Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis SRS Outcome Prediction.

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Abstract

To develop a novel deep ensemble learning model for accurate prediction of brain metastasis(BM) local control outcomes following stereotactic radiosurgery(SRS).A total of 114 BMs from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution(Dmap) of each BM was registered to the planning contrast-enhanced T1(T1-CE) MR. Axial slices of the Dmap, T1-CE, and PTV segmentation(PTVseg) intersecting the BM center were extracted within a fixed field-of-view determined by the V60% in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four VGG-19 deep encoders were utilized in an ensemble design, with each sub-model using a different spherical projection formula as input for BM outcome prediction. In each sub-model, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble’s outcome was synthesized from the four sub-model results via logistic regression. A total of 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared to 1) a single VGG-19 encoder; 2) an ensemble with T1-CE MRI as the sole image input after projections; and 3) an ensemble with the same image input design without clinical feature inclusion.The ensemble model achieved an excellent AUCROC=0.89±0.02 with high sensitivity(0.82±0.05), specificity(0.84±0.11), and accuracy(0.84±0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity:0.35±0.01, AUC:0.64±0.08), the MRI-only deep ensemble (sensitivity:0.60±0.09, AUC:0.68±0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity:0.78±0.08, AUC:0.84±0.03).Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated an excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiotherapy outcome models and warrants further evaluation.Copyright © 2024. Published by Elsevier Inc.