McEvoy AM, Hippe DS, Lachance K, Park S, Cahill K, Redman M, Gooley T, Kattan MW, Nghiem P. Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator.
J Am Acad Dermatol 2024;
90:569-576. [PMID:
37984720 PMCID:
PMC10922724 DOI:
10.1016/j.jaad.2023.11.020]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND
Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.
PURPOSE
Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.
METHODS
Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.
RESULTS
In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).
LIMITATIONS
Lack of an external data set for model validation.
CONCLUSION/RELEVANCE
As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.
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