Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.

Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9 % using 19 features and 92.1 % using 7 of them; while from ABVS we attained an AUC-ROC of 72.3 % using 22 features and 85.8 % using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8 % - 74.1 % ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.

Male Breast: A Review of the Literature and Current State of the Art of Diagnostic Imaging Work-Up.

Pathological conditions affecting the male breast (MB) share some similarities with those found in women, while others are specific to men. The first part of this review provides an overview of MB disorders, exploring the most common types of MB diseases. The second part then emphasizes the state-of-the-art approaches proposed in the literature for screening and follow-up with MB cancer patients, which highlights the importance of tailored strategies for diagnosis, follow-up, and identifying high-risk populations. Considering the increasing attention in recent years on the topic, transgender individuals are also included in this review. Together with the MB, it is an understudied category thus far. This review aims to raise awareness among radiologists that MBs should be approached differently from female breasts, contributing to the advancement of medical knowledge, improving patient outcomes, and promoting early detection of MB disorders. The review also provides an update on breast cancer and screening in the transgender population.