Performance of Conventional and Machine-Learning Approaches for the Diagnosis of Tumor Recurrence on MRI after Radiation Therapy of Brain Metastases
Authors:   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, S Ammari , B Rabiee ,   Montefiore Medical Center, Albert Einstein College of Medicine, USA, S Juvina ,   Digital Vision Center, OPIS, CentraleSupélec, Inria, Paris-Saclay University, France, L Antonios ,   Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, France, R Sallé de Chou ,   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, C Balleyguier ,   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, F Bidault ,   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, M El Haik ,   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, GCTE Garcia ,   Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, France, M Laurence ,   Department of Imaging, Gustave Roussy Cancer Campus, Université Paris Saclay, France, E Limkin ,   Department of Radiation Oncology, Gustave Roussy Cancer Campus, France, S Bockel ,   Department of Radiation Oncology, Gustave Roussy Cancer Campus, France, M Khettab ,   Department of Medical Oncology, Gustave Roussy Cancer Campus, France, C Robert ,   Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France, A Carre ,   Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France, S Reuze ,   Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France, N Lassau ,   Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, France, E Chouzenoux ,   Digital Vision Center, OPIS, CentraleSupélec, Inria, Paris-Saclay University, France, L Dercle ,   Department of Radiology, New York Presbyterian, Columbia University Irving Medical Center, USA
Published: October 2021

Objective: To compare the performance of conventional and machinelearning approaches for the diagnosis of tumor recurrence after radiation therapy of brain metastases. Methods: 184 symptomatic patients with solitary metastatic brain lesions treated with radiation therapy were enrolled in a monocentric retrospective study from June 2013 to May 2018. The diagnosis was tumor recurrence (n=71) and radiation necrosis (n=113) using as reference standard expert-consensus derived from pathology and long-term follow-up. 37 potential predictors were recorded at the time of radiological progression (7-15 months after therapy): 6 clinical features and 31 imaging features including 20 radiomics features derived from standard of care 3D T1-gadolinium sequences. We compared four approaches (A, B, C, D): expert report using MRI sequences without (A) and with delayedcontrast MRI (TRAM) sequences (B), 11 non-Radiomics imaging features alone (C) and a signature combining variables selected using unsupervised machinelearning algorithms (D), training:validation sets: n=144:40 pts). Results: Overall (n=184), approaches B and C (using TRAM sequence alone) reached comparable performances with respective AUCs [95% CI] of 78.7% [72.3%-85.1%] and 76.8% [70.3%-83.3%]. Both significantly outperformed approach A with AUC [95% CI] of 57.4% [50.7%-64.1%] (DeLong’s test, p-value=10-7). In the validation set (n=40), the signature reached an AUC [95% CI] of 92% [87%-97%]. Conclusion: A quantitative analysis of TRAM sequence seems the best approach for the diagnosis of recurrent tumor after radiation therapy. It is parsimonious, objective and less time-consuming than interpreting all sequences. A signature derived from the analysis of standard of care 3D T1- gadolinium sequence showed promising results that warrant prospective validation.