ArticleiScience2024
A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multi-component radiological model based on intratumoral CT threshold segmentation for predicting visceral pleural invasion in lung adenocarcinoma ≤ 30 mm.Insights into imaging · 2026Article
- Preoperative prediction of visceral pleural invasion in clinical stage T1 invasive lung adenocarcinoma using intratumoral and peritumoral radiomics: a multicenter development, validation, and comparison study.BMC medical imaging · 2026Article
- CT predictors of visceral pleural invasion in subsolid nodular pulmonary adenocarcinoma: differences between direct and indirect tumor-pleura contact.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Observational
- Integrated predictive model for visceral pleural invasion in small NSCLC with high clinical utility.NPJ precision oncology · 2026Article
- Predicting visceral pleural invasion in invasive adenocarcinoma with a maximum diameter ≤ 3 cm based onEuropean journal of nuclear medicine and molecular imaging · 2026Article
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- Attention mechanism-based habitat analysis for predicting pleural invasion and prognosis of pulmonary nodules.Translational lung cancer research · 2025Article
- Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.Journal of cardiothoracic surgery · 2025Article
- Prognostic significance of visceral pleural changes in stage IA lung adenocarcinoma: a retrospective study.Frontiers in oncology · 2025Article
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Development and validation of a nomogram for predicting visceral pleural invasion in patients with early-stage non-small cell lung cancer.Translational lung cancer research · 2024Article
- Using the length of pleural tag to predetermine pleural invasion by lung adenocarcinomas.Frontiers in oncology · 2024Article
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8 authors.
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Abstract
Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.
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