CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Abstract

Background Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. Methods This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. Results Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869–0.906], 0.874 (95%CI 0.844–0.904) and 0.846 (95%CI 0.822–0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. Conclusions The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients

“…More importantly, CT image-based radiomics can objectively, reliably and quantitatively assess images, without being affected by inter-reader variability. The application of radiomics, especially whole lung radiomics, in COPD has been proven to be feasible [30] , not only in the diagnosis of COPD, but also predicting comorbidities of COPD and severity evaluation of COPD [31] , [32] . Among 1,218 radiomics features collected on CT images, fourteen radiomics features extracted on Laplacian of Gaussian (LoG) and wavelet transformed images, and one shape radiomics feature were obtained to be significant elements for constructing a radiomics model, and the AUCs were 0.82, 0.77, and 0.80 for three sets, separately.…”

“…To address this task, we used an automatic whole-lung algorithm for chest CT image segmentation. Previous research has demonstrated the predictive validity of fully automatic algorithm in COPD [18] , [19] , [20] .…”