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Open AccessArticle

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

1
Department of Radiation Oncology, CHRU Brest, 29200 Brest, France
2
LaTIM, INSERM, UMR 1101, CHRU Brest, 29200 Brest, France
3
Urology Department, CHRU Brest, 29200 Brest, France
4
Medical Physics Unit, McGill University, Montreal, QC H3A 0G4, Canada
5
Anatomopathology Department, CHRU Brest, 29200 Brest, France
6
Radiology Department, CHRU Brest, 29200 Brest, France
7
Urology Department, Cornouaille Hospital, 29000 Quimper, France
8
Radiology Department, Cornouaille Hospital, 29000 Quimper, France
9
Radiology Department, Keraudren Clinique, 29000 Brest, France
10
Radiology Department, Clinique St Michel, 29000 Quimper, France
11
Urology Department, Clinique St Michel, 29000 Quimper, France
12
Anatomopathology Department, Ouest Pathologie, 29000 Quimper, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2020, 12(4), 814; https://doi.org/10.3390/cancers12040814
Received: 17 March 2020 / Revised: 21 March 2020 / Accepted: 26 March 2020 / Published: 28 March 2020
(This article belongs to the Special Issue Radiomics and Cancers)
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. View Full-Text
Keywords: magnetic resonance imaging; prostatic neoplasms; radiomics; machine learning; treatment failure magnetic resonance imaging; prostatic neoplasms; radiomics; machine learning; treatment failure
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Bourbonne, V.; Fournier, G.; Vallières, M.; Lucia, F.; Doucet, L.; Tissot, V.; Cuvelier, G.; Hue, S.; Le Penn Du, H.; Perdriel, L.; Bertrand, N.; Staroz, F.; Visvikis, D.; Pradier, O.; Hatt, M.; Schick, U. External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer. Cancers 2020, 12, 814.

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