Surace Alessandra, Ferrarese Alessia, Gentile Valentina, Bindi Marco, Cumbo Jacopo, Solej Mario, Enrico Stefano, Martino Valter
University of Turin, Department of Oncology, School of Medicine, Teaching Hospital "San Luigi Gonzaga", Section of General Surgery, Orbassano, Turin, Italy.
Department of Oncology, University of Turin, Section of General Surgery, San Luigi Gonzaga Teaching Hospital, Regione Gonzole 10, 10043 Orbassano, Turin, Italy.
Open Med (Wars). 2016 Nov 19;11(1):418-425. doi: 10.1515/med-2016-0074. eCollection 2016.
Aim of the study is to highlight difficulties faced by an inexperienced surgeon in approaching endorectal-ultrasound, trying to define when learning curve can be considered complete. A prospective analysis was conducted on endorectal-ultrasound performed for subperitoneal rectal adenocarcinoma staging in the period from January 2008 to July 2013, reported by a single surgeon of Department of Oncology, Section of General Surgery, "San Luigi Gonzaga" Teaching Hospital, Orbassano (Turin, Italy); the surgeon had no previous experience in endorectal-ultrasound. Fourty-six endorectal-ultrasounds were divided into two groups: early group (composed by 23 endorectal-ultrasounds, made from January 2008 to May 2009) and late group (composed by 23 endorectal-ultrasound, carried out from June 2009 to July 2013). In our experience, the importance of a learning curve is evident for T staging, but no statystical significance is reached for results deal with N stage. We can conclude that ultrasound evaluation of anorectal and perirectal tissues is technically challenging and requires a long learning curve. Our learning curve can not be closed down, at least for N parameter.
本研究的目的是突出一位缺乏经验的外科医生在进行直肠内超声检查时所面临的困难,试图确定学习曲线何时可被视为完成。对2008年1月至2013年7月期间由意大利都灵奥尔巴萨诺“圣路易吉·贡扎加”教学医院普通外科肿瘤学部的一位外科医生报告的用于腹膜下直肠腺癌分期的直肠内超声检查进行了前瞻性分析;该外科医生此前没有直肠内超声检查的经验。46次直肠内超声检查被分为两组:早期组(由2008年1月至2009年5月进行的23次直肠内超声检查组成)和晚期组(由2009年6月至2013年7月进行的23次直肠内超声检查组成)。根据我们的经验,学习曲线对T分期的重要性是明显的,但对于N分期的结果没有达到统计学意义。我们可以得出结论,对肛管直肠和直肠周围组织的超声评估在技术上具有挑战性,并且需要很长的学习曲线。我们的学习曲线至少对于N参数不能结束。