ArticleThoracic cancer2020
Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.
Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Patient outcome prognosis for external beam radiation therapy using CBCT-based radiomics: a systematic review.Biomedical physics & engineering express · 2026Pooled it
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.Scientific reports · 2026Article
- RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research.Scientific data · 2026Article
- Review
- Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT.BMC medical imaging · 2025Article
- Longitudinal stability of HyperSightScientific reports · 2025Article
- Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung.Scientific reports · 2024Article
- Cone beam computed tomography (CBCT) and megavoltage computed tomography (MVCT)-based radiomics in head and neck cancers: a systematic review and radiomics quality score assessment.Quantitative imaging in medicine and surgery · 2024Review
- Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study.Radiation oncology journal · 2024Article
- Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study.Scientific reports · 2022Article
- Predicting survival in patients with glioblastoma using MRI radiomic features extracted from radiation planning volumes.Journal of neuro-oncology · 2022Article
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.Quantitative imaging in medicine and surgery · 2021Article
- Promising Biomarkers of Radiation-Induced Lung Injury: A Review.Biomedicines · 2021Review
- The role of radiomics in prostate cancer radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
- Prediction of Local Recurrence Using Clinical and Radiomic Features in Lung Oligometastases Treated with Stereotactic Body Radiotherapy.Technology in cancer research & treatmentArticle
- Predicting Stereotactic Body Radiation Therapy Response Using an AI-Based Tumor Vessel Biomarker.Technology in cancer research & treatmentArticle
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundStereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non-small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone-beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone.
methodsA total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression-free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves.
resultsFive CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression.
conclusionsBoth pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. KEY POINTS: Significant findings of the study: A model with cone-beam CT radiomics features plus pre-treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. WHAT THIS STUDY ADDS: In the prediction of PFS and lung toxicity in early-stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.
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