ArticleBMC cancer2024
Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Assessment of rotational dose perturbations in SBRT: A radiomic-dosiomic predictive model based on the structural similarity index measure.Journal of applied clinical medical physics · 2026Article
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- Using Radiomics and Explainable Ensemble Learning to Predict Radiation Pneumonitis and Survival in NSCLC Patients Post-VMAT.Life (Basel, Switzerland) · 2025Article
- Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.World journal of radiology · 2025Article
- Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?Diagnostics (Basel, Switzerland) · 2025Article
- Comparison of adverse events between intensity-modulated radiation therapy and tomotherapy for early stage breast cancer: a retrospective cohort study.Frontiers in oncology · 2025Article
- Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning with multimodal data integration.Frontiers in oncology · 2025Article
- Development and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer.Frontiers in oncology · 2025Article
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Authors and funding
14 authors.
Funding
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Abstract
purposeThis study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). MATERIALS AND
methodsThis study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.
resultsIn this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2
conclusionIntegrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2
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