Saliency of breast lesions in breast cancer detection using artificial intelligence.

The analysis of mammograms using artificial intelligence (AI) has shown great potential for assisting breast cancer screening. We use saliency maps to study the role of breast lesions in the decision-making process of AI systems for breast cancer detection in screening mammograms. We retrospectively collected mammograms from 191 women with screen-detected breast cancer and 191 healthy controls matched by age and mammographic system. Two radiologists manually segmented the breast lesions in the mammograms from CC and MLO views. We estimated the detection performance of four deep learning-based AI systems using the area under the ROC curve (AUC) with a 95% confidence interval (CI). We used automatic thresholding on saliency maps from the AI systems to identify the areas of interest on the mammograms. Finally, we measured the overlap between these areas of interest and the segmented breast lesions using Dice's similarity coefficient (DSC). The detection performance of the AI systems ranged from low to moderate (AUCs from 0.525 to 0.694). The overlap between the areas of interest and the breast lesions was low for all the studied methods (median DSC from 4.2% to 38.0%). The AI system with the highest cancer detection performance (AUC = 0.694, CI 0.662-0.726) showed the lowest overlap (DSC = 4.2%) with breast lesions. The areas of interest found by saliency analysis of the AI systems showed poor overlap with breast lesions. These results suggest that AI systems with the highest performance do not solely rely on localized breast lesions for their decision-making in cancer detection; rather, they incorporate information from large image regions. This work contributes to the understanding of the role of breast lesions in cancer detection using AI.

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review.

PURPOSE OF THE REVIEW: We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging. RECENT FINDINGS: CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards. The segmentation of skeletal muscle tissue and VAT and SAT compartments is most often performed at the level of the 3rd lumbar vertebra. A decreased amount of CT-determined skeletal muscle mass is a marker of impaired survival in many patient populations, including patients with most types of cancer, some surgical patients, and those admitted to the intensive care unit (ICU). Patients with increased VAT are more susceptible to impaired survival / worse outcomes; however, those patients who are critically ill or admitted to the ICU or who will undergo surgery appear to be exceptions. The independent significance of SAT is less well established. Recently, the roles of the CT-determined decrease of muscle mass and increased VAT area and epicardial adipose tissue (EAT) volume have been shown to predict a more debilitating course of illness in patients suffering from severe acute respiratory syndrome coronavirus 2 (COVID-19) infection. SUMMARY: The field of CT-based body composition analysis is rapidly evolving and shows great potential for clinical implementation.