Effects of feature type and multiple scanners on brain metastasis stereotactic radiosurgery outcome prediction

The prediction of brain metastasis (BM) response to stereotactic radiosurgery could assist clinicians when choosing BM treatments. This study investigates the prediction of in-field progression, out-of-field progression, and 1-year overall survival (OS) endpoints using a machine learning classifier. It also investigates the effect of feature type and magnetic resonance imaging (MRI) scanner variability on classifier performance. The study data set consisted of n = 110 BMs across 91 patients for which endpoints, seven clinical features, and MRI scans were available. 635 radiomic features were extracted from the MRI for the BM region-of-interest (ROI) and a 5mm BM ROI dilation. A 1000-iteration bootstrap experimental design was used with a random forest classifier to provide area under the receiver operating characteristic curve (AUC) estimates. This experimental design was used for multiple endpoints, groups of features, and data partitioning by scanner model. In-field progression, out-of-field progression, and 1-year OS were predicted with respective AUC estimates of 0.70, 0.57 and 0.66. For all endpoints, clinical and/or radiomic features from the BM ROI provided optimal performance. MR scanner variability was found to decrease classifier AUC in general, though pre-processing methods were found to counteract this effect for some scanner models. This study shows that in-field progression, out-of-field progression, and 1-year OS can all be predicted to some degree, with in-field progression being predicted most accurately. The effects of scanner variability indicate that more diverse data sets and robust methods to account for scanner variability are required before clinical translation.