医学
肺癌
内科学
病态的
肿瘤科
星团(航天器)
生存分析
回顾性队列研究
共识聚类
聚类分析
模糊聚类
树冠聚类算法
机器学习
计算机科学
程序设计语言
作者
Q. Tian,Shi-Yu Zhou,Y.-H. Qin,Yue Wu,Chao Qin,Hao Zhou,Jian Shi,Shaofeng Duan,Fred Feng
标识
DOI:10.1016/j.crad.2024.06.011
摘要
AIM This study aims to assess whether consensus clustering, based on CT radiomics from both intratumoral and peritumoral regions, can effectively stratify the risk of non-small cell lung cancer (NSCLC) patients and predict their postoperative recurrence-free survival (RFS). MATERIALS AND METHODS A retrospective analysis was conducted on the data of surgical patients diagnosed with NSCLC between December 2014 and April 2020. After preprocessing CT images, radiomic features were extracted from a 9 mm region encompassing both the tumor and its peritumoral area. Consensus clustering was utilized to analyze the radiomics features and categorize patients into distinct clusters. Comparison of the differences in clinical pathological characteristics was conducted among the clusters. Kaplan-Meier survival analysis was employed to investigate differences in survival among the clusters. RESULTS A total of 266 patients were included in this study, and consensus clustering identified three clusters (Cluster 1: n=111, Cluster 2: n=61, Cluster 3: n=94). Multiple clinical risk factors, including pathological TNM staging, programmed cell death ligand 1 (PD-L1), and epidermal growth factor receptor (EGFR) expression status, exhibit significant differences among the three clusters. Kaplan-Meier survival analysis demonstrated significant variations in RFS across the clusters (P<0.001). The 3-year cumulative recurrence-free survival rates were 76.5% (95% CI: 68.6-84.4) for Cluster 1, 45.9% (95% CI: 33.4-58.4) for Cluster 2, and 41.5% (95% CI: 31.6-51.5) for Cluster 3. CONCLUSIONS Consensus clustering of CT radiomics based on intratumoral and peritumoral regions can stratify the risk of postoperative recurrence in patients with NSCLC.
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