Imaging Pearls ❯ Kidney ❯ Artificial Intelligence
|
-- OR -- |
|
- “Renal cancer is responsible for over 100,000 yearly deaths and is principally discovered in computed tomography (CT) scans of the abdomen. CT screening would likely increase the rate of early renal cancer detection, and improve general survival rates, but it is expected to have a prohibitively high financial cost. Given recent advances in artificial intelligence (AI), it may be possible to reduce the cost of CT analysis and enable CT screening by automating the radiological tasks that constitute the early renal cancer detection pipeline. This review seeks to facilitate further interdisciplinary research in early renal cancer detection by summarizing our current knowledge across AI, radiology, and oncology and suggesting useful directions for future novel work.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “Deep learning-based classifiers can achieve high accuracy in CT images with very little manual intervention. Tanaka et al. (2020) sought to quantify small (≤4 cm) renal mass detection accuracy in CT using axial CT slices and a fine-tuned InceptionV3 CNN; they differentiated malignant and benign masses with a maximum AUC of 0.846 in CECT and 0.562 in NCCT. Pedersen et al. (2020) trained a similar 2D slice-classifying CNN, but used it to classify each slice within each known mass’ 3D volumes to enable a slice-based voting system to differentiate patient-level RC from oncocytoma, returning a perfect validation accuracy of 100%. Han et al. (2019) sought to differentiate between clear cell RCC (ccRCC) and non-ccRCC from known RCC masses, using radiologist-selected axial CT slices from NCCT and two CECT phases, and achieved sub-type classification AUCs between 0.88 and 0.94 in an internal testing dataset.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “Given the potential for RC early detection in LDCT, there is a need for more research quantifying RC segmentation performance in LDCT. Investigations into general NCCT segmentation have shown that using synthetic contrast enhancement as an auxiliary training task in MTL can improve segmentation accuracy. Therefore, an investigation in renal LDCT segmentation may be improved by introducing synthetic enhancement to CECT as an auxiliary learning task in MTL. Such an investigation would likely be complicated by Standley et al. (2020) findings – that MTL task relationships can be unique to each configuration of network architecture, hyperparameters, and dataset domain.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “This manuscript highlights and summarizes existing AI method in RC diagnosis and suggests how these can be repurposed to enable RC early detection. After summarizing existing segmentation, classification, and other AI methods in RC diagnosis, a review of analogous cancer detection and diagnosis methods across broader cancer literature and computer vision was conducted. Contrasting the RC-specific workflows to their equivalents across computer vision and other cancer domains allowed the generation of novel RC-specific research proposals that may enable AI-based RC early detection.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9
- “Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions.”
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review
Matteo Ferro et al.
Ther Adv Urol 2023, Vol. 15: 1–26 - “AI evidence so far indicates a strong association with improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions, and its algorithms that can adjust scanner settings to improve image acquisition (especially the gray zone levels) and standardization of scanner protocols between institutions will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers. Radiomics holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions, but integration in clinical practice will have to be preceded by standardized radiomics models and methodology, and future prospective external validation of obtained data and their comparison with existing traditional, well-validated tools, will have to be performed prior to further integration in current practice.”
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review
Matteo Ferro et al.
Ther Adv Urol 2023, Vol. 15: 1–26
- “After training and fine-tuning, the test set, which should be ideally made of external and unseen data, is used to assess the generalizability of the AI model. So an important point before an AI model can be deployed in the real world, it that its performances be validated using a large validation test with a variety of diagnoses from different databases that also include rare conditions and probably anatomical variations. Large data for AI models is the key because they help increase the confidence in predictions and allows robust internal and external validations and testing. However, large datasets raise several issues such as reliability of original data but also inclusion of rare conditions. One option to increase the prevalence of rare conditions or obtain a distribution that mirrors those of the real world to better train the model is to enrich the dataset using synthetic images obtained with data augmentation techniques, but this requires further investigation.”
Applications of Artificial Intelligence in Urological Oncology Imaging: More Data Are Needed
Philippe Soyer, Anthony Dohan, and Maxime Barat
Canadian Association of Radiologists’ Journal 2023, Vol. 0(0) 1–2
- OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122