Han Mingyu, Guo Shihao, Ma Shuai, Zhou Quanbo, Zhang Weitao, Wang Jinbang, Zhuang Jing, Yao Hongwei, Yuan Weitang, Lian Yugui
Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan Province, People's Republic of China.
Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, People's Republic of China.
J Robot Surg. 2024 Dec 8;19(1):19. doi: 10.1007/s11701-024-02180-6.
Rectal cancer robotic surgery is becoming more and more common, but evidence for predicting surgical difficulty is scarce. Our goal was to look at the elements that influence the complexity of robot-assisted total mesorectal excision (R-TME) in the medical care of middle and low rectal cancer as well as to establish and validate a predictive model on the basis of these factors. Within this multicenter retrospective investigation, 166 consecutive patients receiving R-TME between January 2021 and December 2022 with middle and low rectal cancer were included and categorized according to the median operation time. A nomogram was created to forecast the procedure's complexity after variables that could affect its difficulty were found using logistic regression analysis. Using R software, a total of 166 patients were randomly split into two groups: a test group (48 patients) and a training group (118 patients) at a ratio of 7 to 3. The median operation time of all patients was 207.5 min; patients whose operation time was ≥ 207.5 min were allocated to the difficult surgery group (83 patients), and patients whose operation time was < 207.5 min were allocated to the nondifficult surgery group. Multivariate analysis revealed that body mass index (BMI), the gap between the tumor and the anal verge and the posterior rectal mesenteric thickness were independent predictors of surgical duration. A clinical predictive model was created and assessed employing the above independent predictors. The results of the receiver operating characteristic (ROC) analysis revealed the adequate discriminative ability of the predictive model. Our study revealed that it is feasible to predict surgical difficulty by obtaining clinical and magnetic resonance parameters for imaging (the gap between the anal verge and the tumour, and posterior mesorectal thickness), and these predictions could be useful in making clinical decisions.
直肠癌机器人手术正变得越来越普遍，但预测手术难度的证据却很少。我们的目标是研究在中低位直肠癌医疗中影响机器人辅助全直肠系膜切除术（R-TME）复杂性的因素，并基于这些因素建立和验证一个预测模型。在这项多中心回顾性研究中，纳入了2021年1月至2022年12月期间连续接受R-TME治疗的166例中低位直肠癌患者，并根据中位手术时间进行分类。在使用逻辑回归分析找到可能影响手术难度的变量后，创建了一个列线图来预测手术的复杂性。使用R软件，将总共166例患者随机分为两组：测试组（48例患者）和训练组（118例患者），比例为7比3。所有患者的中位手术时间为207.5分钟；手术时间≥207.5分钟的患者被分配到困难手术组（83例患者），手术时间<207.5分钟的患者被分配到非困难手术组。多变量分析显示，体重指数（BMI）、肿瘤与肛缘的距离以及直肠后系膜厚度是手术持续时间的独立预测因素。使用上述独立预测因素创建并评估了一个临床预测模型。受试者工作特征（ROC）分析结果显示了预测模型具有足够的判别能力。我们的研究表明，通过获取临床和磁共振成像参数（肛缘与肿瘤的距离以及直肠后系膜厚度）来预测手术难度是可行的，这些预测可能有助于做出临床决策。
