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Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models
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Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models
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Journal Article
Predicting Pathogenic Variants of Breast Cancer Using Ultrasound-Derived Machine Learning Models
2025
Overview
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 multigene panel tests, including all seven above-mentioned genes. Ultrasound images were acquired prior to any treatment, and the tumoral and peritumoral areas were used to extract radiomics data. The study population was divided into P/LP and non-P/LP variant groups. Radiomics features were analyzed using machine learning models, alone or in combination with clinical features, with the aim of predicting the genetic status of BC patients. Results: We observed significant differences in radiomics features between P/LP- and non-P/LP-variant-driven tumors. The developed radiomics model achieved a maximum mean accuracy of 85.7% in identifying P/LP variant carriers. Including features from the peritumoral area yielded the same maximum accuracy. Conclusions: Radiomics models based on ultrasound images of breast tumors may provide a promising alternative for predicting P/LP variant status in BC patients. This approach could reduce dependence on costly genetic testing and expedite the diagnostic process. However, further validation in larger and more diverse populations is needed.
