Related Experiment Video
Updated: Sep 24, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Biparametric MR signal characteristics can predict histopathological measures of prostate cancer
Minh Nguyen Nhat To1, Jin Tae Kwak2
1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
Objectives:
The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer.
Methods:
This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses.
Results:
MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001).
Conclusions:
MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression.
Key Points:
• Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI. • Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images. • The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.
