Frontiers in Oncology (Mar 2021)

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

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

DOI
https://doi.org/10.3389/fonc.2021.638223
Journal volume & issue
Vol. 11

Abstract

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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.

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