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Related Experiment Video

Updated: Aug 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine learning based survival prediction in Glioma using large-scale registry data.

Rachel Zhao¹, Ying Zhuge², Kevin Camphausen²

¹8166University of British Columbia, Canada.

Health Informatics Journal

|October 20, 2022

Summary

Machine learning models accurately predict glioma patient survival using clinical data. Radiation therapy and chemotherapy significantly improve prediction accuracy, highlighting their importance in treatment planning.

Keywords:

Artificial intelligence cancer registry data Mining electronic health records machine learning

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Area of Science:

Neuro-oncology
Medical Informatics
Computational Biology

Background:

Gliomas, common central nervous system tumors, have poor prognoses.
Accurate survival prediction is vital for optimal glioma treatment selection.
Machine learning (ML) offers advanced methods for survival prediction using clinical data.

Purpose of the Study:

To compare the effectiveness of different machine learning models for glioma survival prediction.
To identify key clinical features that enhance the accuracy of survival predictions in glioma patients.

Main Methods:

Utilized a large glioma dataset (3462 patients, 2000-2018).
Employed Cox Proportional Hazards (CPH), Support Vector Machine (SVM), and Random Forest (RF) models.
Assessed prediction accuracy using the concordance index (c-index), incorporating features like age, sex, surgery, histology, tumor site, radiation therapy (RT), and chemotherapy.

Main Results:

All three models demonstrated good prediction accuracy (CPH: 0.767, SVM: 0.771, RF: 0.57).
The best performance was achieved when including radiation therapy and chemotherapy status.
These treatment modalities emerged as critical predictive factors for patient survival.

Conclusions:

Machine learning models show significant potential for predicting glioma survival.
Incorporating treatment data, specifically radiation therapy and chemotherapy, is crucial for improving predictive accuracy.
Ensuring the accuracy of clinical registry data is essential for reliable ML-based prognostic tools in neuro-oncology.