ArticleEuropean radiology2021
Determining the invasiveness of ground-glass nodules using a 3D multi-task network.
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 19 papers.
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19 citing papers in PubMed, 34 citations in OpenAlex.
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 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
- 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
- Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.BMC medical imaging · 2025Article
- New Perspectives on Lung Cancer Screening and Artificial Intelligence.Life (Basel, Switzerland) · 2025Review
- 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
- Determining the status of tertiary lymphoid structures in invasive pulmonary adenocarcinoma based on chest CT radiomic features.Insights into imaging · 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
- A Novel Scale for Diagnosis of Pulmonary Ground-Glass Nodules: A Multicenter and Ambispective Cohort Study.The clinical respiratory journal · 2024Article
- Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.BMC cancer · 2024Article
- 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
- An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT.NPJ digital medicine · 2023Article
- Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.European radiology · 2022Article
- [Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022Article
- [Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022Article
- A deep learning approach for anterior cruciate ligament rupture localization on knee MR images.Frontiers in bioengineering and biotechnology · 2022Article
- Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules.Frontiers in oncology · 2021Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesThe aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network.
methodsWe propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results.
resultsIn the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively.
conclusionsThe proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods. KEY POINTS: • The proposed multi-task model has achieved good classification results for the invasiveness of GGNs. • The proposed network includes a classification and segmentation branch to learn global and regional features, respectively. • The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods.
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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.