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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.00807

MRI-derived radiomics to guide post-operative management for high-risk prostate cancer

 Vincent Bourbonne1, 2, 3*,  Martin Vallières1, 4, Francois Lucia1, 2, 3, Laurent Doucet2, Dimitris Visvikis1, Valentin Tissot5, Olivier Pradier1, 2, 3,  Mathieu Hatt1 and Ulrike Schick1, 2, 3
  • 1INSERM U1101 Laboratoire de Traitement de l'information Médicale, France
  • 2Centre Hospitalier Regional et Universitaire de Lille, France
  • 3Université de Bretagne Occidentale, France
  • 4Medical Physics Unit, McGill University, Canada
  • 5Département de Radiologie, Centre Hospitalier Regional et Universitaire de Lille, France

Purpose: Prostatectomy is one of the main therapeutic options for prostate cancer (PCa). Studies proved the benefit of adjuvant radiotherapy (aRT) on clinical outcomes, with more toxicities when compared to salvage radiotherapy. A better assessment of the likelihood of biochemical recurrence (BCR) would rationalize performing aRT. Our goal was to assess the prognostic value of MRI-derived radiomics on BCR for PCa with high recurrence risk.

Methods: We retrospectively selected patients with a high recurrence risk (T3a/b or T4 and/or R1 and/or Gleason score>7) and excluded patients with a post-operative PSA>0.04ng/mL or a lymph-node involvement. We extracted IBSI-compliant radiomic features (shape and first order intensity metrics, as well as second and third order textural features) from tumors delineated in T2 and ADC sequences. After random division (training and testing sets) and machine learning based feature reduction, an univariate and multivariate Cox regression analysis was performed to identify independent factors. Correlation with BCR was assessed using AUC and prediction of biochemical relapse free survival (bRFS) with a Kaplan-Meier analysis.

Results: 107 patients were included. With a median follow-up of 52.0 months, 17 experienced BCR. In the training set, no clinical feature was correlated with BCR. One feature from ADC (SZEGLSZM) outperformed with an AUC of 0.79 and a HR 17.9 (p=0.0001). Lower values of SZEGLSZM are associated with more heterogeneous tumors. In the testing set, this feature remained predictive of BCR and bRFS (AUC 0.76, p=0.0236).

Conclusion: One radiomic feature was predictive of BCR and bRFS after prostatectomy helping to guide post-operative management.

Keywords: Magnetic Resonance Imaging - High Field, Prostatic neoplasms - diagnosis - therapy, Radiomics analysis, machine learning (artificial intelligence), Treatment failure (TF)

Received: 08 May 2019; Accepted: 07 Aug 2019.

Copyright: © 2019 Bourbonne, Vallières, Lucia, Doucet, Visvikis, Tissot, Pradier, Hatt and Schick. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Vincent Bourbonne, INSERM U1101 Laboratoire de Traitement de l'information Médicale, Brest, France,