ArticleBMC cancer2020
CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Evaluation of locoregional invasiveness of early lung adenocarcinoma manifesting as ground-glass nodules via [European journal of nuclear medicine and molecular imaging · 2025Trial
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Preoperative evaluation of solitary pulmonary nodules and adenocarcinoma invasiveness using ultra-high-resolution computed tomography and multidimensional liquid biopsy: a prospective exploratory study.Frontiers in oncology · 2026Article
- Correlation between CT growth patterns and invasiveness progression in neoplastic subcentimeter sub-solid nodules.Annals of medicine · 2025Article
- Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.Journal of cancer research and clinical oncology · 2025Article
- A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.The Thoracic and cardiovascular surgeon · 2025Article
- Retrospective Analysis Comparing Lung-RADS v2022 and British Thoracic Society Guidelines for Differentiating Lung Metastases from Primary Lung Cancer.Biomedicines · 2025Article
- Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.Thoracic cancer · 2024Article
- Enhanced tumor targeting and penetration of fluorophores via iRGD peptide conjugation: a strategy for the precision targeting of lung cancer.Translational lung cancer research · 2024Article
- Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China.Quantitative imaging in medicine and surgery · 2024Article
- Article
- The predictive value of computed tomography value on high-resolution images in differentiating invasive from indolent lung adenocarcinoma.Translational cancer research · 2024Article
- CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma.Frontiers in oncology · 2024Article
- Clinical characteristics and progression of pre-/minimally invasive lung adenocarcinoma harboringTranslational lung cancer research · 2023Article
- A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2023Article
- [Wedge Resection of Pulmonary Nodules (≤2 cm): A Consensus Statement by Specialists of Thoracic Surgery (2023 Edition)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Article
- Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules.The clinical respiratory journal · 2023Article
- A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules.Frontiers in oncology · 2023Article
- Value of preoperativePeerJ · 2023Article
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Abstract
backgroundThe value of the CT features and quantitative analysis of lung subsolid nodules (SSNs) in the prediction of the pathological grading of lung adenocarcinoma is discussed.
methodsClinical data and CT images of 207 cases (216 lesions) with CT manifestations of an SSNs lung adenocarcinoma confirmed by surgery pathology were retrospectively analysed. The pathological results were divided into three groups, including atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Then, the quantitative and qualitative data of these nodules were compared and analysed.
resultsThe mean size, maximum diameter, mean CT value and maximum CT value of the nodules were significantly different among the three groups of AAH/AIS, MIA and IAC and were different between the paired groups (AAH/AIS and MIA or MIA and IAC) (P < 0.05). The critical values of the above indicators between AAH/AIS and MIA were 10.05 mm, 11.16 mm, - 548.00 HU and - 419.74 HU. The critical values of the above indicators between MIA and IAC were 14.42 mm, 16.48 mm, - 364.59 HU and - 16.98 HU. The binary logistic regression analysis of the features with the statistical significance showed that the regression model between AAH/AIS and MIA is logit(p) = - 0.93 + 0.216X
conclusionsPreoperative prediction of pathological classification of CT image features has important guiding value for clinical management. Correct diagnosis results can effectively improve the patient survival rate. Through comprehensive analysis of the CT features and qualitative data of SSNs, the diagnostic accuracy of SSNs can be effectively improved. The logistic regression model established in this study can better predict the pathological classification of SSNs lung adenocarcinoma on CT, and the predictive value is significantly higher than the independent use of each quantitative factor.
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