A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules

Dong Wang; Peng Li; Xiang Fei; Shuyu Che; Jinlong Li; Yunpeng Xuan; Jinglong Wang; Yudong Han; Weiqing Gu; Yongjie Wang

doi:10.1002/jgm.3529

A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules

J Gene Med. 2023 Sep;25(9):e3529. doi: 10.1002/jgm.3529. Epub 2023 May 16.

Authors

Dong Wang¹, Peng Li¹, Xiang Fei², Shuyu Che³, Jinlong Li¹, Yunpeng Xuan¹, Jinglong Wang¹, Yudong Han¹, Weiqing Gu⁴, Yongjie Wang¹

Affiliations

¹ Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
² Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
³ Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
⁴ Department of Oncology, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China.

PMID: 37194408
DOI: 10.1002/jgm.3529

Abstract

Background: Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor-positive circulating tumor cells (FR⁺ CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model.

Methods: A total of 898 patients with a solitary pulmonary nodule who received FR⁺ CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model.

Results: The positive rate of FR⁺ CTC between patients with non-small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p < 0.001). The FR⁺ CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR⁺ CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07-1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01-1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01-1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR⁺ CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587-0.713) in the training set and 0.700 (95% CI, 0.603-0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659-0.791) in the training set and 0.828 (95% CI, 0.754-0.902) in the validation set, respectively.

Conclusions: We confirmed the value of FR⁺ CTC in diagnosing SPNs and developed a prediction model based on FR⁺ CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.

Keywords: biomarker; circulating tumor cell; diagnose; non-small cell lung cancer (NSCLC); prediction model; solitary pulmonary nodule.

Publication types

Randomized Controlled Trial
Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers, Tumor
Carcinoma, Non-Small-Cell Lung* / diagnosis
Humans
Lung Neoplasms* / diagnosis
Lung Neoplasms* / pathology
Neoplastic Cells, Circulating* / pathology
Solitary Pulmonary Nodule* / diagnosis
Solitary Pulmonary Nodule* / pathology

Substances

Biomarkers, Tumor