An updated model for predicting side-specific extraprostatic extension in the era of MRI-targeted biopsy.

Prostate cancer and prostatic diseases(2024)

引用 0|浏览26
暂无评分
摘要
PURPOSE:Accurate prediction of extraprostatic extension (EPE) is pivotal for surgical planning. Herein, we aimed to provide an updated model for predicting EPE among patients diagnosed with MRI-targeted biopsy. MATERIALS AND METHODS:We analyzed a multi-institutional dataset of men with clinically localized prostate cancer diagnosed by MRI-targeted biopsy and subsequently underwent prostatectomy. To develop a side-specific predictive model, we considered the prostatic lobes separately. A multivariable logistic regression analysis was fitted to predict side-specific EPE. The decision curve analysis was used to evaluate the net clinical benefit. Finally, a regression tree was employed to identify three risk categories to assist urologists in selecting candidates for nerve-sparing, incremental nerve sparing and non-nerve-sparing surgery. RESULTS:Overall, data from 3169 hemi-prostates were considered, after the exclusion of prostatic lobes with no biopsy-documented tumor. EPE was present on final pathology in 1,094 (34%) cases. Among these, MRI was able to predict EPE correctly in 568 (52%) cases. A model including PSA, maximum diameter of the index lesion, presence of EPE on MRI, highest ISUP grade in the ipsilateral hemi-prostate, and percentage of positive cores in the ipsilateral hemi-prostate achieved an AUC of 81% after internal validation. Overall, 566, 577, and 2,026 observations fell in the low-, intermediate- and high-risk groups for EPE, as identified by the regression tree. The EPE rate across the groups was: 5.1%, 14.9%, and 48% for the low-, intermediate- and high-risk group, respectively. CONCLUSION:In this study we present an update of the first side-specific MRI-based nomogram for the prediction of extraprostatic extension together with updated risk categories to help clinicians in deciding on the best approach to nerve-preservation.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要