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

• Main Authors: Vincent Bourbonne, Georges Fournier, Martin Vallières, François Lucia, Laurent Doucet, Valentin Tissot, Gilles Cuvelier, Stephane Hue, Henri Le Penn Du, Luc Perdriel, Nicolas Bertrand, Frederic Staroz, Dimitris Visvikis, Olivier Pradier, Mathieu Hatt, Ulrike Schick
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Cancers
• Subjects: magnetic resonance imaging; prostatic neoplasms; radiomics; machine learning; treatment failure
• Online Access: https://www.mdpi.com/2072-6694/12/4/814

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 SZE_GLSZM) predicted BCR with an accuracy of 78% and allowed for significant stratification of patients for bRFS (<i>p</i> < 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.