ArticleCancers2024
Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Association of Cardiac and Pulmonary CT Imaging Features with Respiratory Side Effects After Whole-Breast Radiotherapy.Cancers · 2026Article
- Evidence of different predictors for different types of cardiotoxicities after hypo-fractionated whole breast radiotherapy.Frontiers in oncology · 2026Article
- Article
- Voxel-Based Dose-Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective.Journal of clinical medicine · 2025Article
- Prediction of plaque progression using different machine learning models of pericoronary adipose tissue radiomics based on coronary computed tomography angiography.European journal of radiology open · 2025Article
- Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy.Physics and imaging in radiation oncology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDifferent ML models were compared to predict toxicity in RT on a large cohort (n = 1314).
methodsThe endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high
resultsThe model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM).
conclusionsNo model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.
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Registered trials
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