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J Thorac Oncol. 2014 Aug;9(8):1111-9. doi: 10.1097/JTO.0000000000000235.

Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer.

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*The Scripps Research Institute, Department of Cell Biology, La Jolla, CA; †Department of Medicine, Stanford University School of Medicine Stanford, CA; ‡Centre Hospitalier de l'Universite de Sherbrooke, Department of Nuclear Medicine and Radiobiology, Sherbrooke, Québec; §The California Pacific Medical Center Research Institute, San Francisco, CA; ‖The VA Palo Alto Health Care System, Section of Nuclear Medicine, Palo Alto, CA; ¶Department of Radiology, Stanford University School of Medicine, Stanford, CA; #The VA Palo Alto Health Care System Section of Pulmonary & Critical Care, Palo Alto, CA; **Department of Radiation Oncology; ††Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA; ‡‡The VA Palo Alto Health Care System Section of Cardiothoracic Surgery, Palo Alto, CA; §§Scripps Clinic, Department of Pathology, La Jolla, CA; ‖‖Nuclear Medicine Division, University of San Diego Medical Center, San Diego, CA; ¶¶The Moores Cancer Center, University of San Diego Medical Center, La Jolla, CA; ##The Billings Clinic, Department of Hematology/Oncology, Billings, MT; ***Departments of Bioengineering and †††Materials Science and Engineering, Stanford University School of Medicine, Stanford, CA.



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.


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.


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).


CTM may add utility for lung cancer diagnosis during imaging evaluation using a sensitive detection platform.

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