A Predictive Model for Surgical Approach Selection in Robotic Partial Nephrectomy and Its Perioperative Outcomes Based on Single-Center Retrospective Data

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-02-11 DOI:10.1002/cam4.70625

Fan Shu, Zhuo Liu, Peichen Duan, Yichang Hao, Xin Ma, Hongxian Zhang, Guoliang Wang, Xiaojun Tian, Lei Liu, Shudong Zhang

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引用次数: 0

Abstract

Objectives

To compare the perioperative and postoperative outcomes of transperitoneal and retroperitoneal robotic partial nephrectomy (RPN) and develop a prediction model for reference to select the approach.

Materials and Methods

We retrospectively reviewed our single-institutional RPN database. The patients were divided into training and validation sets. In training set, transperitoneal and retroperitoneal cases were matched using propensity score matching to balance confounding factors. The intraoperative and postoperative outcomes of both approaches were compared. A prediction model was constructed to predict the probability of the retroperitoneal approach. The model was then externally validated using the data from the validation set.

Results

A total of 318 patients were included in the training set, and after propensity score matching, 200 cases were left. Additionally, 92 patients were included in the validation set. The estimated blood loss (p = 0.021) and the hemoglobin change (p = 0.016) were greater in the transperitoneal group. There was no significant difference in operative time (p = 0.539), warm ischemia time (p = 0.678), hospitalization time (p = 0.673), extubation time (p = 0.621), creatinine change (p = 0.623), negative margin (p = 1), local recurrence (p = 1), postoperative complication (p = 0.229), long-term creatinine (p = 0.158), and overall survival (p = 0.671) between the two groups. Tumor diameter, anteroposterior location, longitudinal location, and accessory renal artery were employed as variables to construct the prediction model, resulting in area under the curve values of 0.84 and 0.77, respectively, during internal and external validation.

Conclusions

Retroperitoneal and transperitoneal approaches of RPN showed no difference in perioperative outcomes except estimated blood loss and hemoglobin change. The retroperitoneal approach is recommended for smaller tumors located in the upper pole or posterior and the presence of an accessory renal artery. Our model is available to predict the probability of the retroperitoneal approach.

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基于单中心回顾性数据的机器人肾部分切除术手术入路选择预测模型及其围手术期预后

目的比较经腹膜和后腹膜机器人肾部分切除术（RPN）围手术期和术后预后，建立预测模型，为选择手术入路提供参考。材料和方法我们回顾性地回顾了我们的单一机构RPN数据库。将患者分为训练组和验证组。在训练集中，使用倾向评分匹配来平衡混杂因素，对经腹膜和后腹膜病例进行匹配。比较两种入路的术中、术后效果。建立预测模型预测腹膜后入路的可能性。然后使用验证集中的数据对模型进行外部验证。结果318例患者被纳入训练集，倾向评分匹配后，剩余200例。此外，92例患者被纳入验证集。经腹膜组估计失血量（p = 0.021）和血红蛋白变化（p = 0.016）更大。两组患者在手术时间（p = 0.539）、热缺血时间（p = 0.678）、住院时间（p = 0.673）、拔管时间（p = 0.621）、肌酐变化（p = 0.623）、阴性切度（p = 1）、局部复发（p = 1）、术后并发症（p = 0.229）、长期肌酐（p = 0.158）、总生存（p = 0.671）方面差异均无统计学意义。以肿瘤直径、前后位、纵向位、副肾动脉为变量构建预测模型，内外部验证曲线下面积分别为0.84、0.77。结论经腹膜后入路和经腹膜入路RPN围手术期预后除估计失血量和血红蛋白变化外无差异。对于位于肾上极或后极且存在副肾动脉的较小肿瘤，推荐采用腹膜后入路。我们的模型可用于预测腹膜后入路的可能性。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.