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

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  • “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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