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Radiology. 2017 Apr;283(1):119-129. doi: 10.1148/radiol.2016161124. Epub 2016 Oct 26.
2
PI-RADS Version 2: A Pictorial Update.PI-RADS 版本 2:图像更新。
Radiographics. 2016 Sep-Oct;36(5):1354-72. doi: 10.1148/rg.2016150234. Epub 2016 Jul 29.
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Preoperative Multiparametric Magnetic Resonance Imaging Predicts Biochemical Recurrence in Prostate Cancer after Radical Prostatectomy.术前多参数磁共振成像可预测前列腺癌根治术后的生化复发
PLoS One. 2016 Jun 23;11(6):e0157313. doi: 10.1371/journal.pone.0157313. eCollection 2016.
4
Differentiation of prostatitis and prostate cancer using the Prostate Imaging-Reporting and Data System (PI-RADS).使用前列腺影像报告和数据系统(PI-RADS)鉴别前列腺炎和前列腺癌。
Eur J Radiol. 2016 Jul;85(7):1304-11. doi: 10.1016/j.ejrad.2016.04.014. Epub 2016 Apr 29.
5
Benign causes of diffusion restriction foci in the peripheral zone of the prostate: diagnosis and differential diagnosis.良性前列腺外周区弥散受限灶的病因:诊断与鉴别诊断。
Abdom Radiol (NY). 2016 May;41(5):910-8. doi: 10.1007/s00261-016-0719-6.
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Perineural Invasion in Prostate Cancer Is More Frequently Detected by Multiparametric MRI Targeted Biopsy Compared With Standard Biopsy.与标准活检相比,多参数磁共振成像靶向活检更常检测到前列腺癌的神经周围侵犯。
Am J Surg Pathol. 2016 Apr;40(4):490-4. doi: 10.1097/PAS.0000000000000546.
7
Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference.使用多参数磁共振成像检测临床显著性前列腺癌的更新版前列腺影像报告和数据系统(PIRADS v2)推荐意见:以全层病理作为参考标准的批判性评估
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8
Synopsis of the PI-RADS v2 Guidelines for Multiparametric Prostate Magnetic Resonance Imaging and Recommendations for Use.多参数前列腺磁共振成像PI-RADS v2指南概要及使用建议
Eur Urol. 2016 Jan;69(1):41-9. doi: 10.1016/j.eururo.2015.08.038. Epub 2015 Sep 8.
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[PI-RADS v2评分联合前列腺特异性抗原在诊断外周带前列腺癌中的价值:一项逻辑回归分析]

[Value of PI-RADS v2 scores combined with prostate specific antigen in diagnosis of peripheral zone prostate cancer: a logistic regression analysis].

作者信息

Lei Li-Zhi, Xu Yi-Kai, Hou Mei-Rong, He Meng-Qi

机构信息

Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. E-mail:

出版信息

Nan Fang Yi Ke Da Xue Xue Bao. 2017 Aug 20;37(8):1092-1097. doi: 10.3969/j.issn.1673-4254.2017.08.15.

DOI:10.3969/j.issn.1673-4254.2017.08.15
PMID:28801291
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6765728/
Abstract

OBJECTIVE

To assess the value of Prostate Imaging and Reporting and Data System: Version 2 (PI-RADS v2) combined with prostate specific antigen (PSA) in the diagnosis of peripheral zone (PZ) prostate cancer (PCa).

METHODS

The preoperative magnetic resonance imaging and PSA data were ananlyzed for 69 patients with pathologically confirmed PCa and 109 non-PCa patients. PI-RADS v2 scores (1-5) was used to evaluate the risk of PZ PCa. The total PSA (tPSA) level, free to total PSA ratio (f/t PSA), PSA density (PSAD), PZ-PSAD and PI-RADS v2 scores were compared between the PCa and non-PCa patients. Logistic regression models were established with parameters that differed significantly the two groups. The receiver opearting characteristics (ROC) curve was constructed based on the P values derived from the logical regression models and PI-RADS scores to assess the diagnostic efficiency.

RESULTS

PI-RADS v2 score, tPSA, f/t PSA, PSAD and PZ-PSAD differed significantly between the two groups (P<0.01). Four predictive multivariate models were established: Logit P=-6.825+1.024PI-RADS v2+ 0.223tPSA (A), Logit P=-4.354+1.586PI-RADS v2-12.7841f/tPSA (B), Logit P=-8.993+1.630PI-RADS v2+17.091PSAD (C), and Logit P=-9.434+1.596PI-RADS v2+10.494PZ-PSAD (D), whose area under the ROC curves was 0.908, 0.891, 0.944, and 0.961, respectively, all significantly greater than that of PI-RADS v2 score (P<0.05).

CONCLUSION

Compared with PI-RADS v2 score alone, the combination of PI-RADS v2 score and PSA in the logistic regression model can improve the diagnostic efficiency of PZ PCa and offers better confidence in the decision of biopsy in suspected cases.

摘要

目的

评估前列腺影像报告和数据系统第2版(PI-RADS v2)联合前列腺特异性抗原(PSA)在诊断外周带(PZ)前列腺癌(PCa)中的价值。

方法

分析69例经病理证实的PCa患者和109例非PCa患者的术前磁共振成像和PSA数据。采用PI-RADS v2评分(1 - 5分)评估PZ PCa的风险。比较PCa组和非PCa组患者的总PSA(tPSA)水平、游离PSA与总PSA比值(f/t PSA)、PSA密度(PSAD)、PZ-PSAD和PI-RADS v2评分。建立包含两组间有显著差异参数的逻辑回归模型。基于逻辑回归模型和PI-RADS评分得出的P值构建受试者操作特征(ROC)曲线,以评估诊断效率。

结果

两组间PI-RADS v2评分、tPSA、f/t PSA、PSAD和PZ-PSAD差异有统计学意义(P<0.01)。建立了四个预测多变量模型:Logit P = -6.825 + 1.024PI-RADS v2 + 0.223tPSA(A)、Logit P = -4.354 + 1.586PI-RADS v2 - 12.7841f/tPSA(B)、Logit P = -8.993 + 1.630PI-RADS v2 + 17.091PSAD(C)和Logit P = -9.434 + 1.596PI-RADS v2 + 10.494PZ-PSAD(D),其ROC曲线下面积分别为0.908、0.891、0.944和0.961,均显著大于PI-RADS v2评分(P<0.05)。

结论

与单独的PI-RADS v2评分相比,逻辑回归模型中PI-RADS v2评分与PSA联合可提高PZ PCa的诊断效率,并为疑似病例的活检决策提供更高的可信度。