Table_1_An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer.docx

ObjectiveThe current work aimed to develop a nomogram comprised of MRI-based pelvimetry and clinical factors for predicting the difficulty of rectal surgery for middle and low rectal cancer (RC).
MethodsConsecutive mid to low RC cases who underwent transabdominal resection between June 2020 and August 2021 were retrospectively enrolled. Univariable and multivariable logistic regression analyses were carried out for identifying factors (clinical factors and MRI-based pelvimetry parameters) independently associated with the difficulty level of rectal surgery. A nomogram model was established with the selected parameters for predicting the probability of high surgical difficulty. The predictive ability of the nomogram model was assessed by the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).
ResultsA total of 122 cases were included. BMI (OR = 1.269, p = 0.006), pelvic inlet (OR = 1.057, p = 0.024) and intertuberous distance (OR = 0.938, p = 0.001) independently predicted surgical difficulty level in multivariate logistic regression analysis. The nomogram model combining these predictors had an area under the ROC curve (AUC) of 0.801 (95% CI: 0.719–0.868) for the prediction of a high level of surgical difficulty. The DCA suggested that using the nomogram to predict surgical difficulty provided a clinical benefit.
ConclusionsThe nomogram model is feasible for predicting the difficulty level of rectal surgery, utilizing MRI-based pelvimetry parameters and clinical factors in mid to low RC cases.

研究目的：本研究旨在构建一款列线图（nomogram），整合基于磁共振成像（MRI）的骨盆测量参数与临床因素，用于预测中低位直肠癌（RC）的直肠手术难度。
研究方法：回顾性纳入2020年6月至2021年8月期间接受经腹切除术的连续中低位直肠癌病例。开展单因素及多因素logistic回归分析，筛选与直肠手术难度等级独立相关的影响因素（涵盖临床因素与基于MRI的骨盆测量参数）。基于筛选得到的参数构建列线图模型，以预测高手术难度的发生概率。通过受试者工作特征（ROC）曲线与决策曲线分析（DCA）评估该列线图模型的预测效能。
研究结果：本研究共纳入122例病例。多因素logistic回归分析显示，体质量指数（BMI，OR=1.269，P=0.006）、骨盆入口径（OR=1.057，P=0.024）与坐骨结节间径（OR=0.938，P=0.001）为手术难度等级的独立预测因子。整合上述预测因子的列线图模型，用于预测高手术难度时的受试者工作特征曲线下面积（AUC）为0.801（95%置信区间：0.719~0.868）。决策曲线分析结果表明，采用该列线图预测手术难度可带来临床获益。
研究结论：针对中低位直肠癌病例，整合基于MRI的骨盆测量参数与临床因素所构建的列线图模型，可有效预测直肠手术难度等级，具备临床应用可行性。