ArticleJournal of computer assisted tomography
Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography.
Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis.Frontiers in public health · 2022Pooled it
- Diagnostic Performance and Imaging Features of Dual-Phase Contrast-Enhanced CT in Peripheral Lung Cancer: A Prospective Study With Pathological Correlation.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics.Cancer medicine · 2026Article
- Diagnostic Accuracy of Artificial Intelligence Models for Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of Lung-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Pilot Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-Small-Cell Lung Cancer in the Moscow Population: A Step Toward Virtual Biopsy.Journal of imaging · 2025Article
- Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.Cancer medicine · 2024Article
- Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.Thorax · 2024Article
- Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.Frontiers in veterinary science · 2024Article
- A pairwise radiomics algorithm-lesion pair relation estimation model for distinguishing multiple primary lung cancer from intrapulmonary metastasis.Precision clinical medicine · 2023Article
- Radiomics nomogram for the prediction of Ki-67 index in advanced non-small cell lung cancer based on dual-phase enhanced computed tomography.Journal of cancer research and clinical oncology · 2023Article
- Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed.BMC pulmonary medicine · 2023Article
- Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model.Journal of healthcare engineering · 2023Article
- Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.F1000Research · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.Frontiers in oncology · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesThe aim of this study was to evaluate the performance of the radiomics method in classifying lung cancer histological subtypes based on multiphasic contrast-enhanced computed tomography (CT) images.
methodsA total of 229 patients with pathologically confirmed lung cancer were retrospectively recruited. All recruited patients underwent nonenhanced and dual-phase chest contrast-enhanced CT; 1160 quantitative radiomics features were calculated to build a radiomics classification model. The performance of the classification models was evaluated by the receiver operating characteristic curve.
resultsThe areas under the curve of radiomics models in classifying adenocarcinoma and squamous cell carcinoma, adenocarcinoma and small cell lung cancer, and squamous cell carcinoma and small cell lung cancer were 0.801, 0.857, and 0.657 (nonenhanced); 0.834, 0.855, and 0.619 (arterial phase); and 0.864, 0.864, and 0.664 (venous phase), respectively. Moreover, the application of contrast-enhanced CT may affect the selection of radiomics features.
conclusionsOur study indicates that radiomics may be a promising tool for noninvasive predicting histological subtypes of lung cancer based on the multiphasic contrast-enhanced CT images.
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