Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy.
Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy.
Urol Oncol. 2024 May;42(5):159.e9-159.e16. doi: 10.1016/j.urolonc.2024.01.033. Epub 2024 Feb 29.
To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.
All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).
Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them.
We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.
开发一种基于微超声的列线图,纳入临床病理参数和微超声表现,以预测前列腺外延伸的存在并指导神经保留的分级。
所有患者在机器人辅助前列腺根治术前一天接受微超声检查。单变量分析中与前列腺外延伸显著相关的变量被用于建立多变量逻辑模型,回归系数被用于建立列线图。该模型经过 1000 次自举重采样进行内部验证。使用受试者工作特征(ROC)曲线下面积(AUC)、校准图和决策曲线分析(DCA)评估基于微超声的模型性能。
总体而言,295 例患者中有 122 例(41.4%)在明确病理诊断为前列腺外延伸。微超声正确识别 84/122 例(68.9%)前列腺外延伸病例,其敏感性和特异性分别为 68.9%和 84.4%,AUC 为 76.6%。经过 1000 次自举重采样,基于微超声的模型预测准确性为 85.9%。校准图显示预测概率与前列腺外延伸的观察频率之间具有较好的一致性。DCA 显示与仅包括临床参数的模型相比,具有更高的临床净获益。考虑 4%的截断值,建议在 173 例(58.6%)患者中保留神经,其中 32 例(18.5%)检测到前列腺外延伸。
我们开发了一种基于微超声的前列腺外延伸预测列线图,可辅助决定是否保留或不保留神经血管束。外部验证和与 mpMRI 列线图的直接比较对于证实我们的结果至关重要。