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. 2021 Aug;62(3):323-330.
doi: 10.1111/ajd.13624. Epub 2021 May 24.

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

Shayan Cheraghlou  1 , Praneeth Sadda  2   3 , George O Agogo  4 , Michael Girardi  1
Affiliations

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

Shayan Cheraghlou et al. Australas J Dermatol. 2021 Aug.

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.

Keywords: CART; Merkel cell carcinoma; NCDB; SEER; machine-learning; staging.

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