Abstract
Jie Zong, Haiyan Li, Zhiling Li
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
ror
Confidence 99%
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Confidence 99%
openalex
No local reference links have been materialized yet.
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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
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
Survival of patients with stage I lung cancer detected on CT screening
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Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
10.1007/s10278-020-00320-6 · 2020
Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017
10.1148/radiol.2017161659 · 2017
Applications and limitations of radiomics
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Radiomics: the facts and the challenges of image analysis
10.1186/s41747-018-0068-z · 2018
Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation
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Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules
Confidence 95%
doaj
Confidence 92%
datacite
Confidence 0%
10.3978/j.issn.2223-4292.2016.02.01 · 2016
Multilevel binomial logistic prediction model for Malignant pulmonary nodules based on texture features of CT image
10.1016/j.ejrad.2009.01.024 · 2010
CT texture analysis of histologically proven benign and Malignant lung lesions
10.1097/md.0000000000011172 · 2018
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
Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
10.1148/radiol.2019181960 · 2019
U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations
10.1148/radiol.2017161911 · 2017
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
Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases
10.21037/qims.2019.12.16 · 2020
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Textural features for image classification
10.1109/tsmc.1973.4309314 · 1973
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · 2014
Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study
10.21147/j.issn.1000-9604.2025.05.10 · 2025
Longitudinal multimodal assessment of pulmonary nodules in fibrosing interstitial lung diseases: a retrospective study
10.1007/s00408-026-00877-z · 2026
Longitudinal multimodal assessment of pulmonary nodules in fibrosing interstitial lung diseases: a retrospective study
10.1007/s00408-026-00877-z · 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 · doi-reference
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · doi-reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · doi-reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · doi-reference
Textural features for image classification
10.1109/tsmc.1973.4309314 · doi-reference
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
10.1016/j.jcm.2016.02.012 · doi-reference
Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases
10.21037/qims.2019.12.16 · 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 · doi-reference
U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations
10.1148/radiol.2017161911 · doi-reference
Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
10.1148/radiol.2019181960 · 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 · doi-reference
CT texture analysis of histologically proven benign and Malignant lung lesions
10.1097/md.0000000000011172 · 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 · 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 · doi-reference
Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation
10.1148/radiology.179.2.2014294 · doi-reference
Radiomics: the facts and the challenges of image analysis
10.1186/s41747-018-0068-z · doi-reference
Applications and limitations of radiomics
10.1088/0031-9155/61/13/r150 · doi-reference
Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017
10.1148/radiol.2017161659 · doi-reference
Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
10.1007/s10278-020-00320-6 · doi-reference
Survival of patients with stage I lung cancer detected on CT screening
10.1056/nejmoa060476 · 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 · 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 · doi-reference