SDPS-41 PATHOMICS AUGMENTS PREDICTIVE SURVIVAL MODELLINGIN MEDULLOBLASTOMA

Abstract

Abstract PURPOSE Medulloblastoma survival and treatment response has shown wide variability based on molecular and genomic subtyping. Early prediction of overall survival (OS) may afford clinicians an opportunity to develop individualized treatment paradigms for patients. Computational pathology (“pathomics”) confers the ability to extract sub-visual histopathologic features using high-throughput analysis to better characterize tumor biology or behavior. METHODS Using the Children’s Brain Tumor Network database, pediatric patients with available clinical data and whole-slide images (WSI) were evaluated for inclusion in the study. 1mm x 1mm regions-of-interest (ROI) on each H&E-stained WSI were selected to identify tumor-rich areas for subsequent pathomic analysis, and 49 quantitative pathomics features were extracted using a digital pathology software package (Qupath). LASSO cox proportional hazard model with stratified 5-fold cross-validation was used to identify high-performing features and generate risk scores. Estimated risk scores were subsequently used to stratify patients into low-, medium- and high-risk OS cohorts. Two separate models were trained including either: 1) clinical variables only; or 2) combined clinical and pathomic features. RESULTS A total of 84 patients with median age at diagnosis of 8.61 years (range 0.31-21.72), and median OS of 45.6 months (range 0.76-195.87)) were included in the study. A survival model built using only clinical features yielded a concordance index (c-index) of 0.76 compared to actual outcomes (stratification: p<0.001). Combining clinical and high-performing pathomic features, however, yielded the best performance of the model, showing disparate outcomes between low-, medium- and high-risk groups (p<0.001) with resultant c-index of 0.85. CONCLUSION Our results highlight the utility of pathomics analysis in predicting survival in patients with pediatric medulloblastoma, which can be used to guide treatment strategy. Further work will aim to perform our analysis on an unseen validation set to improve its performance and applicability in the clinical realm.