Evidence map›Paper›PMID 41374969›Full record

ArticleCancers2025

Dose-Guided Hybrid AI Model with Deep and Handcrafted Radiomics for Explainable Radiation Dermatitis Prediction in Breast Cancer VMAT.

Tsair-Fwu Lee, Ling-Chuan Chang-Chien, Lawrence Tsai, Chia-Hui Chen, Po-Shun Tseng, Jun-Ping Shiau, Yang-Wei Hsieh, Shyh-An Yeh, Cheng-Shie Wuu, Yu-Wei Lin and 1 more

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Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Tsair-Fwu LeeMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.ORCID 0000-0002-3112-3594
Ling-Chuan Chang-ChienMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
Lawrence TsaiMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.ORCID 0009-0002-1404-6758
Chia-Hui ChenMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
Po-Shun TsengMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.
Jun-Ping ShiauMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.ORCID 0000-0003-0443-962X
Yang-Wei HsiehMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.ORCID 0000-0002-1374-0775
Shyh-An YehDepartment of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung 82445, Taiwan.
Cheng-Shie WuuDepartment of Radiation Oncology, Columbia University, New York, NY 10032, USA.
Yu-Wei LinDepartment of Radiation Oncology, Kaohsiung Veterans General Hospital, Kaohsiung 81341, Taiwan.
Pei-Ju ChaoMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.

Funding

National Science and Technology Council 113-2221-E-992-011-MY2, 114-2637-8-992-002
6 · The paper itself

Abstract

purposeTo improve the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT), we developed a hybrid artificial intelligence (AI) model that integrates deep learning radiomics (DLR), handcrafted radiomics (HCR), clinical features, and dose-volume histogram (DVH) parameters, aiming to enhance the early identification of high-risk individuals and support personalized prevention strategies.

methodsA retrospective cohort of 156 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital (2018-2023) was analyzed; 148 patients were eligible after exclusions, with RD graded according to the RTOG criteria. Clinical variables and 12 DVH indices were collected, while HCR features were extracted via PyRadiomics. DLR features were derived from a pretrained VGG16 network across four input designs: original CT images (DLR

resultsAmong the 148 patients, 49 (33%) developed Grade ≥ 2 RD. The DLR models outperformed the HCR models (AUC = 0.72 vs. 0.66). The best performance was achieved with DLR

conclusionsThe proposed hybrid AI framework, which integrates DLR, clinical, and DVH features, provides accurate and explainable predictions of Grade ≥ 2 RD after VMAT in breast cancer patients. By combining ensemble learning with XAI methods, the model offers reliable high-risk stratification and potential clinical utility for personalized treatment planning.

Indexed as

breast cancerdeep learning radiomicsensemble learningexplainable artificial intelligenceradiation dermatitisvolumetric modulated arc therapy

Identifiers

PMID41374969
PMCPMC12691004

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