ArticleTranslational lung cancer research2020
Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics.
Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- 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
- Evaluation of locoregional invasiveness of early lung adenocarcinoma manifesting as ground-glass nodules via [European journal of nuclear medicine and molecular imaging · 2025Trial
- Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.BMC medical imaging · 2026Article
- Super-resolution and habitat radiomics based computed tomography machine-learning model for prediction of lung invasive adenocarcinoma: a multi-centre study.Quantitative imaging in medicine and surgery · 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
- Imaging-based transformer model predicts early therapy response in advanced nasopharyngeal carcinoma: a dual-center study.Insights into imaging · 2025Article
- Deep learning for Three-Class Classification of ground-glass nodules on non-enhanced chest CT: A multicenter comparative study of CNN architectures.European journal of radiology open · 2025Article
- Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: a two-center comparative study.Translational lung cancer research · 2024Article
- CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules.European journal of radiology open · 2024Article
- Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.Journal of imaging informatics in medicine · 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
- Integrated whole-exome and bulk transcriptome sequencing delineates the dynamic evolution from preneoplasia to invasive lung adenocarcinoma featured with ground-glass nodules.Cancer medicine · 2024Article
- Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.European radiology · 2024Article
- A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Application value of a computer-aided diagnosis and management system for the detection of lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.Cancer medicine · 2023Article
- A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network.Quantitative imaging in medicine and surgery · 2023Article
- Artificial intelligence in lung cancer: current applications and perspectives.Japanese journal of radiology · 2023Review
- 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
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs.
methodsThis retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance.
resultsThe AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05).
conclusionsDeep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.
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