Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease

Xiaopeng Jiang; Ningling Su; Shuai Quan; Linning E; Rui Li

doi:10.1016/j.acra.2023.01.038

Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease

Acad Radiol. 2023 Nov;30(11):2598-2605. doi: 10.1016/j.acra.2023.01.038. Epub 2023 Mar 2.

Authors

Xiaopeng Jiang¹, Ningling Su¹, Shuai Quan², Linning E³, Rui Li⁴

Affiliations

¹ Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China; Tongji Hospital, Tongji Medical College, Huazhong University, China.
² GE HealthCare China (Shanghai), Shanghai, 210000, China.
³ Affiliated Longhua People's Hospital, Southern Medical University (Longhua People's Hospital), Shenzhen, 518110, China.
⁴ Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China; Tongji Hospital, Tongji Medical College, Huazhong University, China. Electronic address: liruiyier@163.com.

PMID: 36868880
DOI: 10.1016/j.acra.2023.01.038

Abstract

Purpose: To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest.

Materials and methods: Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results. GAP I, II, and III have 137, 36, and 11 cases, respectively. The cases in GAP Ⅱ and Ⅲ were then combined into one group, and the two groups of patients were randomly divided into the training and testing groups with a 7:3 ratio. The radiomics features were extracted using AK software. Multivariate logistic regression analysis was then conducted to establish a radiomics model. A nomogram model was established on the basis of Rad-score and clinical factors (age and gender).

Results: For the radiomics model, four significant radiomics features were selected to construct the model and showed excellent ability to differentiate GAP I from GAP Ⅱ and Ⅲ in both the training group (the area under the curve [AUC] = 0.803, 95% confidence interval [CI]: 0.724-0.874) and testing group (AUC = 0.801, 95% CI:0.663-0.912). The nomogram model that combined clinical factors and radiomics features improved higher accuracy of both training (88.4% vs. 82.1%) and testing (83.3% vs. 79.2%).

Conclusion: The disease severity assessment of patients with CTD-ILD can be evaluated by applying the radiomics method based on CT images. The nomogram model demonstrates better performance for predicting the GAP staging.

Keywords: computed tomography; connective tissue disease; gender–age–physiology staging; interstitial lung disease; radiomics.