Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma

作者：Yong Chen, Jun Wu, Jie You, Mingjun Gao, Shichun Lu, Chao Sun, Yusheng Shu, Xiaolin Wang · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17177 · 被引用次数：9 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Cholangiocarcinoma and Gallbladder Cancer Studies

BACKGROUND: The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement. PURPOSE: To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD. METHODS: We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA). RESULTS: In total, nine radiomics features were ...