Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study

Abstract

Purpose

This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD.

Patients and Methods

This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms.

Results

Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761–0.854] and 0.753 [95% CI, 0.674–0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770–0.858] and 0.780 [95% CI, 0.705–0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824–0.903], 0.811 [95% CI, 0.742–0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort.

Conclusion

The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.

Keywords:

• chronic obstructive pulmonary disease
• lung cancer
• computed tomography
• chest imaging
• nomogram

Abbreviations

COPD, Chronic obstructive pulmonary disease; PFT, Pulmonary function test; CT, Computed tomography; BMI, Body mass index; VC, Vital capacity; FEV1, Forced expiratory volume in one second; FVC, Forced vital capacity; FEV1%pred, the percentage of FEV1 to the predicted value; MEF25, Maximum expiratory flow at 25% of the FVC; ELLC, Emphysema in the lobe of lung cancer; ERL, Emphysema in the remaining lobes; ROC, Receiver operating characteristic; AUC, Area under the curve; CI, Confidence interval; OS, Overall survival.

Data Sharing Statement

The data supporting the conclusion of this article are included within the article. All data are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the sponsors who provide financial support for the conduct of the research. The details of the sponsors are described below.

Author Contributions

Shiyuan Liu and Li Fan are co-corresponding authors. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that we have no known competing financial interests or other conflict of interests that could influence our work reported in this paper.

Additional information

Funding

This study was sponsored by National Key R&D Program of China [grant numbers 2022YFC2010002, 2022YFC2010000]; Shanghai Sailing Program [grant number 20YF1449000]; the National Natural Science Foundation of China [grant numbers 81871321, 81930049, 82171926, 81871405]; the Youth Fund of the National Natural Science Foundation of China [grant numbers 82202140, 82001812]; Pyramid Talent Project of Shanghai Changzheng Hospital; the program of Science and Technology Commission of Shanghai Municipality [grant numbers 19411951300, 21DZ2202600]; Medical imaging database construction program of National Health Comission [grant number YXFSC2022JJSJ002]; and the clinical Innovative Project of Shanghai Changzheng Hospital [grant number 2020YLCYJ-Y24]. The funders have no role in the study design, data collection, data analysis and writing the manuscripts.