ArticleEuropean radiology2021
The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.BMC pulmonary medicine · 2024Pooled it
- Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023.Frontiers in oncology · 2024Pooled it
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled it
- Machine learning models based on tumoral, 4 mm-peritumoral, and combined radiomic features for evaluating the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.World journal of surgical oncology · 2026Article
- CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms.Insights into imaging · 2026Article
- MRI-based habitat radiomics for predicting WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Frontiers in oncology · 2026Article
- Contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics for identifying malignancy in pulmonary ground-glass nodules.Journal of thoracic disease · 2025Article
- Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory.NPJ digital medicine · 2025Article
- Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.Scientific reports · 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
- Radiomics of Vascular Structures in Pulmonary Ground-glass Nodules: A Predictor of InvasivenessCurrent medical imaging · 2025Article
- Clinical value of intratumoral and peritumoral CT radiomics models for discriminating benign and malignant parotid gland tumors.Frontiers in oncology · 2025Article
- CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.Translational lung cancer research · 2024Article
- Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50.Journal of thoracic disease · 2024Article
- Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study.Journal of cardiothoracic surgery · 2024Article
- The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging.Sensors (Basel, Switzerland) · 2024Article
- Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort.Diagnostics (Basel, Switzerland) · 2024Review
- Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era.Frontiers in bioengineering and biotechnology · 2024Article
- Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.Quantitative imaging in medicine and surgery · 2023Article
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8 authors.
Funding
Abstract
objectivesTo evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).
methodsFor this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method.
resultsThe radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423).
conclusionsThe radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region. KEY POINTS: • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.