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September 17, 2025

Development an Artificial Intelligence Model to Identify BRCA Mutations in Prostate Cancer Through prostate MRI images

Key Points

The KNN model achieved AUROCs of 1.00 in training and 0.80 in testing, indicating strong predictive performance.
BRCA mutations were identified in 204 patients using next-generation sequencing to inform treatment strategies.
T2-weighted MRIs provided 1,422 radiomic features, revealing significant potential for non-invasive mutation identification.
MRI-based models may reduce reliance on NGS testing, potentially leading to cost savings and improved patient outcomes.

Abstract

Motivation: Prostate cancer is the leading male malignancy in South Korea, with BRCA1/2 mutations critical for PARP inhibitor use in personalized therapy. Goal(s): Build a model to predict BRCA mutations using MRI, aiming to reduce NGS costs and improve personalized treatment. Approach: In a retrospective study of 204 patients, BRCA mutations were identified with NGS. T2-weighted MRIs were segmented, and 1,422 radiomic features extracted. Feature selection included ICC, LASSO, and classifiers (KNN, RF, GNB) were applied. Results: The KNN model achieved AUROCs of 1.00 (train) and 0.80 (test) with TNR of 0.84. RF and GNB had TNRs of 0.86 and 1.00 for negatives. Impact: MRI-based radiomics models offer a non-invasive, cost-effective alternative to NGS, potentially reducing the need for NGS testing. These models could aid in drug selection and prognosis prediction, enhancing personalized treatment strategies.

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