国际肿瘤学杂志

国际肿瘤学杂志 ›› 2025, Vol. 52 ›› Issue (7): 414-418.doi: 10.3760/cma.j.cn371439-20240607-00072

• 论著 • 上一篇    下一篇

高分辨率CT影像特征联合Ki-67表达对低分化浸润性非黏液肺腺癌的预测价值

张贝1, 霍阿伟2, 康彤2, 杨博2()   

  1. 1陕西省肿瘤医院CT室，西安 710061
    2中国人民解放军中部战区总医院放射诊断科，武汉 430070
  • 收稿日期:2024-06-07 修回日期:2025-03-12 出版日期:2025-07-08 发布日期:2025-07-23
  • 通讯作者: 杨博 E-mail:49007877@qq.com
  • 基金资助:
    陕西省科技厅社会发展一般项目(2024SF-YBXM-105)

Exploration of the predictive value of high-resolution CT imaging features combined with Ki-67 expression for poorly differentiated invasive non-mucinous lung adenocarcinoma

Zhang Bei1, Huo Awei2, Kang Tong2, Yang Bo2()   

  1. 1Computer Tomography Room of Shaanxi Provincial Cancer Hospital，Xi'an 710061，China
    2Department of Radiology，General Hospital of Central Theater Command of the Chinese People's Liberation Army，Wuhan 430070，China
  • Received:2024-06-07 Revised:2025-03-12 Online:2025-07-08 Published:2025-07-23
  • Contact: Yang Bo E-mail:49007877@qq.com
  • Supported by:
    General Social Development Project of Shaanxi Science and Technology Department(2024SF-YBXM-105)

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摘要：

目的 探讨高分辨率CT（HRCT）影像特征和Ki-67表达水平对低分化浸润性非黏液肺腺癌（INMA）的预测价值，基于此构建列线图预测模型并进行验证。方法 回顾性纳入2020年7月至2023年10月于陕西省肿瘤医院经根治手术切除并取得病理组织学结果的INMA患者共210例，基于病灶分化程度分为高/中分化组（n=152）和低分化组（n=58），比较两组患者的一般临床资料、HRCT影像特征和Ki-67表达情况，采用多因素logistic回归分析低分化INMA的独立影响因素并构建列线图预测模型，通过受试者操作特征（ROC）曲线评估预测模型的诊断效能，使用一致性系数（C-index）和校准曲线对预测模型进行验证。结果 高/中分化组和低分化组INMA患者的年龄、部位、边缘、分叶、血管集束征、空洞差异均无统计学意义（均P>0.05），性别（χ2=6.65，P=0.010）、Ki-67表达（U=2.33，P=0.021）、结节大小（t=-3.34，P=0.010）、毛刺（χ2=5.22，P=0.022）、胸膜凹陷征（χ2=17.02，P<0.001）、支气管充气征（χ2=15.54，P<0.001）、结节类型（χ2=59.67，P<0.001）差异均有统计学意义。多因素分析显示，结节大小（OR=1.07，95%CI为1.01~1.14，P=0.025）、结节类型（OR=8.23，95%CI为3.04~22.32，P<0.001）与Ki-67表达（OR=1.07，95%CI为1.03~1.11，P<0.001）均是低分化INMA发生的独立影响因素。基于上述指标构建低分化INMA的列线图预测模型，ROC曲线分析显示，该模型预测低分化INMA发生的曲线下面积为0.893，敏感性、特异性分别为89.70%、77.60%；该模型C-index值为0.893；校准曲线显示，预测概率与实际发生概率均具有较好的一致性。结论 HRCT影像特征中的结节大小、结节类型与Ki-67表达均是INMA患者发生低分化的独立影响因素，基于上述指标构建的列线图预测模型具有良好的预测效能。

关键词: 肺腺癌, 体层摄影术，螺旋计算机, Ki-67抗原

Abstract:

Objective To investigate the predictive value of high-resolution CT （HRCT） imaging features and Ki-67 expression levels for poorly differentiated invasive non-mucinous lung adenocarcinoma （INMA），and to construct and validate a nomogram prediction model based on these factors. Methods A total of 210 INMA patients who underwent radical surgery at Shaanxi Provincial Cancer Hospital from July 2020 to October 2023 and obtained histopathological results were retrospectively included. Based on the degree of lesion differentiation，they were divided into well/moderately differentiated INMA group （n=152） and poorly differentiated INMA group （n=58）. The general clinical data，HRCT imaging features and Ki-67 expression of the two groups of patients were compared. Multivariate logistic regression analysis was used to analyze independent influencing factors for poorly differentiated INMA，and a nomogram prediction model was constructed. The diagnostic efficacy of the prediction model was evaluated by the receiver operator characteristic （ROC） curve，and the prediction model was validated by consistency index （C-index） and calibration curve. Results There were no statistically significant differences in terms of age，location，margin，lobulation，vascular convergence，and cavitation between the well/moderately differentiated INMA group and poorly differentiated INMA group （all P>0.05）. There were statistically significant differences in sex （χ2=6.65，P=0.010），Ki-67 expression （U=2.33，P=0.021），nodule size （t=-3.34，P=0.010），spiculation （χ2=5.22，P=0.022），pleural indentation （χ2=17.02，P<0.001），air bronchogram （χ2=15.54，P<0.001） and nodule type （χ2=59.67，P<0.001） between the two groups. Multivariate analysis showed that nodule size （OR=1.07，95%CI： 1.01-1.14，P=0.025），nodule type （OR=8.23，95%CI： 3.04-22.32，P<0.001） and Ki-67 expression （OR=1.07，95%CI： 1.03-1.11，P<0.001） were independent influencing factors for the occurrence of poorly differentiated INMA. A nomogram prediction model for poorly differentiated INMA was constructed based on the above indicators. ROC curve analysis showed that the area under curve of the prediction model to predict the occurrence of poorly differentiated INMA was 0.893，and the sensitivity and specificity were 89.70% and 77.60%，respectively. The C-index value of the model was 0.893. The calibration curve showed that the predicted probability was in good agreement with the actual probability. Conclusions Nodule size，nodule type in HRCT imaging features and Ki-67 expression are independent influencing factors for the occurrence of low differentiation in INMA patients. The nomogram prediction model constructed based on the above indicators has good predictive performance.

Key words: Adenocarcinoma of lung, Tomography，spiral computed, Ki-67 antigen