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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.

Performance of Conventional and Machine-Learning Approaches for the Diagnosis of Tumor Recurrence on MRI after Radiation Therapy of Brain Metastases