Computed tomography versus frozen sections for distinguishing lung adenocarcinoma: A cohort study of concordance rate.

BACKGROUND: Computed tomography (CT) imaging can help to predict the pathological invasiveness of early-stage lung adenocarcinoma and guide surgical resection. This retrospective study investigated whether CT imaging could distinguish pre-invasive lung adenocarcinoma from IAC. It also compared final pathology prediction accuracy between CT imaging and intraoperative frozen section analysis. METHODS: This study included 2093 patients with early-stage peripheral lung adenocarcinoma who underwent CT imaging and intraoperative frozen section analysis between March 2013 and November 2014. Nodules were classified as ground-glass (GGNs), part-solid (PSNs), and solid nodules according to CT findings; they were classified as pre-IAC and IAC according to final pathology. Univariate, multivariate, and receiver operating characteristic (ROC) curve analyses were performed to evaluate whether CT imaging could distinguish pre-IAC from IAC. The concordance rates of CT imaging and intraoperative frozen section analyses with final pathology were also compared to determine their accuracies. RESULTS: Multivariate analysis identified tumor size as an independent distinguishing factor. ROC curve analyses showed that the optimal cut-off sizes for distinguishing pre-IAC from IAC for GGNs, PSNs, and solid nodules were 10.79, 11.48, and 11.45 mm, respectively. The concordance rate of CT imaging with final pathology was significantly greater than the concordance rate of intraoperative frozen section analysis with final pathology (P = 0.041). CONCLUSION: CT imaging could distinguish pre-IAC from IAC in patients with early-stage lung adenocarcinoma. Because of its accuracy in predicting final pathology, CT imaging could contribute to decisions associated with surgical extent. Multicenter standardized trials are needed to confirm the findings in this study.

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

BACKGROUND: To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. METHODS: We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. RESULTS: There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). CONCLUSIONS: For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

Plasma ghrelin levels are closely associated with severity and morphology of angiographically-detected coronary atherosclerosis in Chineses patients with diabetes mellitus.

AIM: Low plasma ghrelin level was found to be associated with diabetes, and ghrelin was shown to inhibit pro-atherogenic changes in experimental models of atherosclerosis. The aim of this study was to investigate the relationship between plasma ghrelin levels and coronary atherosclerotic lesions in Chinese patients with diabetes. METHODS: Plasma ghrelin levels were measured using an ELISA kit. The severity of coronary artery disease (CAD) was determined via angiography. Composition of atherosclerotic plaques was detected via coronary CT angiography. RESULTS: A total of 178 patients with diabetes were recruited. Among the patients, 70 were diagnosed with acute coronary syndrome (ACS), 82 with stable angina pectoris (SAP) and 26 without coronary angiographic finding (controls). A negative correlation was found between ghrelin levels and the severity of the CAD, as determined via the Gensini score (r=-0.2434; P=0.0217). In diabetic patients with CAD and a complex lesion, the plasma ghrelin levels were significantly lower than in those with a simple lesion (ACS group: 3.81 ± 0.49 ng/mL vs 4.72 ± 0.50 ng/mL, P<0.0001; SAP group: 4.21 ± 0.52 ng/mL vs 4.76 ± 0.59 ng/mL, P=0.0397). Angiographically-detected complex lesion was an independent factor associated with ghrelin levels (adjusted beta coefficient=-0.67, 95% CI -0.97 to -0.37, P<0.0001). CONCLUSION: Low plasma ghrelin level is closely related to angiographically-detected severity and the complex lesion morphology in Chinese diabetic patients with CAD.