ArticleFrontiers in oncology2021
Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Trial
- A radiomics-based nomogram for differentiating minimally invasive adenocarcinoma from stage I invasive non-mucinous adenocarcinoma (CTR < 0.5, GGO-dominant).Updates in surgery · 2026Article
- Development of a Simplified Protocol for Respiratory Muscle Segmentation in Unenhanced Chest CT and Identification of New Radiomic Biomarkers of Sarcopenia in Lung Diseases: A Retrospective Study.Journal of clinical medicine · 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
- Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis.Journal of thoracic disease · 2024Article
- Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.BMC medical imaging · 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
- Article
- CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma.Frontiers in oncology · 2024Article
- Invasiveness identification in pure ground-glass nodules: exploring the generalizability of radiomics based on external validation and stress testing.Journal of cancer research and clinical oncology · 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
- Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2023Article
- 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.Quantitative imaging in medicine and surgery · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Development and validation ofEJNMMI research · 2022Article
- Diagnostic value of double low-dose targeted perfusion CT imaging for the diagnosis of invasive and preinvasive pulmonary ground-glass nodules: systematic review and meta-analysis.Translational cancer research · 2022Article
- Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.The oncologist · 2022Review
- Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence.Diagnostics (Basel, Switzerland) · 2022Article
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10 authors.
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Abstract
purposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma MATERIALS AND
methodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.
resultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.
conclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.
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