ArticleScientific reports2021
Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks.European radiology · 2024Pooled it
- Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.Radiation oncology (London, England) · 2022Trial
- CT-based deep foundation model for predicting immune checkpoint inhibitor-induced pneumonitis risk in lung cancer.Journal for immunotherapy of cancer · 2026Article
- Radiomics features combined with clinical and CT features for predicting the Ki-67 index in stage T1 non-small cell lung cancer patients: a multicenter study.Journal of thoracic disease · 2026Article
- Radiomic imaging models for predicting breast cancer prognosis based on Interleukin-18 (IL18).BMC medical imaging · 2025Article
- Radiomics-based classification of medication-related osteonecrosis of the jaw using panoramic radiographs.Oral radiology · 2025Article
- Radiation Pneumonitis Prediction Using Dual-Modal Data Fusion Based on Med3D Transfer Network.Journal of imaging informatics in medicine · 2025Article
- Review
- Impact of omitting clinical target volume in radiotherapy for locally advanced non-small cell lung cancer: a propensity score matching analysis.Translational lung cancer research · 2025Article
- Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.Scientific reports · 2025Article
- Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis.Cancer immunology, immunotherapy : CII · 2025Article
- Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics.Frontiers in oncology · 2025Article
- Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.Radiation oncology (London, England) · 2024Article
- Determining risk and predictors of head and neck cancer treatment-related lymphedema: A clinicopathologic and dosimetric data mining approach using interpretable machine learning and ensemble feature selection.Clinical and translational radiation oncology · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC.Scientific reports · 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
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis.Frontiers in immunology · 2023Review
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Authors and funding
10 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction from the whole lung (whole-lung radiomics analysis) and imaging- and dosimetric-based segmentation (multi-region radiomics analysis) were performed. Patients with RP grade ≥ 2 or < 2 were classified. Predictors were selected with least absolute shrinkage and selection operator logistic regression and the model was built with neural network classifiers. A total of 49,383 radiomics features per patient image were extracted from the radiotherapy planning computed tomography. We identified 4 features and 13 radiomics features in the whole-lung and multi-region radiomics analysis for classification, respectively. The accuracy and area under the curve (AUC) without the synthetic minority over-sampling technique (SMOTE) were 60.8%, and 0.62 for whole-lung and 80.1%, and 0.84 for multi-region radiomics analysis. These were improved 1.7% for whole-lung and 2.1% for multi-region radiomics analysis with the SMOTE. The developed multi-region radiomics analysis can help predict grade ≥ 2 RP. The radiomics features in the median- and high-dose regions, and the local intensity roughness and variation were important factors in predicting grade ≥ 2 RP.
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