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Abstract #5094

Predicting Genetic Subtypes of Prostate Cancer Using Radiomics Features from T2-weighted Prostate MRI Images

Jongjin Yoon1, Hyunho Han2, 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, Machine Learning/Artificial Intelligence

Motivation: Prostate cancer has diverse genetic subtypes affecting prognosis and treatment response.

Goal(s): Develop a machine learning model to predict four genetic subtypes (Luminal A, Luminal S, AVPC-I, ACPV-M) using radiomic features from T2-weighted MRI, supporting personalized treatment.

Approach: In 195 patients, RNA sequencing identified subtypes. T2-weighted MRIs were segmented, and 1,422 radiomic features were extracted. Feature selection used ICC, and classification models were trained with SMOTE for data balance.

Results: The RF model achieved AUROC scores of 0.98 (train) and 0.84 (test) for AR inhibitor-resistant subtype, and 0.95 (train) and 0.77 (test) for differentiating docetaxel-resistant subtypes.

Impact: Our pilot study demonstrated promising results for a radiomics model capable of predicting genetic subtypes of prostate cancer. This model demonstrated its ability to predict AR inhibitor-resistant and docetaxel-resistant genetic subtypes with AUROCs of 0.84 and 0.77, respectively.

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