ArticleEuropean radiology2019
Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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Who cites it
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- A computerized tomography-based radiomic model for assessing the invasiveness of lung adenocarcinoma manifesting as ground-glass opacity nodules.Respiratory research · 2022Trial
- Predictive value of CT target scan-based radiomics and clinical features for patients with chronic obstructive pulmonary disease combined with malignant pulmonary nodules.Biomedical engineering online · 2026Article
- A CT-based multi-scale fusion model with SHAP interpretation for preoperative differentiation between lung adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting Invasiveness of Lung Adenocarcinoma from Chest CT with Few-shot Vision-Language Ternary Classification Model.NPJ digital medicine · 2025Article
- Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.Insights into imaging · 2025Article
- Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules.Scientific reports · 2025Article
- Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma.Discover oncology · 2025Article
- Application of Radiomics in Predicting the Prognosis of Medulloblastoma in Children.Children (Basel, Switzerland) · 2025Article
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 2025Article
- Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.Journal of cardiothoracic surgery · 2025Article
- Assessment of intratumor heterogeneity for preoperatively predicting the invasiveness of pulmonary adenocarcinomas manifesting as pure ground-glass nodules.Quantitative imaging in medicine and surgery · 2025Article
- CT-based habitat radiomics for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma from invasive adenocarcinoma manifesting as ground-glass nodules: a multicenter study.Frontiers in oncology · 2025Article
- Preoperative computed tomography-guided localization of pulmonary ground‑glass nodules: a comparison of conventional and soft hook‑wires.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2024Article
- 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
- Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizingBMC cancer · 2024Article
- Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.BMC cancer · 2024Article
- Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
41 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
objectivesTo identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.
methodsThis retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.
results355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.
conclusionsThe radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma. KEY POINTS: • The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.
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