期刊
JOURNAL OF THORACIC ONCOLOGY
卷 9, 期 8, 页码 1111-1119出版社
ELSEVIER SCIENCE INC
DOI: 10.1097/JTO.0000000000000235
关键词
NSCLC; CTC; diagnostic; stage I; lung nodule
资金
- NCATS NIH HHS [UL1 TR001085] Funding Source: Medline
- NCI NIH HHS [R33 CA173373, U54 CA151459, P50CA114747, U54 CA143906, U54 CA119367, U54CA143906, P50 CA114747] Funding Source: Medline
- NCRR NIH HHS [UL1 RR025744] Funding Source: Medline
Introduction: Circulating tumor microemboli (CTM) are potentially important cancer biomarkers, but using them for cancer detection in early-stage disease has been assay limited. We examined CTM test performance using a sensitive detection platform to identify stage I non-small-cell lung cancer (NSCLC) patients undergoing imaging evaluation. Methods: First, we prospectively enrolled patients during F-18-FDG PET-CT imaging evaluation for lung cancer that underwent routine phlebotomy where CTM and circulating tumor cells (CTCs) were identified in blood using nuclear (DAPI), cytokeratin (CK), and CD45 immune-fluorescent antibodies followed by morphologic identification. Second, CTM and CTC data were integrated with patient (age, gender, smoking, and cancer history) and imaging (tumor diameter, location in lung, and maximum standard uptake value [SUVmax]) data to develop and test multiple logistic regression models using a case-control design in a training and test cohort followed by crossvalidation in the entire group. Results: We examined 104 patients with NSCLC, and the subgroup of 80 with stage I disease, and compared them to 25 patients with benign disease. Clinical and imaging data alone were moderately discriminating for all comers (Area under the Curve [AUC] = 0.77) and by stage I disease only (AUC = 0.77). However, the presence of CTM combined with clinical and imaging data was significantly discriminating for diagnostic accuracy in all NSCLC patients (AUC = 0.88, p value = 0.001) and for stage I patients alone (AUC = 0.87, p value = 0.002). Conclusion: CTM may add utility for lung cancer diagnosis during imaging evaluation using a sensitive detection platform.
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