高分辨率CT影像组学分类诊断类风湿关节炎相关间质性肺疾病的效能

柳洪亚; 祝洁; 刘晨; 钟兵; 敬洋; 邹庆华

doi:10.16016/j.2097-0927.202401024

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高分辨率CT影像组学分类诊断类风湿关节炎相关间质性肺疾病的效能

临床医学 | 更新时间：2026-01-09

- 高分辨率CT影像组学分类诊断类风湿关节炎相关间质性肺疾病的效能
- Efficacy of high-resolution CT imaging radiomics classification for diagnosis of rheumatoid arthritis-associated interstitial lung disease
- 陆军军医大学学报 2024年46卷第8期页码：878-885
- 作者机构：
  
  1.400038 重庆，陆军军医大学(第三军医大学)第一附属医院中医与风湿免疫科
  2.400038 重庆，陆军军医大学(第三军医大学)第一附属医院放射科
  3.100192 北京，慧影医疗科技(北京)股份有限公司AI创新中心
- 作者简介：
  
  [邹庆华，E-mail: zouqinghua318@tmmu.edu.cnZOU Qinghua, E-mail: zouqinghua318@tmmu.edu.cn
- 基金信息：
  
  陆军军医大学优秀人才库重点扶持对象个性化培养(XA-2019-505-404);重庆市科卫联合中医药科研项目重点项目(2024ZYZD007)
- DOI：10.16016/j.2097-0927.202401024
  中图分类号： R563.13;R593.22;R814.42
- 收稿：2024-01-02，
  
  修回：2024-02-01，
  
  纸质出版：2024-04-30
- 稿件说明：
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柳洪亚, 祝洁, 刘晨, 等. 高分辨率CT影像组学分类诊断类风湿关节炎相关间质性肺疾病的效能[J]. 陆军军医大学学报, 2024,46(8):878-885.

Hongya LIU, Jie ZHU, Chen LIU, et al. Efficacy of high-resolution CT imaging radiomics classification for diagnosis of rheumatoid arthritis-associated interstitial lung disease[J]. Journal of Army Medical University, 2024, 46(8): 878-885.
柳洪亚, 祝洁, 刘晨, 等. 高分辨率CT影像组学分类诊断类风湿关节炎相关间质性肺疾病的效能[J]. 陆军军医大学学报, 2024,46(8):878-885. DOI： 10.16016/j.2097-0927.202401024.

Hongya LIU, Jie ZHU, Chen LIU, et al. Efficacy of high-resolution CT imaging radiomics classification for diagnosis of rheumatoid arthritis-associated interstitial lung disease[J]. Journal of Army Medical University, 2024, 46(8): 878-885. DOI： 10.16016/j.2097-0927.202401024.

摘要

目的

探究高分辨率CT (high-resolution computed tomography

HRCT)影像组学用于分类诊断类风湿关节炎相关间质性肺疾病(rheumatoid arthritis associated interstitial lung disease

RA-ILD)的效能。

方法

筛选陆军军医大学第一附属医院中医与风湿免疫科2019年1月至2023年7月收治的RA-ILD患者病例261例

其中寻常型间质性肺炎(usual interstitial pneumonia

UIP)143例

非特异性间质性肺炎(nonspecific interstitial pneumonia

NSIP)118例

所有患者行HRCT

在HRCT图像上应用U-net深度学习肺分割模型进行自动肺分割

从每个肺分割中提取1 688个影像组学特征。逐级使用方差阈值法、单变量特征选择法、最小绝对收缩和选择算子(least absolute shrinkage and selection operator

LASSO)进行特征降维

采用多种机器学习算法构建RA-ILD分类诊断影像组学模型。分别采用受试者工作特性(receiver operating characteristic

ROC)曲线和曲线下面积(area under curve

AUC)比较各模型的诊断价值

同时对模型的准确度、敏感度和特异度进行评估。

结果

从RA-ILD患者HRCT图像中通过特征筛选法最终选定18个最佳特征。在训练集和测试集中UIP和NSIP的放射评分(radiomics score

Radscore)分布差异有统计学意义(

0.01)。在5种影像组学模型中

支持向量机(the support vector machine

SVM)算法的训练集AUC为0.943(95%

：0.916~0.966

灵敏度和特异度分别为0.787、0.912)

测试集AUC为0.909(95%

：0.849~0.969;灵敏度和特异度分别为0.625、0.897)。

结论

本研究构建的RA-ILD分类诊断模型性能良好

且基于SVM算法构建的模型在分类诊断RA-ILD中显示最佳潜能。

Abstract

Objective

To investigate the efficacy of high-resolution computed tomography (HRCT) imaging radiomics for the classification and diagnosis of rheumatoid arthritis associated interstitial lung disease (RA-ILD).

Methods

A total of 261 patients diagnosed with RA-ILD admitted in our hospital from January 2019 to July 2023 were recruited in this study.There were 143 cases of usual interstitial pneumonia (UIP) and 118 cases of nonspecific interstitial pneumonia (NSIP).All the patients underwent HRCT.A U-net deep learning lung segmentation model was applied to obtain HRCT images for automatic lung segmentation

and 1 688 imaging histologic features were extracted from each lung segmentation.Variance thresholding

univariate feature selection

and least absolute shrinkage and selection operator (LASSO) were used for feature dimensionality reduction step by step

and various machine learning algorithms were conducted to construct the RA-ILD diagnostic histology model.The diagnostic value of each model was compared using receiver operating characteristic (ROC) curve and area under curve (AUC)

and the accuracy

sensitivity and specificity of the models were evaluated.

Results

Feature screening finally identified 18 best features from the HRCT images of RA-ILD patients.Statistical difference was found in the distribution of Radiomics score (Radscore) between the UIP and NSIP patients in both the training and test sets (

0.01).Among the 5 imaging histology models

the support vector machine (SVM) algorithm had an AUC value of 0.943(95%

: 0.916~0.966)

a sensitivity of 0.787 and a specificity of 0.912

respectively for the training set

and an AUC value of 0.909(95%

: 0.849~0.969)

a sensitivity of 0.625 and a specificity of 0.897

respectively for the testing set.

Conclusion

Our constructed RA-ILD classification and diagnosis model performs well

and the model based on SVM algorithm shows the best potential in classifying and diagnosing RA-ILD.

关键词

Keywords

references

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