Effect of surgical treatment for anorectal melanoma: a propensity score-matched analysis of the Surveillance, Epidemiology, and End Results programme data

Author:

Lei XiaoORCID,Qingqing Luo,Weijie Yuan,Li Peng,Huang Changhao,Kexun Yu,Zihua Chen

Abstract

ObjectiveAnorectal melanoma (AM) is a rare but aggressive tumour with limited information in the existing literature. This study aimed to assess the effect of surgical treatment for AM and predict the prognosis of affected patients.DesignA retrospective cohort study.SettingData of patients diagnosed with AM between 1975 and 2016 in the USA were collected from the Surveillance, Epidemiology, and End Results (SEER) database.ParticipantsThis study enrolled a total of 795 patients with AM from the SEER database and the validation cohort comprised 40 patients with AM enrolled from Chinese institutes.Primary and secondary outcome measuresOverall survival (OS) and AM-specific survival (AM-SS).ResultsA total of 795 patients with AM diagnosed between 1975 and 2016 were enrolled in this study. Data over the past four decades showed a trend of increase in incidence rate. A nomogram based on a multivariate Cox regression model was generated to predict AM-SS. The C-index of the nomogram was 0.74 (95% CI 0.71 to 0.77) on internal verification. In the validation cohort, the C-index of the nomogram was 0.72 (95% CI 0.68 to 0.76). The results of propensity score matching (PSM) analysis showed that patients who underwent surgical treatment achieved significant survival (OS: log-rank=17.41, p<0.001; AM-SS: log-rank=14.55, p<0.001). Patients who underwent surgery were stratified into local and extended surgery subgroups. AM-SS and OS were also compared after PSM, but the results were not significantly different between the two surgery subgroups (all p>0.05).ConclusionsThe nomogram based on the analysis of SEER data showed good performance in predicting OS and AM-SS. Patients with AM can benefit from surgery; however, extensive surgery and appendectomy may not improve AM-SS or OS.

Funder

Central South University Xiangya Clinical Big Data Project

National Natural Science Foundation of China

Publisher

BMJ

Subject

General Medicine

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