Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis

作者：Yantao Yang, Libin Zhang, Han Wang, Jie Zhao, Jun Liu, Yun Chen, Jiagui Lu, Yaowu Duan, Huilian Hu, Hao Peng, Lianhua Ye · 发表于：BMC Medical Imaging · 年份：2024 · DOI：10.1186/s12880-024-01313-5 · 被引用次数：11 · 研究领域：Lung Cancer Diagnosis and Treatment、Lung Cancer Treatments and Mutations、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE: Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS: A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curv...