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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 to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer

Lie Cai¹, Thomas M Deutsch¹, Chris Sidey-Gibbons^2,3

¹Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany.

JCO Clinical Cancer Informatics

|December 23, 2024

Summary

Machine learning algorithms can predict individual patient risk for severe toxicity during cancer treatment. This approach aids in proactive management and improving treatment efficacy.

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Area of Science:

Oncology
Medical Informatics
Machine Learning

Background:

Systemic cancer treatment toxicity causes patient anxiety and treatment challenges.
Predicting toxicity is crucial for effective cancer care management.
Early breast cancer patients undergoing neoadjuvant treatment are a key population.

Purpose of the Study:

To develop and validate machine learning algorithms for predicting treatment-relevant toxicity.
To identify patients at high risk for grade 3 or 4 adverse events.
To enhance the proactive management of cancer treatment side effects.

Main Methods:

Utilized clinical records from a single-center cohort of early breast cancer patients.
Developed and validated logistic regression (GLM) and support vector machine (SVM) algorithms.
Employed 10-fold cross-validation and evaluated performance using area under the curve (AUROC).

Main Results:

590 patients were included; 55.8% experienced grade 3 or 4 toxicity.
Algorithm performance significantly improved with the addition of treatment information (regimen, dose intensity).
GLM and SVM AUROC increased from 0.59/0.64 to 0.75 with enhanced data.

Conclusions:

Machine learning effectively predicts individual risk for treatment-related toxicity.
This predictive capability offers a promising strategy for proactive toxicity management.
Improved efficacy and patient outcomes are potential benefits of this approach.