亚实性肺结节浸润性腺癌风险列线图模型的建立  

A nomogram for predicting the risk of invasive adenocarcinoma in patients with a sub-solid nodule

作　　者：梁晴 陈书艳[2] LIANG Qing;CHEN Shu-yan(Department of General Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China;Department of Geriatrics, Xinhua Hospital Affiliated Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China)

机构地区：[1]上海交通大学医学院附属新华医院全科医学科,上海200092 [2]上海交通大学医学院附属新华医院老年医学科,上海200092

出　　处：《中国临床医学》2021年第5期879-884,共6页Chinese Journal of Clinical Medicine

摘　　要：目的:建立亚实性结节浸润性腺癌风险预测模型,为制订合适的亚实性结节临床诊疗策略提供参考。方法:收集2019年11月至2020年12月上海交通大学医学院附属新华医院收治的手术切除、病理为腺癌的亚实性结节患者221例,分析患者临床资料、血清肿瘤标志物及影像学资料中的17个变量,基于最小绝对收缩和选择算子(the least absolute shrinkage and selection operator,LASSO)算法筛选预测因子,绘制列线图并行内部验证。结果:221例亚实性结节患者中,非典型腺瘤样增生(atypical adenomatous hyperplasia,AAH)2例、原位腺癌(adenocarcinoma in situ,AIS)44例、微浸润腺癌(minimally invasive adenocarcinoma,MIA)102例、浸润性腺癌(invasive adenocarcinoma,IA)73例。基于LASSO算法筛选出5个预测因子进入列线图模型,包括结节直径、CT值、形状、分叶征、实性成分直径,最终列线图模型显示了较强的区分能力,通过内部校验后,预测风险与实际风险达到了较高的吻合度。结论:结节直径、CT值、形状、有无分叶征、实性成分直径有助于预测亚实性结节的浸润风险,为指导个体化治疗方案提供参考。Objective:To develop a model for predicting the risk of invasive pulmonary adenocarcinoma for sub-solid nodules,and provide a reference for the formulation of appropriate clinical diagnosis and treatment strategies for subsolid nodules.Methods:Totally,221 patients with SSN who underwent surgical resection with a definite postoperative pathology from November 2019 to December 2020 in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were included,patients’clinical data,serum tumor markers,and 17 parameters in radiological data were analyzed.The variables included in the model were screened by LASSO regression,then a risk evaluation nomogram model was established and verified.Results:Among the 221 patients with SSN,there were 2 atypical adenomatous hyperplasia(AAH),44 adenocarcinomas in situ(AIS),102 minimally invasive adenocarcinomas(MIA),and 73 invasive adenocarcinomas(IA).Five factors were selected by LASSO regression,including the maximum diameter of lung nodule,CT attenuation,shape,lobulation,and diameter of the solid component.The calibration curves for the probability of invasive pulmonary adenocarcinoma showed optimal agreement between the probability as predicted by the nomogram and the actual probability.Conclusions:The factors including the maximum diameter of lung nodule,CT attenuation,shape,lobulation,and diameter of the solid component might help to predict the risk of invasive pulmonary adenocarcinoma for sub-solid nodules and provide guidance for the subsequent personalized treatment.

关 键 词：亚实性结节 腺癌 预测模型 列线图 

分 类 号：R736[医药卫生—肿瘤]