Advanced Deep Learning Methods for Brain Metastasis Post-SRS Outcome Management

Purpose: The purpose of this study is to develop and validate two deep learning (DL) models for the management of brain metastasis (BM) patients treated with stereotactic radiosurgery (SRS). The first model is a radiomics-integrated deep learning (RIDL) model, which aims to distinguish between radionecrosis and tumor recurrence in patients with post-SRS radiographic progression. The second model, a novel dose-incorporated deep ensemble learning (DEL) model, aims to accurately predict local failure outcomes in brain metastasis patients following SRS.Materials/Methods: A total of 51 patients with post-SRS radiographic progression (37 radionecrosis, 14 recurrence) and 114 BMs (including 26 BMs that developed biopsy-confirmed local failure post-SRS) from 85 patients were included in this study. For the first aim, a radiomics-integrated deep learning (RIDL) model was developed using three steps: 1) 184 radiomics features (RFs) were extracted from the SRS planning target volume (PTV) and 60% isodose volume (V60%); 2) a deep neural network (DNN) mimicking the encoding path of U-net was trained for radionecrosis or recurrence prediction using the 3D MR volume. Prior to the binary prediction output, latent variables in the DNN were extracted as 512 deep features (DFs); and 3) all extracted features were synthesized as a multi-dimensional input for support vector machine (SVM) execution. Key features with higher linear kernel weighting factors were identified by clustering analysis and were utilized by SVM to predict radionecrosis or recurrence. During model training, 50 model versions were acquired with random validation sample assignments following an 8:2 training/test ratio, and sensitivity, specificity, accuracy, and ROC were evaluated and compared with results from a radiomics-only and a DNN-only prediction model. For the second aim, a novel dose-incorporated deep ensemble learning (DEL) model was developed. The DEL design included four VGG-19 deep encoder networks, and each sub-network utilized a different variable type as input for BM outcome prediction. The DEL's outcome was synthesized from the four sub-network results via logistic regression. For each BM, four variables were obtained, including three with different curvatures during spherical projection and one with the original planar images. The proposed DEL model was developed using an 8:2 ratio for training/test assignment, and 10 model versions were acquired with random validation sample assignments. The DEL model performance was compared based on ROC analysis to a single VGG-19 encoder and to DEL models with the same projection designs, which used T1-CE MRI as the only input. Results: The RIDL model demonstrated superior performance compared to radiomics-based and DNN-only prediction models for distinguishing radionecrosis from tumor recurrence in brain metastasis patients with post-SRS radiographic progression. The RIDL model achieved the best prediction accuracy (0.643±0.059) and sensitivity (0.650±0.122) results with 32 identified key features (3 RFs+29 DFs), and it also demonstrated superior ROC results (AUC=0.688±0.035). In addition, for patients with NSCLC primary disease, the RF joint energy extracted from V60% and one DF correlated with ALK/EGFR mutations, respectively. Moreover, the DEL model achieved an excellent ROC AUC=0.84±0.03 with high sensitivity (0.78±0.08), specificity (0.81±0.09), and accuracy (0.80±0.06) results. This outperformed the MRI-only single VGG-19 encoder (sensitivity:0.35±0.01, AUC:0.64±0.08) and the MRI-only DEL (sensitivity:0.60±0.09, AUC:0.68±0.06) models. Conclusions: The RIDL model successfully differentiates brain metastasis radionecrosis from recurrence using a single post-SRS MR scan. Integration of clinical and treatment-related features is warranted to develop a comprehensive clinico-radiomic model. Additionally, the dose-incorporated DEL model design demonstrated robust and promising performance. It could potentially improve other radiotherapy outcome models and warrant further evaluation.