Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: a multivariable model and web-based calculator.
Merkel cell carcinoma
nomogram
prognosis
recurrence
risk calculator
Journal
Journal of the American Academy of Dermatology
ISSN: 1097-6787
Titre abrégé: J Am Acad Dermatol
Pays: United States
ID NLM: 7907132
Informations de publication
Date de publication:
18 Nov 2023
18 Nov 2023
Historique:
received:
30
04
2023
revised:
19
10
2023
accepted:
02
11
2023
medline:
21
11
2023
pubmed:
21
11
2023
entrez:
20
11
2023
Statut:
aheadofprint
Résumé
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. Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone. 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. In this multivariable model, the most impactful recurrence risk factors were: AJCC stage (p<0.001), immunosuppression (hazard ratio 2.05; p<0.001), male sex (1.59; p=0.003) and unknown primary (0.65; p=0.064). Compared to stage alone, the model improved prognostic accuracy (concordance index for two-year risk, 0.66 vs. 0.70; p<0.001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs. 78% for high-risk IIIA over five years). Lack of an external data set for model validation. / 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.
Sections du résumé
BACKGROUND
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
OBJECTIVE
Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.
METHODS
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
RESULTS
In this multivariable model, the most impactful recurrence risk factors were: AJCC stage (p<0.001), immunosuppression (hazard ratio 2.05; p<0.001), male sex (1.59; p=0.003) and unknown primary (0.65; p=0.064). Compared to stage alone, the model improved prognostic accuracy (concordance index for two-year risk, 0.66 vs. 0.70; p<0.001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs. 78% for high-risk IIIA over five years).
LIMITATIONS
CONCLUSIONS
Lack of an external data set for model validation.
CONCLUSION
CONCLUSIONS
/ 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.
Identifiants
pubmed: 37984720
pii: S0190-9622(23)03220-6
doi: 10.1016/j.jaad.2023.11.020
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2023. Published by Elsevier Inc.