ArticleEuropean journal of nuclear medicine and molecular imaging2024
PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06829147 (Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images), which is not on this map. Cited by 7 papers.
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Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images
Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
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- Enhancing the intraoperative identification of high-grade patterns in invasive lung adenocarcinoma via radiomics.Translational lung cancer research · 2025Article
- Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines.Insights into imaging · 2025Review
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15 authors at 8 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeNo consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.
methodsA total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.
resultsThe DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).
conclusionThe DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.
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