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Abstract #0291

Radiomic features on Multi-parametric MRI can help risk categorization of Prostate Cancer patients on Active Surveillance

Ahmad Algohary1, Satish Viswanath1, Sadhna Verma2, and Anant Madabhushi1

1Case Western Reserve University, Cleveland, OH, United States, 2University of Cincinnati, Cincinnati, OH, United States

Active Surveillance (AS) offers an important alternative to radical treatment as more men die with prostate cancer (PCA) than of the disease. In this study, we explore the role of radiomic texture features on a pre-biopsy screening 3 Tesla multi-parametric MRI that can predict which men with elevated PSA will have a cancer-positive or cancer-negative biopsy. The selected texture features correctly identified 14/15 biopsy-negative (compared to 10/15 cases correctly identified by PIRADS) and 23/30 biopsy-positive cases (compared to only 15/30 correctly identified by PIRADS). These features appear to enhance differentiation between biopsy-positive and biopsy-negative prostate cancer patients on Active Surveillance.

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