ArticleScientific reports2021
A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Diagnostic accuracy of combined TCF-21 protein expression and computed tomography morphological features for predicting microvascular invasion in early-stage non-small cell lung cancer.Journal of thoracic disease · 2026Article
- Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.La Radiologia medica · 2026Article
- CT-based interpretable delta-radiomics model for risk stratification of pulmonary ground-glass nodules: a multicentre study.Insights into imaging · 2026Article
- Habitat-Based Radiomics for Predicting Visceral Pleural Invasion in Subpleural Nodules with Solid Component on Low-Dose CT: A Multicenter Study.Diagnostics (Basel, Switzerland) · 2026Article
- A radiomics nomogram integrated with radiological features for preoperative prediction of lung nodule invasiveness: comparison with Lung-RADS.Translational lung cancer research · 2026Article
- The value of an integrated multi-omics model in the diagnosis of benign and malignant pulmonary nodules.Translational cancer research · 2026Article
- Development and validation of a nomogram for preoperative prediction of the invasiveness of stage T1 lung adenocarcinoma utilizing AI-driven radiomics.BMC cancer · 2026Article
- Evaluation of the efficacy of different lung biopsy modalities in solitary pulmonary nodules.Frontiers in bioengineering and biotechnology · 2026Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of invasive lung adenocarcinoma.Translational lung cancer research · 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
- CT-based radiomics integrated model for brain metastases in stage III/IV ALK-positive lung adenocarcinoma patients.Frontiers in oncology · 2025Article
- Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features.Medical sciences (Basel, Switzerland) · 2024Article
- Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules.Frontiers in oncology · 2024Article
- Classification of nasal polyps and inverted papillomas using CT-based radiomics.Insights into imaging · 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
- Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features.Scientific reports · 2023Article
- Current challenges in adopting machine learning to critical care and emergency medicine.Clinical and experimental emergency medicine · 2023Article
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Abstract
This study aims to predict the histological invasiveness of pulmonary adenocarcinoma spectrum manifesting with subsolid nodules ≦ 3 cm using the preoperative CT-based radiomic approach. A total of 186 patients with 203 SSNs confirmed with surgically pathologic proof were retrospectively reviewed from February 2016 to March 2020 for training cohort modeling. The validation cohort included 50 subjects with 57 SSNs confirmed with surgically pathologic proof from April 2020 to August 2020. CT-based radiomic features were extracted using an open-source software with 3D nodular volume segmentation manually. The association between CT-based conventional features/selected radiomic features and histological invasiveness of pulmonary adenocarcinoma status were analyzed. Diagnostic models were built using conventional CT features, selected radiomic CT features and experienced radiologists. In addition, we compared diagnostic performance between radiomic CT feature, conventional CT features and experienced radiologists. In the training cohort of 203 SSNs, there were 106 invasive lesions and 97 pre-invasive lesions. Logistic analysis identified that a selected radiomic feature named GLCM_Entropy_log10 was the predictor for histological invasiveness of pulmonary adenocarcinoma spectrum (OR: 38.081, 95% CI 2.735-530.309, p = 0.007). The sensitivity and specificity for predicting histological invasiveness of pulmonary adenocarcinoma spectrum using the cutoff value of CT-based radiomic parameter (GLCM_Entropy_log10) were 84.8% and 79.2% respectively (area under curve, 0.878). The diagnostic model of CT-based radiomic feature was compared to those of conventional CT feature (morphologic and quantitative) and three experienced radiologists. The diagnostic performance of radiomic feature was similar to those of the quantitative CT feature (nodular size and solid component, both lung and mediastinal window) in prediction invasive pulmonary adenocarcinoma (IPA). The AUC value of CT radiomic feature was higher than those of conventional CT morphologic feature and three experienced radiologists. The c-statistic of the training cohort model was 0.878 (95% CI 0.831-0.925) and 0.923 (0.854-0.991) in the validation cohort. Calibration was good in both cohorts. The diagnostic performance of CT-based radiomic feature is not inferior to solid component (lung and mediastinal window) and nodular size for predicting invasiveness. CT-based radiomic feature and nomogram could help to differentiate IPA lesions from preinvasive lesions in the both independent training and validation cohorts. The nomogram may help clinicians with decision making in the management of subsolid nodules.
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