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

Abstract

Purpose: It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.

Methods: Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results: The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility.

Conclusions: The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Keywords: COPD; PRISm; Radiomics.

Fig. 1

Patient recruitment process at four centers.

Fig. 2

Screening of radiomics features by the least absolute shrinkage regression and the histogram showing Rad-score of those selected features. A. 10-fold cross-validation conducted to select tuning parameter (λ) in LASSO model on the basis of minimum criteria. Binomial deviance obtained through cross-validation by LASSO regression was plotted with log (λ). The best λ value was chosen to be 0.007. B. 10-fold cross-validation was performed to select the value in a for drawing the black vertical line. The 15 obtained features with nonzero coefficients were shown in the plot. C. The x- and y-axes stand for radiomics coefficients and those 15 selected radiomics features separately.

Fig. 3

AUC of Rad-score, clinical model, and combined model for training, internal validation, and external validation sets. The combined model outperformed clinical model and Rad-score in predictive performance for three sets.

Fig. 4

The radiomics nomogram performance and DCA of diverse models. (A) Representation of radiomics nomogram constructed by incorporating clinical factors and radiomics signature. Calibration curves for radiomics nomogram of (B) training, (C) internal validation and (D) external validation sets, respectively. (E) DCA of diverse models.

Fig. 5

The risk scores of COPD in two patients were calculated by using the nomogram. A. Thin‑slice chest CT images of PRISm in a 51‑year‑old woman with height 163 cm, non‑smoker, Radscore ‑3.06. C. The nomogram shows that the total score was 174 points, corresponding to the probability of developing COPD is approximately 2.25 %. B. Thin‑slice chest CT image of COPD in a 72‑year‑old male subject. He is 162 cm tall, former smoker, and has a Radscore of 4.68. D. The total score of the nomogram was 254, corresponding to the probability of developing COPD of approximately 99.6 %. PRISm Preserved Ratio Impaired Spirometry, COPD chronic obstructive pulmonary disease, CT computed tomography, FEV1/FVC ratio of forced expiratory volume in 1 s to forced vital capacity.