ArticleRadiation oncology (London, England)2024
Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.
Article in Radiation oncology (London, England), 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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Who cites it
7 citing papers in PubMed.
- Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients.Physics and imaging in radiation oncology · 2026Article
- Article
- Risk prediction model for radiation pneumonitis in breast cancer radiotherapy based on dose-volume parameters combined with the neutrophil-to-lymphocyte ratio.Frontiers in oncology · 2026Article
- An interpretable delta ultrasound radiomics model for predicting live birth outcomes in single vitrified-warmed blastocyst transfer.Journal of ovarian research · 2025Article
- A machine learning approach for radiation pneumonitis prediction in elderly esophageal cancer patients by integrating baseline computed tomography radiomics, dosiomics, and clinical characteristics.Physics and imaging in radiation oncology · 2025Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Mechanism of Shashen Maidong Decoction in the Treatment of Radiation Pneumonitis Based on Network Pharmacology and Molecular Docking.Current pharmaceutical design · 2025Article
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16 authors.
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Abstract
backgroundTo integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT).
methodsTotal of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated.
resultsModels with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively.
conclusionCT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
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