ArticleQuantitative imaging in medicine and surgery2023
Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models.Journal of cancer research and clinical oncology · 2023Trial
- Moran's I-driven habitat radiomics: A biologically plausible and temporally robust approach for risk stratification of lung adenocarcinoma invasiveness.European journal of radiology open · 2026Article
- Identification of prognostic-related tumor microenvironment genes in lung adenocarcinoma and establishment of a prognostic prediction model.Translational lung cancer research · 2025Article
- Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive lung adenocarcinoma.Translational lung cancer research · 2025Article
- Nomogram integrating clinical-radiological and radiomics features for differentiating invasive from non-invasive pulmonary adenocarcinomas presenting as ground-glass nodules.American journal of cancer research · 2025Article
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2024Article
- Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Repeatability and reproducibility of deep learning features for lung adenocarcinoma subtypes with nodules less than 10 mm in size: a multicenter thin-slice computed tomography phantom and clinical validation study.Quantitative imaging in medicine and surgery · 2024Article
- Pathological and imaging features of pulmonary invasive mucinous adenocarcinoma-a retrospective cohort study.Translational lung cancer research · 2024Article
- A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network.Quantitative imaging in medicine and surgery · 2023Article
- The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma.Translational lung cancer research · 2023Article
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7 authors.
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Abstract
Background: Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features. Methods: This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum redundancy maximum relevance (mRMR) feature selection, and the top 10 features were determined by the least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating 3D radiomic signatures and radiological features in the prediction system. The nomogram was evaluated from multiple perspectives and tested on the validation cohort. Results: The model combined 3 radiological features and seven 3D radiomic signatures. The area under the curve (AUC) of the model was 0.877 (95% CI: 0.829-0.925) in the training cohort, 0.864 (95% CI: 0.789-0.940) in the testing cohort, and 0.836 (95% CI: 0.749-0.924) in the validation cohort. The nomogram applied in all 3 cohorts showed reliable accuracy and calibration. The decision curve also demonstrated the clinical effectiveness of the nomogram. Conclusions: In this study, a nomogram-based model combining 3D radiomic signatures and radiological features was developed. Its performance in identifying IAC and MIA/AIS was satisfactory and had clinical value.
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