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基于微超声的预测前列腺癌包膜外侵犯的侧别特异性列线图

Side-specific, Microultrasound-based Nomogram for the Prediction of Extracapsular Extension in Prostate Cancer.

作者信息

Pedraza Adriana M, Parekh Sneha, Joshi Himanshu, Grauer Ralph, Wagaskar Vinayak, Zuluaga Laura, Gupta Raghav, Barthe Flora, Nasri Jordan, Pandav Krunal, Patel Dhruti, Gorin Michael A, Menon Mani, Tewari Ashutosh K

机构信息

Department of Urology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.

Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Eur Urol Open Sci. 2022 Dec 28;48:72-81. doi: 10.1016/j.euros.2022.12.005. eCollection 2023 Feb.

Abstract

BACKGROUND

Prediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.

OBJECTIVE

To create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.

DESIGN SETTING AND PARTICIPANTS

Prospective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

ECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.

RESULTS AND LIMITATIONS

The areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.

CONCLUSIONS

We developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.

PATIENT SUMMARY

The integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

摘要

背景

预测包膜外侵犯(ECE)对于在肿瘤切除与神经组织保留之间取得平衡至关重要。在分期过程中,微型超声(MUS)是多参数磁共振成像(mpMRI)的一种有吸引力的替代方法。

目的

创建一个整合临床病理参数和MUS结果的侧别特异性列线图,以预测同侧ECE并指导神经保留。

设计、设置和参与者:前瞻性数据收集自2021年6月至2022年5月接受机器人辅助根治性前列腺切除术且术前行MUS和mpMRI的连续患者。分析共纳入391例患者和612个叶。

结局测量和统计分析

手术病理中的ECE是主要结局。进行多变量回归分析以确定ECE的预测因素。使用受试者工作特征曲线直观显示所得多变量模型的性能。基于基于MUS的模型的logit函数系数开发列线图。进行决策曲线分析(DCA)以评估临床实用性。

结果和局限性

内部验证后,基于MUS的模型的受试者工作特征曲线下面积(AUC)分别为81.4%和80.9%(95%置信区间[CI]75.6,84.6)。mpMRI模型的AUC也为80.9%(95%CI 77.2,85.7)。DCA显示了基于MUS的列线图的净临床益处及其与单独的MUS和MRI相比在检测ECE方面的优越性。我们研究的局限性包括样本量和读者间一致性中等。

结论

我们开发了一个基于临床病理变量和MUS结果预测ECE的侧别特异性列线图。其性能与基于mpMRI的模型相当。需要外部验证和前瞻性试验来证实我们的结果。

患者总结

临床参数与微型超声的整合能够预测包膜外侵犯,结果与基于磁共振成像结果的模型相似。这对于在手术期间调整神经保留可能有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2479/9895764/dddddf03ba26/gr1a.jpg

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