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CDX2 Expression and Prognostic Factors of Resectable Pulmonary Large Cell Neuroendocrine Carcinoma.
- Source :
-
Clinical Medicine Insights: Oncology . 11/26/2020, p1-11. 11p. - Publication Year :
- 2020
-
Abstract
- Background and Aim: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare neoplasm, and its clinical features and management are still limited. We evaluated the clinicopathological factors, including CDX2 immunohistochemical expression, to predict survival in patients with LCNEC. Patients and Methods: In all, 50 patients with LCNEC who underwent surgery at 4 institutes between 2001 and 2017 were included. Clinicopathological characteristics were evaluated for prognostic factors and statistically analyzed by Kaplan-Meier curve with a log-rank test or Cox regression models. We used immunohistochemical (IHC) analysis to determine the expressions of CDX2 and compared them with clinicopathological factors and survival. Results: Sixteen of the 50 cases (32%) were CDX2 positive. No correlation was found between the CDX2 expression by IHC and clinicopathological factors. Multivariate analysis identified adjuvant chemotherapy (hazard ratio [HR] =2.86, 95% confidence interval [CI] = 1.04-8.16, P =.04) and vascular invasion (HR = 4.35, 95% CI = 1.21-15.63, P =.03) as being associated with a significantly worse rate of recurrence-free survival. Conclusion: CDX2 was expressed in 1/3 of LCNEC but not associated with prognostic factor. Adjuvant chemotherapy and vascular invasion were associated with a negative prognostic factor of LCNEC. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CANCER patients
*CANCER relapse
*COMBINED modality therapy
*CONFIDENCE intervals
*IMMUNOHISTOCHEMISTRY
*LUNG tumors
*MULTIVARIATE analysis
*NEUROENDOCRINE tumors
*SURVIVAL analysis (Biometry)
*TRANSCRIPTION factors
*SYMPTOMS
*PROPORTIONAL hazards models
*DESCRIPTIVE statistics
*KAPLAN-Meier estimator
*LOG-rank test
Subjects
Details
- Language :
- English
- ISSN :
- 11795549
- Database :
- Academic Search Index
- Journal :
- Clinical Medicine Insights: Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 147263890
- Full Text :
- https://doi.org/10.1177/1179554920967319