A Novel Prediction Tool Based on Multiparametric Magnetic Resonance Imaging to Determine the Biopsy Strategy for Clinically Significant Prostate Cancer in Patients with PSA Levels Less than 50 ng/ml
A Novel Prediction Tool Based on Multiparametric Magnetic Resonance Imaging to Determine the Biopsy Strategy for Clinically Significant Prostate Cancer in Patients with PSA Levels Less than 50 ng/ml
复制标题
一种基于多参数磁共振成像的新型预测工具，用于确定 PSA 水平低于 50 ng/ml 的临床显着前列腺癌患者的活检策略
DOI：
10.1245/s10434-019-08111-2
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发表时间：
2020-04-01
影响因子：
3.7
通讯作者：
Wang, Hai-Feng
中科院分区：
文献类型：
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作者：
He, Bi-Ming;Shi, Zhen-Kai;Wang, Hai-Feng
目的开发和内部验证nomographic，以帮助在无活检，靶向活检（TB）或TB加系统活检（SB）中选择最佳活检策略。患者和方法本回顾性研究共纳入了385例2015年至2018年间在我院接受多参数MRI （mpMRI）检查后接受磁共振成像（MRI）引导的结核病和/或SB患者。我们建立了预测临床显著性前列腺癌（csPCa）的模型，该模型基于TB结果的可疑病变，以及基于TB加SB或仅SB结果的整个前列腺。采用logistic回归生成nomogram，并采用受试者工作特征（ROC）曲线分析、校准曲线和决策分析进行评价。采用ROC曲线和校准方法对同一研究所2018 - 2019年177例患者进行验证。结果在多变量分析中，前列腺特异性抗原水平、前列腺体积、前列腺影像学报告和数据系统评分是两种形态图中csPCa的预测因子。可疑病变的模型中还包括年龄，而整个腺体的模型中包括肥胖。在预测模型的ROC分析中，可疑病变的曲线下面积（AUC）为0.755，整个腺体的AUC为0.887。两种模型在校准和决策分析中都表现良好。在验证队列中，可疑病变nomogram和全腺体nomogram的ROC曲线auc分别为0.723和0.917。此外，校准曲线检测到两种模型的错误率都很低。结论该图具有良好的判别能力，并得到了验证。这些图可以用来选择最佳的活检策略为个别患者在未来。
Purpose To develop and internally validate nomograms to help choose the optimal biopsy strategy among no biopsy, targeted biopsy (TB) only, or TB plus systematic biopsy (SB). Patients and Methods This retrospective study included a total of 385 patients who underwent magnetic resonance imaging (MRI)-guided TB and/or SB at our institute after undergoing multiparametric MRI (mpMRI) between 2015 and 2018. We developed models to predict clinically significant prostate cancer (csPCa) based on suspicious lesions from a TB result and based on the whole prostate gland from the results of TB plus SB or SB only. Nomograms were generated using logistic regression and evaluated using receiver-operating characteristic (ROC) curve analysis, calibration curves and decision analysis. The results were validated using ROC curve and calibration on 177 patients from 2018 to 2019 at the same institute. Results In the multivariate analyses, prostate-specific antigen level, prostate volume, and the Prostate Imaging Reporting and Data System score were predictors of csPCa in both nomograms. Age was also included in the model for suspicious lesions, while obesity was included in the model for the whole gland. The area under the curve (AUC) in the ROC analyses of the prediction models was 0.755 for suspicious lesions and 0.887 for the whole gland. Both models performed well in the calibration and decision analyses. In the validation cohort, the ROC curve described the AUCs of 0.723 and 0.917 for the nomogram of suspicious lesions and nomogram of the whole gland, respectively. Also, the calibration curve detected low error rates for both models. Conclusion Nomograms with excellent discriminative ability were developed and validated. These nomograms can be used to select the optimal biopsy strategy for individual patients in the future.