Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI.
Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI.
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DOI：
10.3389/fonc.2022.1068893
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发表时间：
2022
影响因子：
4.7
通讯作者：
中科院分区：
文献类型：
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在前列腺活检中，多参数磁共振成像（mpMRI）和前列腺健康指数（PHI）可以预测具有临床意义的前列腺癌（csPCa）。为了预测csPCa的可能性，我们基于包括PHI和mpMRI的多变量模型创建了列线图。我们评估了315名男性谁是前列腺活检计划。我们使用前列腺成像报告和数据系统版本2（PI-RADS V2）评估mpMRI并在活检前优化PHI测试。单因素分析显示，PHI、PHID和PI-RADS的最佳阈值分别为77.77、2.36和3，可用于csPCa的诊断。使用PI-RADS、游离PSA（fPSA）、PHI和前列腺体积开发了预测csPCa的多变量logistic模型。包括PI-RADS、fPSA、PHI和前列腺体积的多变量模型具有最佳准确性（AUC：0.882）。决策曲线分析（DCA），这是为了验证诺模图的临床适用性，显示了理想的优势（13.35%，高于模型，包括PI-RADS只有）。总之，基于PHI和mpMRI的列线图是预测csPCa的有价值的工具，同时尽可能避免不必要的活检。
On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa). To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies. We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram’s clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only). In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.
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影响因子：
4.5
作者：
Druskin, Sasha C.;Tosoian, Jeffrey J.;Pavlovich, Christian P.
通讯作者：
Pavlovich, Christian P.
影响因子：
3.4
作者：
Hsieh, Po-Fan;Li, Wei-Juan;Wu, Hsi-Chin
通讯作者：
Wu, Hsi-Chin
影响因子：
4.8
作者：
Heidegger, I.;Klocker, H.;Bektic, J.
通讯作者：
Bektic, J.
前列腺健康指数和多参数MRI：与前列腺癌过度诊断和过度治疗的犯罪伙伴。
影响因子：
5.2
作者：
Ferro M;Crocetto F;Bruzzese D;Imbriaco M;Fusco F;Longo N;Napolitano L;La Civita E;Cennamo M;Liotti A;Lecce M;Russo G;Insabato L;Imbimbo C;Terracciano D
通讯作者：
Terracciano D
DOI：
10.1056/nejmoa1801993
发表时间：
2018-05-10
期刊：
The New England journal of medicine
影响因子：
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作者：
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