Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors
Highlights • Extraction, analysis, and interpretation of historical treatment planning data is valuable but very time-consuming.• Proposed machine learning model classifies radiotherapy plans based on their treatment planning objectives and trade-offs.• Application of double nested cross-validation enabled to build a robust model that achieved 94% accuracy on a testing data.• Model reasoning investigated with SHAP values showed consistency with clinical observations.
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Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors
Semantic Scholar · Medicine · 2021
Abstract
Highlights
- Extraction, analysis, and interpretation of historical treatment planning data is valuable but very time-consuming.
- Proposed machine learning model classifies radiotherapy plans based on their treatment planning objectives and trade-offs.
- Application of double nested cross-validation enabled to build a robust model that achieved 94% accuracy on a testing data.
- Model reasoning investigated with SHAP values showed consistency with clinical observations.
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