Early diagnosis of prostate cancer using parametric estimation of ivim from dw-mri

Prostate cancer (PCa) is a widespread type of cancer that leads to numerous fatalities and a high financial cost. The chance of survival for PCa patients increases when the disease is detected at an early stage. This study discusses the development of a non-invasive computer-aided diagnosis (CAD) system that utilizes intravoxel incoherent motion (IVIM) parameters to detect and diagnose prostate cancer. The study focuses on IVIM, which can 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 using a robust level-sets technique and then using an Attention U-Net to extract the tumor-containing region of interest (ROI) from the segmented image. 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 results of this study contribute to the development of non-invasive methods for early prostate cancer detection and diagnosis. The IVIM (CZ + PZ) parameters that utilized the extra trees classifier (ETC) and were implemented without principal component analysis (PCA) 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 (WSM) of 82.79%.