ArticleRadiation oncology (London, England)2021
Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Evaluation of Variability of Dosiomics Features with Varying Prescribed Dose in Prostate Cancer.Journal of biomedical physics & engineering · 2026Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Radiation-induced lung injury: from mechanism to prognosis and drug therapy.Radiation oncology (London, England) · 2025Review
- Added Value of Biological Effective Dose in Dosiomics-Based Modelling of Late Rectal Bleeding in Prostate Cancer.Cancers · 2024Article
- Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning.Clinical and translational radiation oncology · 2024Article
- Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.Radiation oncology (London, England) · 2024Article
- A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.European journal of medical research · 2024Article
- Development and validation of a lung biological equivalent dose-based multiregional radiomic model for predicting symptomatic radiation pneumonitis after SBRT in lung cancer patients.Frontiers in oncology · 2024Article
- Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study.Frontiers in oncology · 2024Article
- Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition.Frontiers in oncology · 2023Article
- Article
- Risk Factors of Acute Radiation-Induced Lung Injury Induced by Radiotherapy for Esophageal Cancer.Computational and mathematical methods in medicine · 2022Article
- Evaluating the Predictive Value of the Dose-volume Parameters and Vascular Endothelial Growth Factor Expression on Rectal Toxicity in Prostate Cancer Patients.Journal of medical physicsArticle
- Comparison of Dosiomics Features and Dosimetric Parameters for Detecting Variations in Dose Distribution in Breast Cancer Radiotherapy.Journal of medical physicsArticle
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Authors and funding
5 authors.
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Abstract
objectiveThe purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). MATERIALS AND
methodsDVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs).
resultThe AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2.
conclusionDosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.
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