CT-based whole lung radiomics nomogram for identification of PRISm from non-COPD subjects

• Published in: Respiratory research Vol. 25; no. 1; pp. 329 - 12
• Main Authors: Zhou, TaoHu, Guan, Yu, Lin, XiaoQing, Zhou, XiuXiu, Mao, Liang, Ma, YanQing, Fan, Bing, Li, Jie, Liu, ShiYuan, Fan, Li
• Format: Journal Article
• Language: English
• Published: London BioMed Central 03.09.2024; BioMed Central Ltd; BMC
• Subjects: Aged; Algorithms; Cancer; Cohort Studies; Computed tomography; CT imaging; Diagnosis; Diagnostic imaging; Female; Humans; Lung - diagnostic imaging; Lung cancer; Lung diseases, Obstructive; Male; Medicine; Medicine & Public Health; Middle Aged; Nomogram; Nomograms; Pneumology/Respiratory System; PRISm; Pulmonary Disease, Chronic Obstructive - diagnostic imaging; Radiomics; Spirometry - methods; Tomography, X-Ray Computed - methods; China; Taiwan; Computed tomography; PRISm; Nomogram; Radiomics
• ISSN: 1465-993X; 1465-9921; 1465-993X
• DOI: 10.1186/s12931-024-02964-2

Background Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening. Methods Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts. Results The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts ( p  < 0.001). The radiomics nomogram incorporating independent predicting factors (radiomics signature, age, BMI, and gender) well discriminated PRISm from non-COPD subjects compared with clinical model or radiomics signature alone for training cohort (AUC 0.787 vs. 0.675 vs. 0.778), internal (AUC 0.773 vs. 0.682 vs. 0.767) and external validation cohorts (AUC 0.702 vs. 0.610 vs. 0.699). Decision curve analysis suggested that our constructed radiomics nomogram outperformed clinical model. Conclusions The CT-based whole lung radiomics nomogram could identify PRISm to help decision-making in clinic. Key message What is already known on this topic Identifying PRISm subjects among non-COPD subjects, especially in the context of large-scale CT lung cancer screening, is currently a challenge. What this study adds In this retrospective, and multicentric study that included 1481 subjects, radiomics nomogram developed by integrating radiomics signature and clinical features achieved good performance for the identification of PRISm, with AUC of 0.787, 0.773 and 0.702 in training, internal and external validation cohort. How this study might affect research, practice or policy Radiomics nomogram, as a promising tool for identifying the PRISm from non-COPD subjects, hold great potential for guiding timely treatment and showing the added value of chest CT to evaluate the lung function status besides the morphological evaluation, especially during large-scale CT lung cancer screening.