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Optical Microscopy Predictions of Focal Recurrence in Glioblastoma

Medrxiv : the Preprint Server for Health Sciences +

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October 3, 2025

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October 3, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Artificial intelligence (AI) models can predict glioblastoma (GBM) recurrence by analyzing tumor infiltration in surgical samples. This AI-driven approach identifies high-risk areas, potentially improving targeted therapies for recurrent brain tumors.

Area Of Science

  • Neuro-oncology
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background

  • Glioblastoma (GBM) frequently recurs after initial treatment, posing a significant clinical challenge.
  • Current management of recurrent GBM lacks a standard of care.
  • Predicting recurrence location is crucial for optimizing advanced-stage therapies.

Purpose Of The Study

  • To develop and validate an AI-based model for predicting the risk of focal glioblastoma recurrence.
  • To assess the utility of AI-estimated tumor infiltration in predicting recurrence at resection margins.

Main Methods

  • An AI model was developed using label-free optical microscopy to quantify tumor infiltration (AI-infiltration) in surgical margin samples.
  • A random forest classifier integrated AI-infiltration with clinical, radiographic, and molecular data.
  • The model was trained and validated on a cohort of 80 patients with glioblastoma.

Main Results

  • Higher glioblastoma infiltration was observed in margin samples from recurrent tumors compared to non-recurrent ones (p = 0.026).
  • The AI-driven random forest model achieved high prediction accuracy for focal recurrence (86.6% training, 80.3% validation AUC).
  • AI-infiltration emerged as the strongest predictor, outperforming molecular features, and maintained performance across tumor locations.

Conclusions

  • AI-based prediction of tumor infiltration can accurately identify sites at high risk for glioblastoma recurrence.
  • This approach holds potential for guiding precision, multimodal therapies to specific high-risk areas.
  • The findings suggest a novel strategy for managing recurrent brain tumors by predicting recurrence patterns.