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Background: The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures.
Methods: We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established.
Results: (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve was close to the -line, and the decision curve was drawn in the range of 0.0-1.0.
Conclusion: We analyzed the risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.
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http://dx.doi.org/10.3389/fmed.2024.1403020 | DOI Listing |
Respirology
September 2025
Radiology Department, Huadong Hospital, Fudan University, Shanghai, China.
Background And Objective: Diagnosing pulmonary ground-glass nodules (GGNs) on chest CT imaging remains challenging in clinical practice. Moreover, different stages of GGNs may require different clinical treatments. Hence, we sought to predict the progressive state of pulmonary GGNs (absorption or persistence) for accurate clinical treatment and decision-making.
View Article and Find Full Text PDFEur J Med Chem
August 2025
Amity Institute of Pharmacy, Amity University Haryana, 122413, India. Electronic address:
Neurodegenerative diseases (NDs), including Alzheimer's, Huntington's, and Parkinson's disease, are associated with significant declines in cognitive function and mobility. The accumulation of misfolded proteins such as β-amyloid, tau, α-synuclein, and polyglutamates is a key factor in the progression of these conditions. Unfortunately, traditional small-molecule drugs face major obstacles in effectively targeting these proteins.
View Article and Find Full Text PDFEur J Radiol Open
December 2025
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, PR China.
Objective: To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN).
Methods: A retrospective analysis was conducted on clinical data and radiological images from 392 patients with lung adenocarcinoma at Binzhou Medical University Hospital between January 1, 2020 to May 31, 2023. All patients underwent preoperative thin-section chest CT scans and surgical resection.
BMC Cancer
September 2025
Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Zhongshan road No.222, Xigang District, Dalian, 116011, Liaoning Province, China.
Purpose: This study aimed to explore the correlation between the growth rate of resected ground glass nodule-featured lung adenocarcinomas (GGN-LUAD) and Ki-67, as well as various immune indexes, in patients with a follow-up period exceeding one year.
Materials And Methods: Sixty-seven tumors were divided into a growth group and a non-growth group. The volume doubling time (VDT) and mass doubling time (MDT) of the growth nodules were calculated.
Acad Radiol
August 2025
Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China (X.W., P.L., Y.L., R.Z., F.D., D.W.). Electronic address:
Rationale And Objectives: To develop and validate predictive models based on F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) radiomics and a clinical model for differentiating invasive adenocarcinoma (IAC) from non-invasive ground-glass nodules (GGNs) in early-stage lung cancer.
Materials And Methods: A total of 164 patients with GGNs histologically confirmed as part of the lung adenocarcinoma spectrum (including both invasive and non-invasive subtypes) who underwent preoperative F-FDG PET/CT and surgery. Radiomic features were extracted from PET and CT images.