables, smart speakers, laptops, and tablets, individuals will be able to share health data during their daily lives and help generate a longitudinal personal health record, with pertinent information incorporated into their EHR. By extracting information from the EHR and incorporating data during an encounter with a patient, clinicians can be provided with a differential diagnosis in real-time with probabilities included.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "Because of the “black box” quality of many deep learning algorithms, clinicians and patients may be hesitant to depend on AI-based solutions. This fear is not unfounded. For example, it was discovered that an algorithm evaluating data from images of skin lesions was more likely to classify the lesion as malignant if a ruler was included in the photograph.149 The reticence by clinicians to embrace AI-based medical devices may also be explained by the paucity of peer-reviewed prospective studies assessing the efficacy of these systems.Finally, regulatory assessment of the effectiveness and safety of AI-based products is different from that of traditional medical devices.Regulatory agencies are working to find the best processes for determining whether an AI medical device should be cleared for clinical use.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "The ability to reliably detect very early-stage PDAC in asymptomatic patients should result in a major improvement in survival. This hypothesis is based on the observation that the prognosis for PDAC is clearly related to the pathological stage of the tumor at the time of diagnosis. Using the SEER database, Ansari et al reported that 5-year survival for patients with lymph node–negative primary PDAC less than 1-cm cancers is ~60%; with primary tumors of 2 cm or larger even without lymph node metastasis, survival was less than 20%. However, less than 1% of patients are found with primary PDAC less than 1 centimeter in size. Pancreatic ductal adenocarcinoma is diagnosed in the large majority of even stage IA patients because of symptoms, not as a result of an early detection program. The hypothesis that the earlier the stage of a PDAC, the better the outcome, is in concert with data from many other solid tumors, including breast, non–small cell lung, colorectal, prostate, and gastric cancers.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "Project Felix is a Lustgarten Foundation initiative led by Elliott Fishman at Johns Hopkins University to develop deep learning tools that can detect pancreatic tumors when they are smaller and with greater reliability than human readers alone. This effort has involved meticulous manual segmentation of thousands of abdominal CT scans to serve as a training and testing cohort, which represents the largest effort in this domain in the world. In collaboration with the computer scientist Alan Yuille. Project Felix has produced at least 17 articles on techniques to automatically detect and characterize lesions within the pancreas (https://www.ctisus.com/responsive/deep-learning/felix.asp).”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • "Eugene Koay from The University of Texas MD Anderson Cancer Center (MDACC) has previously characterized subtypes of PDAC on CT scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology, a higher rate of common pathway mutations, and poorer clinical outcomes compared with inconspicuous (low delta) tumors.His group has recently completed an analysis, currently under review, that shows that high-delta tumors demonstrate higher growth rates and shorter initiation times than their low-delta counterparts in the prediagnostic period. Although not strictly an AI initiative, his work serves as a rich foundation for future AI initiatives in this space. Drs Koay and Anirban Maitra at the MDACC are leading the NCI-sponsored EDRN initiative to assemble a prediagnosis pancreatic cancer cohort that could facilitate AI research into screening and early detection.”
    Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review  
    Barbara Kenner, PhD,* Suresh T. Chari, MD,† David Kelsen, MD, Fishman EK et al.
    Pancreas. 2021 Mar 1;50(3):251-279
  • “Pancreatic cancer remains a major health problem, and only less than 20% of patients have resectable disease at the time of initial diagnosis. Systemic chemotherapy is often used in the patients with borderline resectable, locally advanced unresectable disease and metastatic disease. CT is often used to assess for therapeutic response; however, conventional imaging including CT may not correctly reflect treatment response after chemotherapy.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456. 
  • "Dual-energy (DE) CT can acquire datasets at two different photon spectra in a single CT acquisition, and permits separating materials and extract iodine by applying a material decomposition algorithm. Quantitative iodine mapping may have an added value over conventional CT imaging for monitoring the treatment effects in patients with pancreatic cancer and potentially serve as a unique biomarker for treatment response. In this pictorial essay, we will review the technique for iodine quantification of pancreatic cancer by DECT and discuss our observations of iodine quantification at baseline and after systemic chemotherapy with conventional cytotoxic agents.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456. 
  • “The parameters obtained using tumor segmentation software included (1) RECIST diameter (mm), (2) tumor volume (mL), (3) mean CT number of tumor (HU) at simulated weighted-average 120-kVp images, (4) iodine uptake by tumor per volume of tissue (mg/mL), and (5) normalized tumor iodine uptake (tumor iodine uptake normalized to the reference value acquired using region of interest place in the abdominal aorta at the level of the pancreatic tumor, calculated by tumor iodine uptake [mg/dL]/abdominal aortic uptake [mg/dL]).”
  • “In conclusion, iodine uptake by pancreatic adenocarcinoma using DECT may add supplemental information for assessment of treatment response, although tumor iodine uptake by pancreatic adenocarcinoma is small, and it may be difficult to apply to each case. Normalized tumor iodine uptake might be more sensitive than iodine concentration to measure treatment response. More data are necessary to confirm these observations.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456.
  • Purpose: Evaluate utility of dual energy CT iodine material density images to identify preoperatively nodal positivity in pancreatic cancer patients who underwent neoadjuvant therapy.
    Conclusion: The dual energy based minimum normalized iodine value of all nodes in the surgical field on preoperative studies has modest utility in differentiating N0 from N1/2, and generally outperformed conventional features for identifying nodal metastases.
    CT features predictive of nodal positivity at surgery in pancreatic cancer patients following neoadjuvant therapy in the setting of dual energy CT.  
    Le O, Javadi S, Bhosale PR et al.  
    Abdom Radiol (NY). 2021 Jan 20. doi: 10.1007/s00261-020-02917-5. Epub ahead of print. PMID: 33471129.
  • “Radiomics analysis extracts a large number of features from conventional radiological cross-sectional images that were traditionally undetectable by the naked human eye. It identifies tumor heterogeneity in a comprehensive and noninvasive way, reflecting the biological behaviour of lesions, and thus assists in clinical diagnosis and treatment evaluation. This review describes the radiomics approach and its uses in the evaluation of pancreatic ductal adenocarcinoma (PDAC). This discipline holds the potential to characterize lesions more accurately, assesses the primary tumour and predicts the response to therapy and prognosis in PDAC. Existing studies have provided significant insights into the application of radiomics in managing the PDAC. However, a variety of challenges, including data quality and quantity, imaging segmentation, and the standardization of the radiomics process need to be solved before its widespread clinical implementation.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4

  • Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • "The application of radiomics in PDAC mainly includes the following 3 aspects: lesion characterization, primary tumour assessment and response to therapy and prognosis. It has also been used in other nononcologic conditions associated with PDAC. The initial results of radiomics related to PDAC are promising. However, there are still many problems and challenges that need to be solved, including data quality and quantity, imaging segmentation and the standardization of the radiomics process. Radiologists need to work closely with researchers such as information scientists to establish the standardized process of radiomics analysis. Multi-centre data sharing and public database establishment would provide more high-quality data for radiomics analysis. With the development of the radiomics in PDAC, it has a considerable potential to be a useful assistant in the clinical workflow for PDAC’s personalized medicine.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • “The procedure of the radiomics analysis should be carefully evaluated and standardized in every step to eliminate the potential bias and confounding factors. Extensive disclosure of the imaging protocols, evaluation criteria, reproducibility and/ or clinical utility is of great significance. Multiple studies had a limitation of unclear description about the detailed process of radiomics performed pre-processing, reconstruction, variations in feature nomenclature, mathematical definition, methodology, and software implementation of the applied feature extraction algorithms. The process of feature reduction and/or exclusion should be described clearly in the future. designs and a head-to-head comparison of quantitative features against standard diagnostic radiologist assessment are needed in the future.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • Background: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.
    Interpretation: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Findings Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992–1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998–1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891–0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings: CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas, and with improvements might accommodate variations in patient race and ethnicity and imaging parameters that are inevitable in real-world clinical practice. CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “In conclusion, this study provided a proof of concept that CNN can accurately distinguish pancreatic cancer on portal venous CT images. The CNN model holds promise as a compute r­aided diagnostic tool to assist radiologists and clinicians in diagnosing pancreatic cancer.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Deep learning and radiomics are two broad categories of artificial intelligence (AI) research that have the potential to facilitate automatic disease detection and to provide quantitative imaging biomarkers for individualized disease assessment. The large volumes of digital data inherent in radiology images make radiology a natural field for AI research. Cinematic Rendering is a recently described post-processing technique that uses sophisticated illumination modeling to achieve more photorealistic images, and these images, in turn, have the potential to aid treatment planning. Here we review these AI and advanced visualization techniques and highlight how they can be used to improve the detection and management of pancreatic cancers.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Deep learning is a type of machine learning method in which algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming. Deep neural networks are inspired by biological neural networks and use a matrix of interconnected nodes to mimic the function of a biologic neuron. The basic unit of an artificial neural network is a node. It takes a set of input features, multiplies these features by corresponding weights in the form of mathematical equations, and then passes the output to the next layer of nodes. The deep network architecture uses multiple layers of interconnected nodes to develop a mathematical model that best fits the data. The outputs are compared with the “ground truth,” and errors are used as feedback to adjust the weights in the network to minimize error in subsequent iterations.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)

  • Automatic detection of pancreatic ductal adenocarcinoma (PDAC) with deep learning. (Left panel) Axial IV contrast- enhanced CT image shows a hypoenhancing mass in the pancreatic body (arrow) with dilated pancreatic duct (arrowhead). (Middle panel) Manual segmentation of the tumor (red), pancreatic duct (green), and background pancreas (blue). (Right panel) Deep network prediction of tumor (red), pancreatic duct (green) and background pancreas (blue).

