Research graph
References from Application value of high-resolution CT imaging features combined with texture analysis in patients with solitary pulmonary nodules. Local targets link to admitted publications; unresolved targets remain external evidence.
Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule
10.1038/srep34921 · 2016 · External reference
A prediction model to evaluate the pretest risk of Malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
10.1007/s00432-020-03408-2 · 2021 · External reference
Survival of patients with stage I lung cancer detected on CT screening
10.1056/nejmoa060476 · 2006 · External reference
Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
10.1007/s10278-020-00320-6 · 2020 · External reference
Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017
10.1148/radiol.2017161659 · 2017 · External reference
Applications and limitations of radiomics
10.1088/0031-9155/61/13/r150 · 2016 · External reference
Radiomics: the facts and the challenges of image analysis
10.1186/s41747-018-0068-z · 2018 · External reference
Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation
10.1148/radiology.179.2.2014294 · 1991 · External reference
Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules
10.3978/j.issn.2223-4292.2016.02.01 · 2016 · External reference
Multilevel binomial logistic prediction model for Malignant pulmonary nodules based on texture features of CT image
10.1016/j.ejrad.2009.01.024 · 2010 · External reference
CT texture analysis of histologically proven benign and Malignant lung lesions
10.1097/md.0000000000011172 · 2018 · External reference
A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from Malignant pulmonary nodule
10.3389/fonc.2022.1035307 · 2022 · External reference
Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
10.1148/radiol.2019181960 · 2019 · External reference
U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations
10.1148/radiol.2017161911 · 2017 · External reference
Air bronchogram on chest CT in radiological pure-solid appearance lung cancer: Correlation analysis with genetic pathological features and survival outcomes
10.1016/j.ejrad.2023.111194 · 2023 · External reference
Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases
10.21037/qims.2019.12.16 · 2020 · External reference
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
10.1016/j.jcm.2016.02.012 · 2016 · External reference
Textural features for image classification
10.1109/tsmc.1973.4309314 · 1973 · External reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · 1988 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · 2014 · External reference
Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study
10.21147/j.issn.1000-9604.2025.05.10 · 2025 · External reference
Longitudinal multimodal assessment of pulmonary nodules in fibrosing interstitial lung diseases: a retrospective study
10.1007/s00408-026-00877-z · 2026 · External reference
Longitudinal multimodal assessment of pulmonary nodules in fibrosing interstitial lung diseases: a retrospective study
10.1007/s00408-026-00877-z · ExternalCitation · doi-reference
A prediction model to evaluate the pretest risk of Malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population
10.1007/s00432-020-03408-2 · ExternalCitation · doi-reference
Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
10.1007/s10278-020-00320-6 · ExternalCitation · doi-reference
Multilevel binomial logistic prediction model for Malignant pulmonary nodules based on texture features of CT image
10.1016/j.ejrad.2009.01.024 · ExternalCitation · doi-reference
Air bronchogram on chest CT in radiological pure-solid appearance lung cancer: Correlation analysis with genetic pathological features and survival outcomes
10.1016/j.ejrad.2023.111194 · ExternalCitation · doi-reference
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
10.1016/j.jcm.2016.02.012 · ExternalCitation · doi-reference
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · ExternalCitation · doi-reference
Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule
10.1038/srep34921 · ExternalCitation · doi-reference
Survival of patients with stage I lung cancer detected on CT screening
10.1056/nejmoa060476 · ExternalCitation · doi-reference
Applications and limitations of radiomics
10.1088/0031-9155/61/13/r150 · ExternalCitation · doi-reference
CT texture analysis of histologically proven benign and Malignant lung lesions
10.1097/md.0000000000011172 · ExternalCitation · doi-reference
Textural features for image classification
10.1109/tsmc.1973.4309314 · ExternalCitation · doi-reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · ExternalCitation · doi-reference
Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017
10.1148/radiol.2017161659 · ExternalCitation · doi-reference
U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations
10.1148/radiol.2017161911 · ExternalCitation · doi-reference
Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
10.1148/radiol.2019181960 · ExternalCitation · doi-reference
Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation
10.1148/radiology.179.2.2014294 · ExternalCitation · doi-reference
Radiomics: the facts and the challenges of image analysis
10.1186/s41747-018-0068-z · ExternalCitation · doi-reference
Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases
10.21037/qims.2019.12.16 · ExternalCitation · doi-reference
Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study
10.21147/j.issn.1000-9604.2025.05.10 · ExternalCitation · doi-reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · ExternalCitation · doi-reference
A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from Malignant pulmonary nodule
10.3389/fonc.2022.1035307 · ExternalCitation · doi-reference
Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules
10.3978/j.issn.2223-4292.2016.02.01 · ExternalCitation · doi-reference
