Stratification of outcomes for mucinous appendiceal adenocarcinoma with peritoneal metastasis by histological grade

Travis E. Grotz(The University of Texas MD Anderson Cancer Center), Richard E. Royal(The University of Texas MD Anderson Cancer Center), Paul F. Mansfield(The University of Texas MD Anderson Cancer Center), Michael J. Overman(The University of Texas MD Anderson Cancer Center), Gary N. Mann(The University of Texas MD Anderson Cancer Center), Kristen A. Robinson(The University of Texas MD Anderson Cancer Center), Karen A. Beaty(The University of Texas MD Anderson Cancer Center), Safiea Rafeeq(The University of Texas MD Anderson Cancer Center), Auerlio Matamoros(The University of Texas MD Anderson Cancer Center), M. Taggart(The University of Texas MD Anderson Cancer Center), Keith F. Fournier(The University of Texas MD Anderson Cancer Center)
World Journal of Gastrointestinal Oncology
January 1, 2017
Cited by 36Open Access
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Abstract

AIM: To investigate the importance of a three-tiered histologic grade on outcomes for patients with mucinous appendiceal adenocarcinoma (MAA). METHODS: Two hundred and sixty-five patients with MAA undergoing cytoreductive surgery and hyperthermic intraperitoneal chemotherapy were identified from a prospective database from 2004 through 2014. All pathology was reviewed by our gastrointestinal subspecialty pathologists and histological grade was classified as well-differentiated, moderately differentiated, and poorly differentiated. Survival analysis was performed using Cox proportional hazards regression. RESULTS: = 0.03). Tumor grade and PCI were the only independent predictors of both DFS and OS. Furthermore, histological grade and lymphovascular invasion stratified the risk of lymph node metastasis into a low (6%) and high (40%) risk groups. CONCLUSION: Our data demonstrates that moderately differentiated MAA have a clinical behavior and outcome that is distinct from well- and poorly-differentiated MAA. The three-tier grade classification provides improved prognostic stratification and should be incorporated into patient selection and treatment algorithms.


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