Yu Le, Yan Ye, Chu Hongling, Deng Shaohui, Ye Jianfei, Wang Guoliang, Huang Yi, Zhang Fan, Zhang Shudong
Department of Urology, Peking University Third Hospital, Haidian District, Beijing, 100191, P.R. China.
Department of Urology, Peking University Third Hospital, 49 North Garden Road, Haidian, Beijing, 100191, P.R. China.
BMC Urol. 2025 Jan 10;25(1):4. doi: 10.1186/s12894-024-01682-7.
To propose the bladder mucosal smoothness (BMS) grade and validate a predictive model including MRI parameters preoperatively that can evaluate the early recovery of urinary continence (UC) after laparoscopic radical prostatectomy (LRP).
A retrospective analysis was conducted on 203 patients (83 patients experienced UI at the three-month follow-up) who underwent LRP in our medical center and were diagnosed with prostate cancer (PCa) from June 2016 to March 2020. Patients' clinicopathological data were collected. Prostate volume (PV), membranous urethra length (MUL), intravesical prostatic protrusion length (IPPL), and BMS grade were measured by MRI. The total sample was randomly divided into a training set (n = 142) and a validation set (n = 61). A model was developed to predict the risk of urinary incontinence (UI) at three months after LRP.
Age group, clinical T stage group, BMS grade group, PV group, IPPL group, and MUL group differed significantly between patients in the UI group and the UC group (all P values < 0.05). Multivariate analysis identified 3 MRI-related predictors selected for the prediction model: BMS grade (1 odds ratio [OR] 0.17, 95% CI 0.11-0.66; P value = 0.024) (2 + 3 OR 0.17, 95% CI 0.04-0.66; P value = 0.011), IPPL (> 5 mm OR 0.17, 95% CI 0.1-0.64; P = 0.004), and MUL (≥ 14 mm OR 6.41, 95% CI 2.72-15.09; P value < 0.001). The model achieved a highest area under the curve of 0.900 in the training set and the validation set. The sensitivity and specificity of the prediction model were 0.800 and 0.816.
Our study confirmed that patients with lower BMS grade are associated with early recovery of urinary continence after LRP. A prediction model was developed and validated to evaluate the early recovery of urinary continence after LRP.
Not applicable.
提出膀胱黏膜光滑度(BMS)分级,并验证一种术前包含MRI参数的预测模型,该模型可评估腹腔镜前列腺癌根治术(LRP)后尿失禁(UC)的早期恢复情况。
对2016年6月至2020年3月在本医疗中心接受LRP并被诊断为前列腺癌(PCa)的203例患者(83例患者在3个月随访时出现尿失禁)进行回顾性分析。收集患者的临床病理数据。通过MRI测量前列腺体积(PV)、膜部尿道长度(MUL)、膀胱内前列腺突出长度(IPPL)和BMS分级。将总样本随机分为训练集(n = 142)和验证集(n = 61)。建立一个模型来预测LRP后3个月尿失禁(UI)的风险。
UI组和UC组患者在年龄组、临床T分期组、BMS分级组、PV组、IPPL组和MUL组方面存在显著差异(所有P值<0.05)。多变量分析确定了3个与MRI相关的预测因子用于预测模型:BMS分级(1比值比[OR]0.17,95%CI 0.11 - 0.66;P值 = 0.024)(2 + 3 OR 0.17,95%CI 0.04 - 0.66;P值 = 0.011)、IPPL(>5 mm OR 0.17,95%CI 0.1 - 0.64;P = 0.004)和MUL(≥14 mm OR 6.41,95%CI 2.72 - 15.09;P值<0.001)。该模型在训练集和验证集中的曲线下面积最高达到0.900。预测模型的敏感性和特异性分别为0.800和0.816。
我们的研究证实,BMS分级较低的患者与LRP后尿失禁的早期恢复相关。建立并验证了一个预测模型以评估LRP后尿失禁的早期恢复情况。
不适用。