EARLY DIAGNOSIS OF PROSTATE CANCER USING PARAMETRIC ESTIMATION OF IVIM FROM DW-MRI

Prostate cancer is a widespread type of cancer that leads to numerous fatalities and a high costs. This study discusses the development of a non-invasive CAD system that utilizes intravoxel incoherent motion (IVIM) parameters to diagnose prostate cancer. The study focuses on IVIM to separate the diffusion of water molecules in capillaries from the molecular diffusion outside of the vessels, and its diagnostic efficacy in the central and peripheral zones of prostate cancer. The study proposes a two-step segmentation approach for tumor detection, starting with the precise localization of the prostate gland and then using an Attention U-Net to extract the tumor-containing region of interest. The study evaluates the performance of the CAD system, the best classifier and IVIM parameters for differentiation, and the diagnostic value of IVIM parameters compared to ADC. The IVIM (CZ + PZ) parameters that utilized the extra trees classifier and were implemented without principal component analysis and standardization scaling achieved the best metrics. They produced an accuracy of 84.62%, a balanced accuracy of 82.58%, a precision of 80%, a specificity of 67.86%, a sensitivity of 97.30%, an F1-score of 87.12%, an IoU of 78.26%, a ROC of 83.88%, and a weighted sum metric of 82.79%.