Related Experiment Video
Updated: Jul 15, 2026
Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
Published on: October 10, 2019
Bi-parametric MRI-based quantification radiomics model for the noninvasive prediction of histopathology and
Rationale And Objectives:
To develop and evaluate the performance of a noninvasive radiomics combined model based on preoperative bi-parametric MRI to assess biochemical recurrence (BCR) risk factors and to predict biochemical recurrence free survival in PCa patients.
Materials And Methods:
Pretreatment bp-MRI and clinicopathology data of 666 (discovery cohort, 545; test cohort, 121) PCa patients from four centers between January 2015 to March 2023 were retrospectively included. To predict BCR, extracapsular extension (ECE), pelvic lymph node metastasis (PLNM), and Gleason Grade group (GG), the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were developed, respectively. Subsequently, a logistic regression algorithm was used to combine one or more radiomics models and clinicopathology variables into radiomics-clinicopathology combined models (M1, M2) and radiomics-clinical combined model without pathology results (M3) for predicting BCR.
Results:
In the test cohort, the AUCs for the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were 0.841, 0.764, 0.896, and 0.698. Of the three combined models, M3 has the best prediction performance with an AUC of 0.884, M2 is the following with an AUC of 0.863, and M1 has the lowest performance with an AUC of 0.838 (95% CI 0.750-0.925) in the test cohort. Delong's test showed that the M3 was significantly higher (M1 vs. M3, p = 0.028; M2 vs. M3, p = 0.044).
Conclusion:
The combined model developed in this study, which is not dependent on pathologic biopsies, can noninvasively predict postoperative histopathology and BCR after PCa, therefore may provide decision support for follow-up and treatment strategies for patients in the postoperative period.
