医学
乳外佩吉特病
手术切缘
边距(机器学习)
莫氏手术
回顾性队列研究
预测值
外科
放射科
疾病
切除术
病理
内科学
机器学习
计算机科学
作者
Qianxi Li,Guohong Zhang,Xinyu Yao,Shuxia Yang,Ping Tu,Hang Li
标识
DOI:10.1684/ejd.2022.4204
摘要
Extramammary Paget's disease (EMPD) frequently extends beyond clinical borders, causing a high recurrence rate. Mohs micrographic surgery (MMS) has been used for management of EMPD, but its efficiency is compromised by technical limitations inherent in MMS. To identify clinicopathologic parameters of predictive value regarding MMS final margin width (FMW) for EMPD, and provide some preliminary guidance in selecting initial surgical margin width for improved efficiency. This was a retrospective study of 150 consecutive EMPD patients who underwent MMS between 2013 and 2019. Clinicopathological parameters and surgical data were collected to construct a classification tree of FMW. A six-node classification tree with a sensitivity of 86.25% and a specificity of 48.57% was generated. Lesion width, disease duration and inflammation score were used to select subgroups of patients in whom optimal initial margin width may be recommended. Classification tree analysis may help identify important variables to consider when selecting MMS initial surgical margins for EMPD.
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