  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.
