Circulating Genetically Abnormal Cells Add Non-Invasive Diagnosis Value to Discriminate Lung Cancer in Patients With Pulmonary Nodules ≤10 mm.

Maosong Ye,Xin Ye,Botao Xiao,Chunxue Bai,Jiayuan Sun,Yanci Chen,Meng Huang,Xianjun Fan,Juncheng Zhang,Zilong Liu,Xiaoxuan Zheng,Chuoji Huang

doi:10.3389/fonc.2021.638223

Abstract

BackgroundLung cancer screening using low-dose computed tomography (LDCT) often leads to unnecessary biopsy because of the low specificity among patients with pulmonary nodules ≤10 mm. Circulating genetically abnormal cells (CACs) can be used to discriminate lung cancer from benign lung disease. To examine the diagnostic value of CACs in detecting lung cancer for patients with malignant pulmonary nodules ≤10 mm.MethodsIn this prospective study, patients with pulmonary nodules ≤10 mm who were detected at four hospitals in China from January 2019 to January 2020 were included. CACs were detected using fluorescence in-situ hybridization. All patients were confirmed as lung cancer or benign disease by further histopathological examination. Multivariable logistic regression models were established to detect the presence of lung cancer using CACs and other associated characteristics. Receiver operating characteristic analysis was used to evaluate the performance of CACs for lung cancer diagnosis.ResultsOverall, 125 patients were included and analyzed. When the cutoff value of CACs was >2, the sensitivity and specificity for lung cancer were 70.5 and 86.4%. Male (OR = 0.330, P = 0.005), maximum solid nodule (OR = 2.362, P = 0.089), maximum nodule located in upper lobe (OR = 3.867, P = 0.001), and CACs >2 (OR = 18.525, P < 0.001) met the P < 0.10 criterion for inclusion in the multivariable models. The multivariable logistic regression model that included the dichotomized CACs (>2 vs. ≤2) and other clinical factors (AUC = 0.907, 95% CI = 0.842–0.951) was superior to the models that only considered dichotomized CACs or other clinical factors and similar to the model with numerical CACs and other clinical factors (AUC = 0.913, 95% CI = 0.850–0.956).ConclusionCACs presented a significant diagnostic value in detecting lung cancer for patients with pulmonary nodules ≤10 mm.

Highlights

Lung cancer is the leading cause of mortality globally, and it is important to detect lung cancer in early-stage to improve patients’ survival and life quality [1, 2]
The multivariable logistic regression model that included the dichotomized Circulating genetically abnormal cells (CACs) (>2 vs. ≤2) and other clinical factors (AUC = 0.907, 95% CI = 0.842–0.951) was superior to the models that only considered dichotomized CACs or other clinical factors and similar to the model with numerical CACs and other clinical factors (AUC = 0.913, 95% CI = 0.850–0.956)
Of the 420 individuals who were enrolled in the Multicenter Chinese Pulmonary Nodule Detection (MCPND) trials, we excluded patients whose pulmonary nodule was bigger than 10 mm (Supplemental Figure 1)

Summary

Introduction

Lung cancer is the leading cause of mortality globally, and it is important to detect lung cancer in early-stage to improve patients’ survival and life quality [1, 2]. The main issue with low-dose computed tomography (LDCT) in lung cancer screening is the high false-positive rate, which is often difficult for physicians to conclude whether their patients can be benefited from additional invasive examinations, including biopsy or surgery [3]. Among those high-risk individuals with pulmonary nodules ≤10 mm, about 1/3 is benign [3, 4]. Lung cancer screening using low-dose computed tomography (LDCT) often leads to unnecessary biopsy because of the low specificity among patients with pulmonary nodules ≤10 mm. To examine the diagnostic value of CACs in detecting lung cancer for patients with malignant pulmonary nodules ≤10 mm

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Conclusion