A review identified 32 studies on machine learning for cancer outcome prediction.
Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.
Adib Hossain et al. · Digital health · 2026
Background
Cancer treatment poses significant challenges due to variability in patient responses, disease progression, and therapy outcomes.
Purpose
This scoping review aimed to explore how ML-powered CDSS are applied to predict treatment outcomes across different types of cancer.
Methods
A systematic search was conducted across six databases covering studies from 2010 to 2025.
n = 32 studies
Results
studies exploring machine learning to predict cancer outcomes
n = 32 studies
More results
Predictive objectives ranged from survival estimation and therapy response to toxicity risk and recurrence detection.
ML techniques varied from decision trees and vector machines to deep learning models such as convolutional neural networks.
More results
While technical performance was promising, few studies demonstrated external validation or integration into clinical workflows.
Interpretability, ethical considerations, and patient involvement were frequently underreported.
“
Conclusion · 1 of 2
ML-enabled CDSS have shown significant potential in predicting cancer treatment outcomes, yet their adoption in practice remains limited.
Conclusion · 2 of 2
Advancing these systems requires focus on validation, interpretability, data integration, and ethical design to bridge the gap between innovation and clinical utility.