Diagnostic Accuracy Of Detecting CTC By Using OHSV1-hTERT-GFP Method For Lung Cancer:A Pilot Study

Objective: For pulmonary nodules with diameter ≤ 30 mm examined by CT,the identification ability of o HSV1-h TERT-GFP method to circulating tumor cells with high telomerase expression in peripheral blood was tested,with histopathological results as the gold standard,and its diagnostic value for lung cancer was evaluated.Methods: From December 2019 to March 2020,according to the inclusion criteria and exclusion criteria designed by the trial scheme,64 consecutive newly diagnosed patients with pulmonary nodules whose diameter ≤ 30 mm examined by CT and 5 healthy persons were prospectively recruited.The samples were provided by the Department of Thoracic surgery of Fujian Medical University Union Hospital.Using o HSV1-h TERT-GFP method to detect circulating tumor cells with high telomerase expression in peripheral blood as a new diagnostic test,and histopathological diagnosis as the only gold standard,a prospective synchronous single-blind diagnostic accuracy test was carried out: the clinical data were masked to the laboratory physicians who performed the CTC assay,and the results of CTC assay were masked to the pathologists who performed the histopathological test.The results of CTC assay and histopathology were masked to radiologists who performed artificial intelligence diagnosis of CT examination.Continuous data were described in the form of median(quartile interval),and Mann-Whitney U test or Fisher exact test were used for comparison between groups.Taking the histopathological results as the gold standard,the ROC curve was drawn to evaluate the diagnostic efficiency of o HSV1-h TERT-GFP and AI,and the Logistic regression equation was constructed to assess the diagnostic value of AI combined with o HSV1-h TERT-GFP method in the diagnosis of lung cancer and compare the two AUC.The optimal cutoff point was determined by selecting the point on the ROC curve with the highest diagnostic accuracy,and the diagnostic accuracy indexes such as sensitivity,specificity and accuracy were calculated accordingly.Results: A total of 57 patients with newly diagnosed pulmonary nodules(49patients with lung cancer and 8 patients with lung benign disease by gold standard)and 5 healthy donors were enrolled in this study.The level of CTC in the lung cancer group was higher than that in the lung benign disease group(1.00 vs.0.000.p=0.243).The AUC of o HSV1-h TERT-GFP method was 0.600(95%CI 0.468 to0.723,p=0.301).With CTC=1 as the cut-off value,the sensitivity,specificity and accuracy of CTC in the diagnosis of lung cancer were 61.22%,69.23% and 62.9%.The AUC for the combination of o HSV1-h TERT-GFP method with AI was 0.789(95%CI 0.667 to 0.882,p=0.0003),which further improves the diagnostic accuracy of pulmonary nodules,and the specificity of serial diagnosis is increased from61.54% to 76.92%.Conclusion: For pulmonary nodules with diameter ≤ 30 mm examined by CT,o HSV1-h TERT-GFP method can be used to detect circulating tumor cells with high telomerase expression in peripheral blood,which has potential clinical value for differential diagnosis of lung cancer.The combination of o HSV1-h TERT-GFP assay method with artificial intelligence has higher diagnostic value and lower misdiagnosis rate.The process of "LDCT+ AI + Liquid Biopsy" has potential clinical value for newly diagnosed patients with pulmonary nodules and should expand the sample size for further study.