Establishment of a CT image radiomics-based prediction model for the differential diagnosis of silicosis and tuberculosis nodules

Jing Liu; Min LI; Rongrong Liu; Yi Zhu; Guangqiang Chen; Xiaobo LI; Chen Geng; Jinjin Wang; Qixian Gao; Haiyan Heng

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Establishment of a CT image radiomics-based prediction model for the differential diagnosis of silicosis and tuberculosis nodules

VernacularTitle: 基于CT图像放射组学的矽肺和肺结核结节鉴别诊断预测模型的建立
Author: Jing LIU ¹ ; Min LI ¹ ; Rongrong LIU ¹ ; Yi ZHU ¹ ; Guangqiang CHEN ² ; Xiaobo LI ³ ; Chen GENG ⁴ ; Jinjin WANG ² ; Qixian GAO ² ; Haiyan HENG ²
Author Information

1. The Affiliated Infectious Diseases Hospital of Soochow University, The Fifth People's Hospital of Suzhou, 215000 Suzhou, China
2. The Second Affiliated Hospital of Soochow University, 215000 Suzhou, China
3. GE Healthcare (Shanghai) Co., Ltd, 200000 Shanghai, China
4. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, 215000 Suzhou, China
Publication Type:Journal Article
Keywords: Silicosis; Tuberculosis; Computor tomography; Random forest; Radiomics
From: Chinese Journal of Industrial Hygiene and Occupational Diseases 2019;37(9):707-710
CountryChina
Language:Chinese
Abstract: Objective:To establish a CT image radiomics-based prediction model for the differential diagnosis of silicosis and tuberculosis nodules.
Methods:A total of 53 patients with silicosis and 89 patients with tuberculosis who underwent routine CT scans in Suzhou Fifth People's Hospital from January to August, 2018 were enrolled in this study. AK/ITK software was used to segment the images to obtain 139 silicosis lesions and 119 tuberculosis lesions. For each lesion image, 396 features were extracted, and feature dimension reduction was applied to select the most characteristic feature subset. Support vector machine (SVM) , feedforward back propagation neural network (FNN-BP) , and random forest (RF) were implemented using R software (Rstudio V1.1.463) , and the algorithm that achieved the largest area under of the receiver operating characteristic (ROC) curve (AUC) was selected as the final prediction model.
Results:RF was the best prediction model for the differential diagnosis of silicosis and tuberculosis nodules, with an accuracy of 83.1%, a sensitivity of 0.76, a specificity of 0.9, and an AUC of 0.917 (95% confidence interval: 0.8431-0.9758) . RF had a significantly larger AUC than SVM and FNN-BP (P<0.05) .
Conclusion:CT image-based RF prediction model can be used to differentially diagnose silicosis and tuberculosis nodules.