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Abstract #5093

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

Jongjin Yoon1, Jong Soo Lee2, and Young Tail Oh1
1Radiology, Severance hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of, 2Urology, Severance hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

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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