  • Automatic detection of pancreatic neuroendocrine tumor (PanNET) with deep learning. (Left panel) Axial IV contrast-enhanced CT image shows a subtle hyperenhancing mass within the head of the pancreas (arrow). (Middle panel) Manual segmentation of tumor (pink) and background pancreas (blue). (Right panel) Deep network prediction of tumor (pink) and background pancreas (blue).

  • Automatic detection of intraductal papillary mucinous neoplasm (IPMN) with deep learning. (Left panel) Axial IV contrast-enhanced CT image shows multiple well-circumscribed cystic lesions in the pancreas (arrow). (Middle panel) Manual segmentation of cystic tumors (yellow) and background pancreas (blue). (Right panel) Deep network prediction of cystic tumors (yellow) and background pancreas (blue).

  • A schematic illustrating the radiomics feature extraction and analysis process. Radiomics features can be classified into signal intensity, shape, texture, and filtered features (e.g., wavelets and Laplacian of Gaussian [LoG]). (Left panel) Input of imaging datasets (normal vs. abnormal) with annotation of regions of interest. (Middle panel) Extraction of radiomics features, including histogram of voxel signal intensities, shape features based on surface rendering of region of interest, and filtered features. (Right panel) The raw data are processed through feature selection to identify the most relevant features. These features can be correlated with clinical outcomes in classification tasks.
  • “Radiomics features have also been used to predict PanNET grade, one of the most important prognostic factors in predicting patient survival. Qualitative features such as ill-defined margins, heterogeneous enhancement, low- level enhancement, vascular involvement, and main pancreatic duct dilatation have been reported to be helpful features in predicting higher tumor grade. Radiomics features achieved equivalent or superior performance compared to traditional clinical and imaging features. in most, but not all studies, with higher tumor grades in the majority of these studies, and with worse progression free survival. The addition of radiomics features to traditional CT features may improve the accuracy of PanNET grade prediction.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Radiomics features have also been reported to be predictive of overall survival in patients with unresectable or locally advanced PDAC. Not surprisingly, the presence of metastatic disease at presentation was the most predictive of poor overall survival. factors. Radiomics features associated with tumor heterogeneity were also found to be poor prognostic factors. There is speculation that tumor hypoattenuation may reflect areas of hypoxic necrosis, which may suggest more aggressive underlying tumor biology as well as impaired response to chemotherapy and radiation therapy. Low attenuation may also be evidence of extensive venous invasion by the cancer.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • "While VR uses a simple ray cast method to generate 3D images, CR uses Monte Carlo path tracing that takes direct and indirect illumination into account. With CR, each pixel is formed by thousands of rays passing through the volumetric dataset and includes effects of light rays from scatter and from voxels adjacent to the paths of the rays. CR has the potential to more accurately depict complex anatomy. When applied to pancreatic imaging, CR can be used to accentuate focal textural change and enhance appreciation of internal architecture (e.g., septations, mural nodules) to improve their visualization and assist in tumor classification.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Pancreatic ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor segmentation tasks, yet critically important for clinical needs. Previous work on PDAC segmentation is limited to the moderate amounts of annotated patient images (n<300) from venous or venous+arterial phase CT scans. Based on a new self-learning framework, we propose to train the PDAC segmentation model using a much larger quantity of patients (n≈1,000), with a mix of annotated and un- annotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas.”
    Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)
  • “Fully automated and accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging tumor segmentation tasks, in the aspects of complex abdominal structures, large variations in morphology and appearance, low image contrast and fuzzy/uncertain boundary, etc. Previous studies introduce the cascade UNet for segmenting venous phase CT and hyperpairing network for segmenting venous+arterial phases CT and achieving mean Dice scores of 0.52 and 0.64, respectively. By incorporating nnUNet into a new self-learning framework with two teachers and one teaching assistant to segment three-phases of CT scans, our method reaches a Dice coefficient of 0.71, similar to the inter-observer variability between radiologists. This provides promise that a radiologist-level performance for accurate PDAC tumor segmentation in multi-phase CT imaging can be achieved through our computerized method.”
    Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)

  • Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)
  • Background: To identify preoperative computed tomography radiomics texture features which correlate with resection margin status and prognosis in resected pancreatic head adenocarcinoma.
    Methods: Improved prognostication methods utilizing novel non-invasive radiomic techniques may accurately predict resection margin status preoperatively. In an ongoing concerning pancreatic head adenocarcinoma, the venous enhanced CT images of 86 patients who underwent pancreaticoduodenectomy were selected, and the resection margin (>1 mm or ≤1 mm) was identified by pathological examination. Three regions of interests (ROIs) were then taken from superior to inferior facing the superior mesenteric vein and artery. Subsequent Laplacian-Dirichlet based texture analysis methods extracting algorithm of texture features within ROIs were analyzed and assessed in relation to patient prognosis.
    Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specicity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.
    Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy.
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)
  • Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specificity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.
    Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy.
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)

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