医学
阶段(地层学)
一致性
危险系数
比例危险模型
肺癌
内科学
磨玻璃样改变
分类
生存分析
总体生存率
肿瘤科
癌症
放射科
人工智能
腺癌
置信区间
古生物学
生物
计算机科学
作者
Jiajun Deng,Mengmeng Zhao,Tingting Wang,Yunlang She,Junqi Wu,E Haoran,Jiani Gao,Xiwen Sun,Gening Jiang,Yuming Zhu,Dong Xie,Chang Chen
出处
期刊:Lung Cancer
[Elsevier BV]
日期:2020-04-28
卷期号:145: 33-39
被引量:19
标识
DOI:10.1016/j.lungcan.2020.04.028
摘要
Objectives We evaluated the prognostic impact of the presence of ground glass opacity (GGO) component and compared a modified clinical T categorization (cTm) with the current 8th classification (cT8) for survival prediction in Chinese patients with clinical stage I non–small cell lung cancer (NSCLC). Methods According to cTm and cT8 classifications, we retrospectively evaluated 1461 patients with part-solid or pure-solid lesions. The recurrence-free survival (RFS) and overall survival (OS) were analyzed by Kaplan-Meier method and Cox proportional hazard model. The concordance index (C- index), reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were performed to estimate reclassification net benefits of cTm for survival prediction. Results The cT8 classification clearly stratifies the survival outcomes in solid tumors but not in part-solid tumors. The presence of GGO components was an independent prognostic factor for both RFS and OS (p < 0.001), indicating a better outcome in each clinical T stage. The C-index was significantly improved from 0.650 to 0.730 for RFS (p < 0.001) and 0.647 to 0.730 for OS (p < 0.001) after reclassifying by cTm categorization. The DCA, NRI (RFS: 0.342, OS: 0.302), and IDI (RFS: 0.070, OS: 0.054) demonstrated that the cTm classification provided more net benefit in the survival prediction compared with the current cT8 classification. Conclusions The current cT8 classification may not be appropriate for part-solid lesions because the presence of GGO components is associated with excellent prognosis despite clinical stage. Also, the cTm classification for part-solid lesions showed an improvement in survival prediction.
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