Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA.
Department of Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Australas J Dermatol. 2021 Aug;62(3):323-330. doi: 10.1111/ajd.13624. Epub 2021 May 24.
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.
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.
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.
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.
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.
默克尔细胞癌（MCC）是一种罕见的神经内分泌皮肤癌，死亡率很高。MCC 的分期目前基于肿瘤原发大小、淋巴结转移的临床可检测性、淋巴结活检的进行情况以及远处转移的存在。
我们旨在使用改良的分类和回归树（CART）算法，利用国家癌症数据库（NCDB）中的可用数据点来阐明 MCC 的新预后因素。
对 NCDB 和监测、流行病学和最终结果（SEER）登记处进行回顾性队列研究。来自 NCDB 的病例被随机分配到训练或验证队列中。使用训练队列中的数据创建改良的 CART 算法，并将其用于识别在 NCDB 验证队列和 SEER 队列中验证的预后组。
使用 NCDB 中可用的肿瘤变量的改良 CART 算法确定了以下预后分层：I：局部疾病，II：≤3 个阳性淋巴结，III：≥4 个阳性淋巴结，IV：远处转移存在。这些组在 NCDB 验证队列中的 3 年生存率分别为 81.2%（SE：1.7）、59.6%（SE：3.0）、38.0%（SE：6.0）和 20.2%（SE：7.0）。这些分层在组内具有更高的同质性，并且比 AJCC 组更能预测生存。
纳入阳性淋巴结计数的 MCC 患者风险分层分组对生存具有很强的预测性，并表现出高度的组内同质性和生存预测。在总体分期或亚分期中纳入阳性淋巴结计数可能有助于改进未来的 MCC 分期标准。
