ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019
Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.European radiology · 2020Trial
- Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies.Journal of thoracic disease · 2026Article
- Innovative approaches for lung cancer screening and interception.Nature reviews. Clinical oncology · 2026Review
- Risk factors for fast-growing lung cancers detected on chest CT: a retrospective cohort study.Frontiers in oncology · 2026Article
- Phantom-based evaluation of radiomics feature stability for low-dose CT lung cancer screening.Frontiers in endocrinology · 2026Article
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- A novel radiomics model combining GTVp, GTVnd, and clinical data for chemoradiotherapy response prediction in patients with advanced NSCLC.Frontiers in medicine · 2025Article
- Correlation analysis of clinical, pathological, imaging and genetic features of ground-glass nodule featured lung adenocarcinomas between high-risk and non-high-risk individuals.European journal of medical research · 2023Article
- Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.BMC pulmonary medicine · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers.Cancer biomarkers : section A of Disease markers · 2022Article
- Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.Cold Spring Harbor perspectives in medicine · 2021Review
- Radiogenomics in Interventional Oncology.Current oncology reports · 2021Review
- Maximum Standardized Uptake Value ofFrontiers in oncology · 2021Article
- [Research Progress on Lung Cancer Screening].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- [Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice.Journal of thoracic disease · 2020Article
- Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations.BMC cancer · 2019Article
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9 authors.
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Abstract
backgroundWe retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features.
methodsOne hundred fifty lung nodules among 114 screen-detected, incident lung cancer patients from the National Lung Screening Trial (NLST) were investigated. Volume double time (VDT) was calculated as the difference between continuous two scans and used to define indolent and aggressive lung cancers. Lung nodules were semi-automatically segmented using lung and mediastinal windows separately, and subtracting the mediastinal window region from the lung window region generated the difference region. 364 radiomic features were separately exacted from nodules using the lung window, the mediastinal window and the difference region. Multivariable models were conducted to identify the most predictive features in predicting tumor growth. Clinical information was also obtained from the database.
resultsBased on our definition, 26% of the cases were indolent lung cancer. The tumor growth pattern could be predicted by radiomic models constructed using features obtained in the lung window, the difference region, and by combining features obtained in both the lung window and difference regions with areas under the receiver operator characteristic (AUROCs) of 0.799, 0.819, and 0.846, respectively. The multi-window feature model showed better performance compared to single window features (P < 0.001). Incorporating clinical factors into the multi-window feature models showed improvement, yielding an accuracy of 84.67% and AUROC of 0.855 for distinguishing indolent from aggressive disease.
conclusionsMulti-window CT based radiomics features are valuable predictors of indolent lung cancers and out performed single CT window setting. Combining clinical information improved predicting performance.
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