Abstract #4860

Quantitative Classification of Clinically Significant Prostate Cancer Using a Biexponential Model of Extended b-value Diffusion-Weighted MRI

Christine H Feng¹, Christopher C Conlin², Ana E Rodríguez-Soto², Roshan Karunamuni¹, Aaron B Simon¹, Rebecca Rakow-Penner², Michael E Hahn², Anders M Dale^2,3, and Tyler Seibert^1,4

¹Radiation Medicine & Applied Sciences, UC San Diego, La Jolla, CA, United States, ²Radiology, UC San Diego, La Jolla, CA, United States, ³Neurosciences, UC San Diego, La Jolla, CA, United States, ⁴Bioengineering, UC San Diego, La Jolla, CA, United States

Prostate cancer is the second most frequent malignancy in men worldwide—novel strategies are needed to better identify patients with clinically significant disease. We developed a biexponential linear regression model using data from 36 men who underwent prostate MRI with 5 distinct b-values. We evaluated quantitative detection of clinically significant prostate cancer. The biexponential model and derivative lookup table outperformed simple ADC and kurtosis in quantitative classification of benign tissue and cancer (AUC 0.958 and 0.976 vs 0.632 and 0.409, respectively).

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