Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance

TL;DR

This study develops an advanced computational approach combining histopathology images and clinical data to predict glioblastoma patient survival, aiming to improve prognostic accuracy and inform treatment decisions.

Contribution

It introduces a weakly supervised attention-based multiple-instance learning algorithm that identifies prognostically relevant tumor patterns from histology images and clinical data.

Findings

Patterns of high diagnostic value classify patients into short or long survivors.

Integrated analysis of histology and clinical data enhances prognostic predictions.

The approach provides interpretable insights for clinical decision-making.

Abstract

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Accurate prediction of patient overall survival (OS) from histopathology whole slide images (WSI) integrated with clinical data using advanced computational methods could optimize clinical decision-making and patient management. Here, we focus on identifying prognostically relevant glioblastoma characteristics from H&E stained WSI & clinical data relating to OS. The exact approach for WSI capitalizes on the comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple-instance learning algorithm that further utilizes clustering to constrain the search space. The automatically placed patterns of high diagnostic value classify each WSI as representative of short or long-survivors. Further assessment of the prognostic relevance of the associated clinical patient data is performed both in isolation and in an integrated manner, using XGBoost and SHapley Additive exPlanations (SHAP). Identifying tumor morphological & clinical patterns associated with short and long OS will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for understanding and potentially treating glioblastoma.