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World J Gastrointest Oncol ; 9(9): 354-362, 2017 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-28979717

RESUMEN

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: There were 201 (75.8%) well-, 45 (16.9%) moderately- and 19 (7.2%) poorly-differentiated tumors. Histological grade significantly stratified the 5-year overall survival (OS), 94%, 71% and 30% respectively (P < 0.001) as well as the 5-year disease-free survival (DFS) 66%, 21% and 0%, respectively (P < 0.001). Independent predictors of DFS included tumor grade (HR = 1.78, 95%CI: 1.21-2.63, P = 0.008), lymph node involvement (HR = 0.33, 95%CI: 0.11-0.98, P < 0.02), previous surgical score (HR = 1.31, 95%CI: 1.1-1.65, P = 0.03) and peritoneal carcinomatosis index (PCI) (HR = 1.05, 95%CI: 1.02-1.08, P = 0.002). Independent predictors of OS include tumor grade (HR = 2.79, 95%CI: 1.26-6.21, P = 0.01), PCI (HR = 1.10, 95%CI: 1.03-1.16, P = 0.002), and complete cytoreduction (HR = 0.32, 95%CI: 0.11-0.92, P = 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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