ArticleFrontiers in oncology2021
Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules.
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 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- [Advances in the Growth Risk Assessment and Precision Management of Pulmonary Subsolid Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
- Natural course of lung adenocarcinoma manifesting as ground-glass nodules: invasiveness assessment based on growth evaluation.Translational lung cancer research · 2025Article
- Diagnostic accuracy of deep learning for the invasiveness assessment of ground-glass nodules with fine segmentation: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2025Article
- Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-pointCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.Frontiers in oncology · 2023Article
- Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules.Frontiers in oncology · 2023Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.Frontiers in oncology · 2022Article
- Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ternary Classification Models.Technology in cancer research & treatmentArticle
- Preoperative Ternary Classification of Pulmonary Ground-Glass Nodules (AIS/MIA/IAC): ResNet-10 Outperforms Radiomics and Clinicoradiographic Models in Multicenter Study.Technology in cancer research & treatmentArticle
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5 authors.
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Abstract
introductionTumors are continuously evolving biological systems which can be monitored by medical imaging. Previous studies only focus on single timepoint images, whether the performance could be further improved by using serial noncontrast CT imaging obtained during nodule follow-up management remains unclear. In this study, we evaluated DL model for predicting tumor invasiveness of GGNs through analyzing time series CT images.
methodsA total of 168 pathologically confirmed GGN cases (48 noninvasive lesions and 120 invasive lesions) were retrospectively collected and randomly assigned to the development dataset (
resultsThe area under the curve (AUC) of the baseline model, models 1, 2, 3, and 4 were 0.562 [(95% confidence interval (C)], 0.406~0.710), 0.693 (95% CI, 0.538-0.822), 0.787 (95% CI, 0.639-0.895), 0.727 (95% CI, 0.573-0.849), and 0.811 (95% CI, 0.667-0.912) in the independent testing dataset, respectively. The results indicated that the peritumor region had potential to contribute to tumor invasiveness prediction, and the model performance was further improved by integrating imaging scans at multiple timepoints. Furthermore, the combined model showed best discrimination ability, with AUC, sensitivity, specificity, and accuracy achieving 0.831 (95% CI, 0.690-0.926), 86.7%, 73.3%, and 82.2%, respectively.
conclusionThe DL model integrating full ROIs from serial CT images shows improved predictive performance in differentiating noninvasive from invasive GGNs than the model using only baseline CT images, which could benefit the clinical management of GGNs.
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