28 An MRI radiomic signature for predicting brachytherapy outcomes in locally advanced cervical cancer. (December 2018)

• Record Type: Journal Article
• Authors: Alexis, Anthony; Reuzé, Sylvain; Bockel, Sophie; Berthelot, Kevin; Dumas, Isabelle; Orlhac, Fanny; Sun, Roger; Escande, Alexandre; Ammari, Samy; Haie-Meder, Christine; Chargari, Cyrus; Deutsch, Eric; Robert, Charlotte
• Is Part Of: Physica medica. Volume 56(2018)Supplement 1
• Page Start: 17
• Publication Date: 2018-12
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.09.041
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Introduction: The standard treatment of locally advanced cervical cancer (LACC) consists of concomitant chemoradiation followed by brachytherapy. The recent implementation of image-guided adaptive brachytherapy (IGABT) has shown a significant improvement in local control while limiting toxicity. We identified a radiomic signature of LACC relapse based on per-brachytherapy MRI. Methods: 100 patients with LACC treated with pulsed-dose-rate IGABT after initial chemoradiation were retrospectively included (TS: N = 60, training; VS: N = 40, validation). Using LIFEx, we extracted 30 textural features from the clinical target volume delineated on T2 FSE MRI. A methodological study was carried out to identify the most robust and informative features. The ability of remaining features to predict relapse was assessed through univariate and multivariate statistical analysis. Results: 9 clusters including 24 robust features were considered for the clinical analysis. In univariate analysis, five features were statistically significant predictors of overall recurrence (TS: p < 0.05). A 5-feature signature predicting relapse was identified and validated (TS: AUC = 0.88, p < 0.00001, VS: AUC = 0.78, p < 0.01) and performed better than the five features determined from univariate analysis and the tumor volume (TS, Delong's test, p < 0.05). Conclusion: Our radiomics signature has been validated in an institutional cohort. MRI-based texture analysis could provide clinically relevant information to adapt brachytherapy planning. However, standardization of MRI protocols or development of normalization methods is still needed to perform multi-center MRI-based radiomics studies.