Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Glioblastoma Patients.

Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Published Date: May 24, 2025

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 the adoption of the current standard-of-care treatment in 2005, no substantial prognostic improvement has been noticed. In this study, we seek the identification of prognostically relevant glioblastoma characteristics from routinely acquired hematoxylin and eosin-stained whole slide images (WSI) and clinical data, that integrated via advanced computational methods could yield improved patient prognostic stratification and hence optimize clinical decision making and patient management. The proposed WSI analysis capitalizes on a comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple- instance learning approach that further utilizes clustering to constrain the search space. Patterns automatically identified by our approach as of high prognostic value classify each WSI as representative of short or long survivors. Further assessments of the prognostic relevance of the associated clinical patient data are performed both in isolation and in an integrated manner, using XGBoost and shapley additive explanations (SHAP). The multimodal integration of WSI with clinical data yields enhanced stratification performance when compared with using either one of the modalities. Identifying tumor morphological and clinical patterns associated with short and long survival will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for further understanding and potentially treating glioblastoma.

Authors

  • Bhakti Baheti
    Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA.
  • Sunny Rai
    Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
  • Shubham Innani
    Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
  • Garv Mehdiratta
    School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
  • W Robert Bell
    Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
  • Sharath Chandra Guntuku
    Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
  • MacLean P Nasrallah
    Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Spyridon Bakas
    Perelman School of Medicine, Philadelphia, PA, USA.

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