ArticleClinical and translational radiation oncology2023
Pseudo-siamese network combined with dosimetric and clinical factors, radiomics features, CT images and 3D dose distribution for the prediction of radiation pneumonitis: A feasibility study.
Article in Clinical and translational radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- 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
- Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.JCO clinical cancer informatics · 2025Article
- Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.Radiation oncology (London, England) · 2024Article
- Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.Journal of applied clinical medical physics · 2024Article
- A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome.Journal of cancer research and clinical oncology · 2023Article
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9 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: Radiation pneumonitis (RP)(grade ≥ 2) can have a considerable impact on patient quality-of life. In previous studies, the traditional method commonly used radiomics and clinical factors for RP prediction. This study aims to develop and evaluate a novel pseudo-siamese network (PSN) to assist radiologists predict RP before radiotherapy based on combination of dosimetric and clinical factors, radiomics features, CT (computed tomography) images, and dose distribution (hybrid model). Method: One hundred and ten patients with lung cancer (19 RP ≥ 2) who received radiotherapy between 2016 and 2020 were retrospectively enrolled in this study. Dosimetric factors were calculated from DVH (dose-volume histogram), such as lung mean dose, lung V Results: For cluster analysis, totally of 106 radiomics features with high correlation were selected. The accuracy was 0.727, 0.636, 0.545, and 0.727 for input dosimetric and clinical factors, dose distribution, CT images, and radiomics features, respectively. The accuracy of hybrid model was 0.818. The sensitivity of hybrid model was 0.800 (95 % confidence interval (CI) [0.299, 0.989]), and specificity was 0.833(95 % CI [0.364, 0.991]). The areas under the receiver operating characteristic curves (AUCs) result in 5-fold cross-validation was 0.77-0.90(mean AUC ± std was 0.85 ± 0.05). Conclusion: This study firstly propose method that the combination of high dimensional and low dimensional features for RP prediction. The results confirm the feasibility of multi-dimensional features predict RP.
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