Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models.

ABSTRACT: Background: Breast cancer (BC) is the most frequently diagnosed cancer in women and the leading cause of cancer-related deaths in women globally. Carriers of P/LP variants in the BRCA1, BRCA2, TP53, PTEN, CDH1, PALB2, and STK11 genes have an increased risk of developing BC, which is why more and more guidelines recommend prophylactic mastectomy in this group of patients. Because traditional genetic testing is expensive and can cause delays in patient management, radiomics based on diagnostic imaging could be an alternative. This study aims to evaluate whether ultrasound-based radiomics features can predict P/LP variant status in BC patients. Methods: This retrospective study included 88 breast tumors in patients tested with mul

SUBMITTER: Antone NZ 

PROVIDER: S-EPMC11940624 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature