Summary of Uncertainty Quantification For Improving Radiomic-based Models in Radiation Pneumonitis Prediction, by Chanon Puttanawarut et al.
Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction
by Chanon Puttanawarut, Romen Samuel Wabina, Nat Sirirutbunkajorn
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Medical Physics (physics.med-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes a novel approach to improve the confidence in machine learning (ML) model predictions for radiation pneumonitis (RP), a side effect of thoracic radiation therapy. By incorporating radiomic, dosiomic, and dosimetric features, ML models can provide better predictions than traditional methods using dose-volume histograms (DVHs). To further enhance clinical decision-making, the study explores the impact of post-hoc uncertainty quantification (UQ) methods on model performance and calibration. Four ML models – logistic regression (LR), support vector machines (SVM), extreme gradient boosting (XGB), and random forest (RF) – are evaluated using a combination of features to predict RP. The results show that UQ methods improve predictive accuracy, particularly for high-certainty predictions, while also enhancing calibration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve the accuracy and reliability of machine learning models in predicting radiation pneumonitis. It uses special kinds of data called radiomic, dosiomic, and dosimetric features to help machines learn better. The researchers tested four different machine learning models and found that adding a way to measure how sure they are about their predictions helps make the results more reliable. |
Keywords
» Artificial intelligence » Extreme gradient boosting » Logistic regression » Machine learning » Random forest
