ArticleEuropean radiology2020
A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed, 108 citations in OpenAlex.
- Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.La Radiologia medica · 2026Article
- Comparison of radiomics handcrafted features and deep features inBMC cancer · 2026Article
- Pulmonary Functional Impact of Resecting Additional Ipsilateral Ground-Glass Opacities: A Retrospective Propensity Score-Matched Study.Thoracic cancer · 2026Article
- Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.BMC medical imaging · 2026Article
- FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS-MIA discrimination on pulmonary CT.Frontiers in medicine · 2026Article
- Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study.Insights into imaging · 2025Article
- Long-Term Prognosis of Resected Stage 1 Lung Adenocarcinoma With Radiomics, Regionality, and Histopathologic Correlation.CHEST pulmonary · 2025Article
- Echocardiography Combined With Radiomics and Deep Transfer Learning to Diagnose Hypertrophic Cardiomyopathy and Other Etiologies of Left Ventricular Hypertrophy: A Multicenter Study Comparing the Performance With Echocardiologists.Reviews in cardiovascular medicine · 2025Article
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
- Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.BMC pulmonary medicine · 2025Article
- Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.Scientific reports · 2025Article
- A novel composite model for distinguishing benign and malignant pulmonary nodules.Clinical and experimental medicine · 2025Article
- Successful Application of Artificial Intelligence-Assisted Analysis of Invasive Pulmonary Adenocarcinoma Less Than 6 mm in Size: A Case Report and Literature Review.The clinical respiratory journal · 2025Review
- 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
- Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.Journal of imaging informatics in medicine · 2024Article
- Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.BMC cancer · 2024Article
- Worldwide research landscape of artificial intelligence in lung disease: A scientometric study.Heliyon · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism.Frontiers in oncology · 2024Article
- Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules.Translational lung cancer research · 2023Article
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveTo develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.
methodsFirst, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs.
resultsThe new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6.
conclusionsThe study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm. KEY POINTS: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive 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.