1. Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer.
- Author
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Carlsson A, Nair VS, Luttgen MS, Keu KV, Horng G, Vasanawala M, Kolatkar A, Jamali M, Iagaru AH, Kuschner W, Loo BW Jr, Shrager JB, Bethel K, Hoh CK, Bazhenova L, Nieva J, Kuhn P, and Gambhir SS
- Subjects
- Aged, Aged, 80 and over, Area Under Curve, Female, Fluorodeoxyglucose F18, Humans, Indoles analysis, Keratins analysis, Leukocyte Common Antigens analysis, Male, Middle Aged, Multimodal Imaging, Neoplasm Staging, Neoplastic Cells, Circulating chemistry, Positron-Emission Tomography, Prospective Studies, Radiopharmaceuticals, Risk Assessment, Tomography, X-Ray Computed, Tumor Burden, Biomarkers, Tumor, Carcinoma, Non-Small-Cell Lung diagnosis, Embolism pathology, Lung Neoplasms diagnosis, Neoplastic Cells, Circulating pathology
- Abstract
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 18F-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 cross-validation 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.
- Published
- 2014
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