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Updated: Jun 13, 2026

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

Hamed Akbari¹, Spyridon Bakas^2,3,4,5, Chiharu Sako^6,7,8

¹Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, California, USA.

|December 12, 2024

Summary

A machine learning model accurately predicts glioblastoma patient outcomes using routine data. This tool stratifies patients into prognostic subgroups, aiding personalized treatment and clinical trials for brain cancer.

Keywords:

glioblastoma machine learning mpMRI prognostic subgrouping survival

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Published on: September 13, 2022

Area of Science:

Oncology
Medical Imaging
Machine Learning

Background:

Glioblastoma (GBM) is a highly aggressive brain cancer with significant patient heterogeneity.
This heterogeneity complicates patient management, treatment planning, and clinical trial stratification.

Purpose of the Study:

To develop a reproducible, personalized prognostication and clinical subgrouping system for glioblastoma.
To leverage machine learning on routine clinical and imaging data for improved patient stratification.

Main Methods:

Developed a machine learning model using routine clinical data, MRI, and molecular measures from 2838 diverse patients across 22 institutions.
Stratified patients into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis and Cox proportional models.

Main Results:

The ML model successfully stratified patients into distinct prognostic subgroups with significant hazard ratios.
Imaging features provided unique prognostic value, supporting a generalizable prognostic classification system.

Conclusions:

The ML model is reproducible and accessible online, using routine imaging data.
This platform facilitates personalized patient management and clinical trial stratification for glioblastoma.