基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值

董浩; 邱勇刚; 汪鑫斌; 杨俊杰; 楼存诚; 叶晓丹

doi:10.19401/j.cnki.1007-3639.2023.08.005

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基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值

论著 | 更新时间：2025-12-31

- 基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值
- Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma
- 中国癌症杂志 2023年33卷第8期页码：768-775
- 作者机构：
  
  1. 杭州市萧山区第一人民医院放射科，浙江杭州 311200
  2. 杭州市萧山区第一人民医院病理科，浙江杭州 311200
  3. 复旦大学附属中山医院放射科，上海 200032
  4. 上海医学影像研究所，上海 200032
  5. 复旦大学附属中山医院肿瘤中心，上海 200032
- 作者简介：
  
  [ "董浩（ORCID: 0000-0003-3345-5506），硕士研究生在读。" ]
  叶晓丹（ORCID: 0000-0003-3059-2740），主任医师。
- 基金信息：
  
  国家自然科学基金(81571629);国家自然科学基金(82071990);上海市科学技术委员会项目(19411965200);浙江省医药卫生科技计划项目(2023RC252);杭州市农业与社会发展科研引导项目(20220919Y078);杭州市萧山区社会发展重大科技计划政策引导项目(2021309)
- DOI：10.19401/j.cnki.1007-3639.2023.08.005
  中图分类号： R734.2
- 收稿：2023-02-03，
  
  修回：2023-05-12，
  
  纸质出版：2023-08-30
- 稿件说明：
移动端阅览
董浩, 邱勇刚, 汪鑫斌, 等. 基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值[J]. 中国癌症杂志, 2023,33(8):768-775.

Hao DONG, Yonggang QIU, Xinbin WANG, et al. Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma[J]. China Oncology, 2023, 33(8): 768-775.
董浩, 邱勇刚, 汪鑫斌, 等. 基于高分辨率CT征象建立logistic回归模型对IA期肺腺癌高级别模式的预测价值[J]. 中国癌症杂志, 2023,33(8):768-775. DOI： 10.19401/j.cnki.1007-3639.2023.08.005.

Hao DONG, Yonggang QIU, Xinbin WANG, et al. Predictive value of logistic regression model based on high-resolution CT signs for high-grade pattern in stage ⅠA lung adenocarcinoma[J]. China Oncology, 2023, 33(8): 768-775. DOI： 10.19401/j.cnki.1007-3639.2023.08.005.

摘要

背景与目的：

研究表明，当存在高级别组织学模式（微乳头状和实体模式）时，肺腺癌患者的预后明显较差，往往需要更积极的治疗方式，术前确定浸润性肺腺癌中是否存在任何高级别模式（high-grade pattern，HGP）可以帮助预测患者的预后并确定治疗策略。本研究旨在建立基于高分辨率计算机体层成像（computed tomography，CT）征象的logistic回归模型预测ⅠA期肺腺癌的HGP。

方法：

回顾性分析经病理学检查证实为ⅠA期肺腺癌的443例患者（445个病灶）的临床、病理学及影像学资料。根据病理学检查结果有无HGP将445个病灶分成两组：HGP组（

=88个）和非HGP（non-HGP，n-HGP）组（

=357个）。患者的临床病理学资料包括年龄、性别、吸烟史、肿瘤位置、分期及病理生长方式等。CT 影像学上观察病灶大小、密度、形状、毛刺征、分叶征、空泡征、空气支气管征、胸膜凹陷征等。两组间定量参数比较采用Mann-Whitney

检验，计数资料采用

检验或Fisher确切概率法。采用单因素结合多因素logistic回归分析筛选独立预测因子，并根据多因素logistic回归分析结果分别

构建临床模型、CT模型及临床-CT模型，模型间诊断效能的比较采用Delong检验。

结果：

单因素分析中HGP组与n-HGP组之间年龄、性别、吸烟史、肿瘤大小、密度、形状、毛刺、分叶征、胸膜牵拉差异有统计学意义（

0.05），多因素logistic 回归分析结果显示肿瘤大小（

= 0.040；OR = 1.063，95% CI：1.003 ~ 1.126）、密度（

0.001；OR = 8.249，95% CI：4.244 ~ 16.034）、分叶征（

= 0.001；OR = 3.101，95% CI：1.598 ~ 6.021）是HGP的独立预测因素，临床模型、CT模型、临床-CT模型预测HGP的曲线下面积（area under curve，AUC）值分别为0.634、0.838及0.834。

结论：

肿瘤大小、密度与分叶征是ⅠA期肺腺癌HGP的独立预测因子。基于高分辨率CT征象的logistic回归模型具有较好的诊断效能，可以为临床诊断及制订外科治疗方案提供一定的参考依据。

Abstract

Background and purpose:

Studies have shown that when high-grade histological patterns (micropapillary and solid patterns) are present

patients with lung adenocarcinoma have a significantly poorer prognosis and often require more aggressive treatment modalities

and preoperative determination of the presence of any high-grade patterns (HGP) in invasive lung adenocarcinoma can help predict patient prognosis and determine treatment strategies. The aim of the study was to establish a logistic regression model based on high-resolution CT signs to predict the HGP of stage ⅠA lung adenocarcinoma.

Methods:

The clinical

pathological and imaging data of 443 patients (445 lesions) with stage ⅠA lung adenocarcinoma confirmed by pathology diagnosis from First People's Hospital of Xiaoshan District (Oct. 2018 to Mar. 2021) and Zhongshan Hospital of Fudan University (Jan. 2018 to Dec. 2020) were retrospectively analyzed. The 445 lesions were divided into two groups according to the presence or absence of HGP in pathological findings: HGP (

=88) and non-HGP (n-HGP) (

=357). The clinical and pathological data of the patients included age

gender

smoking history

tumor location

stage and pathological growth pattern. On CT imaging

the size

density

shape

burr sign

lobulation sign

vacuole sign

air bronchus sign and pleural depression sign we

re observed. Mann-Whitney

test was used to compare quantitative parameters between the two groups

and

test or Fisher's exact test was used for enumeration of data. The independent predictors were screened by univariate combined with multivariate logistic regression analysis

and the clinical model

CT model and clinical-CT model were constructed according to the results of multivariate logistic regression analysis. DeLong test was used to compare the diagnostic efficacy between models.

Results:

In the univariate analysis

there were significant differences in age

gender

smoking history

tumor size

density

shape

burr

lobulation sign and pleural traction between the HGP group and the n-HGP group (

0.05). Multivariate logistic regression analysis showed tumor size (

=0.04; OR=1.063

95% CI: 1.003-1.126)

density (

0.001; OR=8.249

95% CI: 4.244-16.034)

lobulation sign (

=0.001; OR=3.101

95% CI: 1.598-6.021) were independent predictors of HGP

and the area under curve (AUC) values of clinical model

CT model and clinical-CT model for predicting HGP were 0.634

0.838 and 0.834

respectively.

Conclusion:

Tumor size

density and lobulation sign are independent predictors of HGP in stage ⅠA lung adenocarcinoma. The logistic regression model based on high-resolution CT signs has good diagnostic performance and can provide a certain reference for clinical diagnosis and surgical treatment.

关键词

Keywords

references

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