A Predictive Model for Nodal Metastases in Patients With Appendiceal Cancers.

Szczegóły
Abstrakt

Tytuł:: A Predictive Model for Nodal Metastases in Patients With Appendiceal Cancers.
Autorzy:: Day RW; Division of Surgical Oncology, Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ.
Chang YH; Surgical Outcomes Program, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Arizona, Phoenix, AZ.; Department of Statistics, Mayo Clinic Arizona, Phoenix, AZ.
Stucky CC; Division of Surgical Oncology, Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ.
Gray R; Division of Surgical Oncology, Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ.
Pockaj B; Division of Surgical Oncology, Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ.
Wasif N; Division of Surgical Oncology, Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ.; Surgical Outcomes Program, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Arizona, Phoenix, AZ.
Źródło:: Annals of surgery [Ann Surg] 2021 Jul 01; Vol. 274 (1), pp. 155-161.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: Philadelphia, PA : Lippincott Williams & Wilkins
MeSH Terms:: Decision Support Techniques*
Lymphatic Metastasis*
Appendiceal Neoplasms/*pathology
Aged ; Aged, 80 and over ; Appendiceal Neoplasms/surgery ; Clinical Decision-Making ; Colectomy ; Decision Making, Shared ; Female ; Humans ; Male ; Middle Aged ; Neoplasm Grading ; Neoplasm Staging ; Regression Analysis
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Entry Date(s):: Date Created: 20190731 Date Completed: 20210810 Latest Revision: 20230923
Update Code:: 20240105
DOI:: 10.1097/SLA.0000000000003501
PMID:: 31361626
: Czasopismo naukowe

Background: Histologic subtypes of appendiceal cancer vary in their propensity for metastases to regional lymph nodes (LN). A predictive model would help direct subsequent surgical therapy.
Methods: The National Cancer Database was queried for patients with appendiceal cancer undergoing surgery between 1998 and 2012. Multivariable logistic regression was used to develop a predictive model of LN metastases which was internally validated using Brier score and Area under the Curve (AUC).
Results: A total of 21,647 patients were identified, of whom 9079 (41.9%) had node negative disease, 4575 (21.1%) node positive disease, and 7993 (36.9%) unknown LN status. The strongest predictors of LN positivity were histology (carcinoid tumors OR 12.78, 95% CI 9.01-18.12), increasing T Stage (T3 OR 3.36, 95% CI 2.52-4.50, T4 OR 6.30, 95% CI 4.71-8.42), and tumor grade (G3 OR 5.55, 95% CI 4.78-6.45, G4 OR 5.98, 95% CI 4.30-8.31). The coefficients from the regression analysis were used to construct a calculator that generated predicted probabilities of LN metastases given certain inputs. Internal validation of the overall model showed an AUC of 0.75 (95% CI 0.74-0.76) and Brier score of 0.188. Histology-specific predictive models were also constructed with an AUC that varied from 0.669 for signet cell to 0.75 for goblet cell tumors.
Conclusions: The risk for nodal metastases in patients with appendiceal cancers can be quantified with reasonable accuracy using a predictive model incorporating patient age, sex, tumor histology, T-stage, and grade. This can help inform clinical decision making regarding the need for a right hemicolectomy following appendectomy.
Competing Interests: The authors report no conflicts of interest.
(Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.)

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