DSAI-03 DEEP LEARNING-BASED PREDICTION OF IMMUNE CHECKPOINT INHIBITOR EFFICACY IN BRAIN METASTASES USING BRAIN MRI

Abstract Brain metastases (BM) are an emerging challenge in modern oncology due to increasing incidence and limited treatments. Recent work by our group and others has illustrated that immune checkpoint inhibitors (ICI) are a promising therapy for treatment-refractory BM of diverse histologies. To build upon our findings and integrate ICI into precision medicine strategies for BM, scalable tools that quantify likelihood of response are needed. We curated a multi-institutional dataset of longitudinal mpMRI for 860 BM patients treated with ICI, assessing 2,542 BM responses based on pre- and 6-month post-treatment MRIs using standardized RANO criteria. A convolutional neural network (CNN) trained on pre-treatment mpMRI sequences predicted 6-month ICI efficacy. To ensure class balance and clinical relevance, four RANO classes were converted into a binary model: intracranial benefit including complete response (CR), partial response (PR) and stable disease (SD) vs. progressive disease (PD). Our custom CNN consists of three convolutional and two fully connected layers, with an 80/20 train-validation split. We also developed a "foundation model" for brain metastases using self-supervised contrastive learning on 21,179 BM private and public mpMRIs from more than 15,000 patients with a 3D ResNet50 architecture. The model was fine-tuned for ICI efficacy prediction using the same dataset that trained the CNN, held out during pretraining. A logistic regression model was trained on features extracted from the foundation model. Our CNN achieved an AUROC score of 0.650 on the validation set. Fine-tuning the foundation model with a linear classifier improved AUROC to 0.7904. Ongoing work includes optimizing models, developing multi-class response models (e.g., CR/PR vs. SD vs. PD), and conducting histology-specific analyses. Our study presents one of the first Deep Learning-based efforts to predict ICI efficacy for BM. There is emerging promise in using Deep Learning to identify imaging patterns of biological significance within clinically acquired imaging.