A machine-learning modified CART algorithm informs Merkel cell carcinoma prognosis.

ABSTRACT

BACKGROUND: Merkel cell carcinoma (MCC) is a rare neuroendocrine skin cancer with a high mortality rate. MCC staging is currently based on tumour primary size, clinical detectability of lymph node metastases, performance of a lymph node biopsy, and presence of distant metastases. OBJECTIVE: We aimed to use a modified classification and regression tree (CART) algorithm using available data points in the National Cancer Database (NCDB) to elucidate novel prognostic factors for MCC. METHODS: Retrospective cohort study of the NCDB and Surveillance, Epidemiology, and End Results (SEER) registries. Cases from the NCDB were randomly assigned to either the training or validation cohorts. A modified CART algorithm was created with data from the training cohort and used to identify prognostic groups that were validated in the NCDB validation and SEER cohorts. RESULTS: A modified CART algorithm using tumour variables available in the NCDB identified prognostic strata as follows I local disease, II ≤3 positive nodes, III ≥4 positive nodes, and IV presence of distant metastases. Three-year survival for these groups in the NCDB validation cohort were 81.2% (SE 1.7), 59.6% (SE 3.0), 38.0% (SE 6.0), and 20.2% (SE 7.0), respectively. These strata were exhibited greater within-group homogeneity than AJCC groups and were more predictive of survival. CONCLUSIONS: Risk-stratified grouping of MCC patients incorporating positive lymph node count were strongly predictive of survival and demonstrated a high degree of within-group homogeneity and survival prediction. Incorporation of positive lymph node count within overall staging or sub-staging may help to improve future MCC staging criteria.