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

Abstract

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.

Keywords: Computed tomography; Nomogram; PRISm; Radiomics.

Fig. 1

Flowchart for the selection of the study population

Fig. 2

Diagnostic performance of the clinical factors model, radiomics signature, and radiomics nomogram was assessed and compared through ROC curves in the training (A), internal validation (B) and external validation (C) cohorts. ROC = receiver operating characteristics; AUC = area under the receiver operating characteristic curve

Fig. 3

Radiomics feature selection by using the least absolute shrinkage and selection operator (LASSO) logistic regression. (A) Selection of the tuning parameter (λ) in the LASSO model via 10-fold cross-validation based on minimum criteria. Binomial deviances from the LASSO regression cross-validation model are plotted as a function of log(λ). The y-axis shows binomial deviances and the lower x-axis the log(λ). Numbers along the upper x-axis indicate the average number of predictors. Red dots indicate average deviance values for each model with a given λ, and vertical bars through the red dots indicate the upper and lower values of the deviances. The vertical black lines define the optimal values of λ, where the model provides its best fit to the data. (B) The coefficients have been plotted vs. log(λ). (C) The 14 features with nonzero coefficients are shown in the plot

Fig. 4

Radiomics nomogram, calibration curves and DCA curves. (A) The radiomics nomogram, combining age, BMI, gender and Radscore, was developed in the training cohort. (B–D) The nomogram calibration curves in training (B), internal validation (C), and external validation (D) cohorts. Calibration curves indicate the goodness-of-ft of the model. (E) Decision curve analysis for different models

Fig. 5

An example of the nomogram in clinical practice. The nomogram was used to calculate the scoring process of risk of PRISm. (A) Thin-section chest CT image of a 49-year-old normal female subject. Her Clinical features were analyzed as follows: BMI = 19.5 kg/m2, Radscore = -2.64. The nomogram showed that this patient had a total of 174 points after summing all points, which corresponds to a close to 4.00% probability of PRISm. (B) Thin-section chest CT image of a 43-year-old male subject. His clinical features were analyzed as follows: BMI = 30.80 kg/m2, Radscore = 4.30. The nomogram showed that this patient had a total of 225 points after summing all points, which corresponds to a close to 96.9% probability of PRISm