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Concomitant Mutations in EGFR 19Del/L858R Mutation and Their Association with Response to EGFR-TKIs in NSCLC Patients
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Few studies on how to diagnose pulmonary neuroendocrine tumors through morphometric analysis have been reported. In this study, we measured and analyzed the characteristic parameters of pulmonary neuroendocrine tumors using an image analyzer to aid in diagnosis.
Sixteen cases of typical carcinoid tumor, 5 cases of atypical carcinoid tumor, 15 cases of small cell carcinoma, and 51 cases of large cell neuroendocrine carcinoma were analyzed. Using an image analyzer, we measured the nuclear area, perimeter, and the major and minor axes.
The mean nuclear area was 0.318±0.101 µm2 in typical carcinoid tumors, 0.326±0.119 µm2 in atypical carcinoid tumors, 0.314±0.107 µm2 in small cell carcinomas, and 0.446±0.145 µm2 in large cell neuroendocrine carcinomas. The mean nuclear circumference was 2.268±0.600 µm in typical carcinoid tumors, 2.408±0.680 µm in atypical carcinoid tumors, 2.158±0.438 µm in small cell carcinomas, and 3.247±1.276 µm in large cell neuroendocrine carcinomas. All parameters were useful in distinguishing large cell neuroendocrine carcinoma from other tumors (p=0.001) and in particular, nuclear circumference was the most effective (p=0.001).
Pulmonary neuroendocrine tumors showed nuclear morphology differences by subtype. Therefore, evaluation of quantitative nuclear parameters improves the accuracy and reliability of diagnosis.
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Ezrin, a member of the ezrin-radixin-moesin family, is implicated in tumor progression, metastatic dissemination, and adverse outcomes, in several cancer types. In this study, we explored the clinicopathological significance of ezrin expression in non-small cell lung carcinomas (NSCLCs).
Immunohistochemical analysis of tissue microarray with 112 surgically resected NSCLC specimens, was performed to examine the ezrin expression. We also correlated ezrin expression with other clinicopathological features and prognosis.
The ezrin-positive group revealed significantly higher correlation with pleural invasion (p=0.016) and pathologic stage (p=0.050). Univariate survival analysis showed that ezrin-positive group had a significantly shorter cancer-specific survival than ezrin-negative group (p=0.016). Meanwhile, female (p=0.030), no pleural invasion (p=0.023), no lymphatic invasion (p=0.026), and early pathologic stage (p=0.008) significantly correlated with longer survival. Multivariate survival analysis showed that variables such as ezrin positivity (p=0.032), female (p=0.035), and early pathologic stage (p=0.001) were independent prognostic factors for NSCLC.
Ezrin might be a molecular marker to predict poor prognosis of NSCLC.
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Distinguishing small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC) of the lung is difficult with little information about interobserver variability.
One hundred twenty-nine cases of resected SCLC and LCNEC were independently evaluated by four pathologists and classified according to the 2004 World Health Organization criteria. Agreement was regarded as "unanimous" if all four pathologists agreed on the classification. The kappa statistic was calculated to measure the degree of agreement between pathologists. We also measured cell size using image analysis, and receiver-operating-characteristic curve analysis was performed to evaluate cell size in predicting the diagnosis of high-grade neuroendocrine (NE) carcinomas in 66 cases.
Unanimous agreement was achieved in 55.0% of 129 cases. The kappa values ranged from 0.35 to 0.81. Morphometric analysis reaffirmed that there was a continuous spectrum of cell size from SCLC to LCNEC and showed that tumors with cells falling in the middle size range were difficult to categorize and lacked unanimous agreement.
Our results provide an objective explanation for considerable interobserver variability in the diagnosis of high-grade pulmonary NE carcinomas. Further studies would need to define more stringent and objective definitions of cytologic and architectural characteristics to reliably distinguish between SCLC and LCNEC.
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