External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Abstract

Adjuvant radiotherapy after prostatectomy was recently challenged by early salvage radiotherapy, which highlighted the need for biomarkers to improve risk stratification. Therefore, we developed an MRI ADC map-derived radiomics model to predict biochemical recurrence (BCR) and BCR-free survival (bRFS) after surgery. Our goal in this work was to externally validate this radiomics-based prediction model. Experimental Design: A total of 195 patients with a high recurrence risk of prostate cancer (pT3-4 and/or R1 and/or Gleason’s score > 7) were retrospectively included in two institutions. Patients with postoperative PSA (Prostate Specific Antigen) > 0.04 ng/mL or lymph node involvement were excluded. Radiomics features were extracted from T2 and ADC delineated tumors. A total of 107 patients from Institution 1 were used to retrain the previously published model. The retrained model was then applied to 88 patients from Institution 2 for external validation. BCR predictions were evaluated using AUC (Area Under the Curve), accuracy, and bRFS using Kaplan–Meier curves. Results: With a median follow-up of 46.3 months, 52/195 patients experienced BCR. In the retraining cohort, the clinical prediction model (combining the number of risk factors and postoperative PSA) demonstrated moderate predictive power (accuracy of 63%). The radiomics model (ADC-based SZEGLSZM) predicted BCR with an accuracy of 78% and allowed for significant stratification of patients for bRFS (p < 0.0001). In Institution 2, this radiomics model remained predictive of BCR (accuracy of 0.76%) contrary to the clinical model (accuracy of 0.56%). Conclusions: The recently developed MRI ADC map-based radiomics model was validated in terms of its predictive accuracy of BCR and bRFS after prostatectomy in an external cohort.

Integration of Clinical Identifications With Deep Transferrable Imaging Feature Representations Can Help Predict Prostate Cancer Aggressiveness and Outcome

“…It had correctly demonstrated that patient with high-risk PCa had signi cantly worse BCR-free survival compared with low-to intermediate-risk groups [28,29]. Results from some studies indicated that imaging ndings such as PI-RADS and radiomics features had prognostic value on BCR after prostatectomy [30,20,31]. Our preliminary results also showcase the PI-Risk strati cation even revealed a potential role in predicting the prognostic of disease progression risk preoperatively.…”

Integration of Clinical Identifications With Deep Transferrable Imaging Feature Representations Can Help Predict Prostate Cancer Aggressiveness and Outcome

“…It had correctly demonstrated that patient with high-risk PCa had signi cantly worse BCR-free survival compared with low-to intermediate-risk groups [28,29]. Results from some studies indicated that imaging ndings such as PI-RADS and radiomics features had prognostic value on BCR after prostatectomy [20,30,31]. Our preliminary results also showcase the PI-Risk strati cation even revealed a potential role in predicting the prognostic of disease progression risk preoperatively.…”

Deep Learning with Quantitative Features of Magnetic Resonance Images to Predict Biochemical Recurrence of Radical Prostatectomy: A Multi-Center Study

“…Thus, radiomics has successfully introduced to discriminate benign and malignant lesions [ 8 ], predict aggressiveness [ 9 ] and estimate Gleason scores [ 10 ]. However, there were still few studies focusing on BCR prediction for patients after radical prostatectomy with relatively large amount datasets [ 11 , 12 , 13 ].…”