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1.
目的 探讨基于第2版前列腺影像报告和数据系统(PI-RADS v2)评分的外周带前列腺癌多参数MRI(mp-MRI)与前列腺临床显著癌及Gleason评分的相关性。方法 回顾性分析T2WI、扩散加权成像(DWI)和动态增强MRI(DCE-MRI)诊断且经穿刺病理证实的91例外周带前列腺癌(阳性组)、70例前列腺增生和21例前列腺炎(阴性组)患者,采用PI-RADS v2评分标准进行盲法评分,分析其结果与外周带前列腺临床显著癌及Gleason评分的相关性,并以受试者工作特征(ROC)曲线分析PI-RADS v2评分诊断外周带前列腺临床显著癌的敏感度、特异度和准确率。结果 PI-RADS v2评分诊断外周带前列腺临床显著癌的曲线下面积(AUC)为0.965;评分为4分时约登指数最大(0.877),诊断前列腺临床显著癌的敏感度、特异度和准确率分别为98.61%、89.09%和92.86%。以PI-RADS v2评分3分为穿刺指征,使36.26%(66/182)患者免于不必要的穿刺,但漏诊2例非临床显著前列腺癌。PI-RADS v2评分与外周带前列腺临床显著癌呈高度正相关(r=0.853,P<0.01),与外周带前列腺癌Gleason评分呈高度正相关(r=0.816,P<0.01)。结论 PI-RADS v2评分与外周带前列腺临床显著癌及Gleason评分呈高度正相关,诊断前列腺临床显著癌准确率高,并可初步评估肿瘤分化程度,有助于减少不必要的前列腺穿刺。  相似文献   

2.
目的 建立基于前列腺影像报告和数据系统第2版(PI-RADS v2)的支持向量机(SVM)、决策树(DT)和Logistic回归3种机器学习模型,评价上述模型对高级别前列腺癌的诊断价值。方法 回顾性分析于我院接受前列腺多参数MR扫描并取得病理结果的194例患者的资料,其中高级别癌63例,非高级别癌131例。将评价因素(PI-RADS v2评分、年龄、游离前列腺特异抗原、前列腺特异性抗原比值、前列腺特异抗原密度)录入SVM、DT和Logistic回归3种机器学习模型进行诊断,通过ROC曲线评价PI-RADS v2评分和3种机器学习模型诊断高级别前列腺癌的价值。结果 PI-RADS v2、SVM、DT和Logistic回归模型诊断高级别前列腺癌的敏感度分别为72.73%、69.09%、87.27%和70.91%;特异度分别为87.29%、93.22%、93.22%和95.76%。DT模型诊断高级别前列腺癌ROC的AUC(AUC=0.90,P<0.01)最大,且与PI-RADS v2评分、SVM、Logistic回归比较差异均有统计学意义(P均<0.05)。结论 PI-RADS v2评分、SVM、DT和Logistic回归模型诊断高级别前列腺癌的价值均较好。  相似文献   

3.
目的 建立第2版前列腺影像报告和数据系统(PI-RADS v2)评分联合前列腺特异性抗原(PSA)的Logistic回归预测模型,评价其对移行区前列腺癌(PCa)的诊断价值。方法 回顾性分析经病理证实的移行区前列腺腺癌(PCa组,n=33)和良性前列腺增生或前列腺炎(非PCa组,n=54)患者的术前MRI及PSA资料。采用PI-RADS v2对2组进行评分(由低至高评为1~5分)。分析2组的PI-RADS v2评分、总PSA(t-PSA)、游离PSA(f-PSA)与t-PSA比值(f-PSA/t-PSA)及PSA密度(PSAD)的差异,选择有统计学意义的指标为自变量,以病理结果是否为PCa为因变量,建立3项Logistic回归模型:PI-RADS v2+t-PSA(A);PI-RADS v2+f-PSA/t-PSA(B);PI-RADS v2+PSAD(C)。建立Logistic回归模型产生的Logit(P)和PI-RADS v2评分的ROC曲线,评估其诊断效能。结果 2组t-PSA、f-PSA/t-PSA、PSAD及PI-RADS v2评分差异均有统计学意义(P均<0.01)。A、B、C Logistic回归预测模型分别为:Logit(P)=-8.682+1.507 PI-RADS v2+0.234 t-PSA(χ2=65.993,P<0.01);Logit(P)=-5.425+1.906 PI-RADS v2-13.921 f-PSA/t-PSA(χ2=65.993,P<0.01);Logit(P)=-7.534+1.045 PI-RADS v2+13.318 PSAD(χ2=74.036,P<0.01)。以A、B、C模型产生的Logit(P)预测病理结果,其ROC曲线下面积分别为0.945、0.919、0.960,均高于单独使用PI-RADS v2评分(AUC为0.861),差异有统计学意义(P均<0.01)。其中C模型诊断效能最佳,其敏感度、特异度分别为87.88%、92.59%。单独使用PI-RADS v2评分的敏感度、特异度分别为87.88%、77.78%。结论 联合PI-RADS v2评分和PSA指标的Logistic回归预测模型对移行区PCa的诊断效能优于单独使用PI-RADS v2评分,为可疑移行区PCa患者行穿刺活检提供了可靠的依据。  相似文献   

4.
目的 探讨前列腺特异性抗原密度(PSAD)对前列腺影像报告和数据系统第二版(PI-RADS v2)评分为3分患者临床决策中的应用价值。方法 收集接受前列腺穿刺且穿刺前MRI PI-RADS v2评分为3分的54例患者,根据穿刺病理结果分为前列腺癌(PCa)组(n=11)和良性组(n=43)。比较2组间总前列腺特异性抗原(TPSA)、游离前列腺特异性抗原(FPSA)、二者比值(F/T)及PSAD、前列腺体积、标识病灶体积的差异,并以ROC曲线分析PSAD诊断PI-RADS v2评分3分患者前列腺病灶良恶性的效能。结果 2组间PSAD差异有统计学意义(P=0.006),TPSA、FPSA、F/T、前列腺体积及标识病灶体积差异均无统计学意义(P均<0.05)。PSAD的ROC曲线下面积为0.771(P<0.05),以PSAD=0.25 ng/ml2为临界值,其诊断PI-RADS v2评分为3分患者前列腺病变良恶性的敏感度为72.73%(8/11),特异度为74.42%(32/43)。结论 PSAD可有效评估PI-RADS v2评分3分患者的患癌风险,以PSAD=0.25 ng/ml2筛查PI-RADS v2评分为3分的高危患者,可减少无效穿刺,提高穿刺阳性率。  相似文献   

5.
目的 观察灰区前列腺癌[前列腺特异性抗原(prostate specific antigen, PSA)4~10μg/L]患者PI-RADS v2.1评分及PSA衍生指标水平,探讨二者联合对灰区前列腺癌的诊断价值。方法 116例前列腺疾病患者均行前列腺穿刺活检病理检查,其中前列腺癌33例为前列腺癌组,良性前列腺增生或前列腺炎83例为非前列腺癌组。2组前列腺穿刺活检前均行前列腺多参数MRI检查,对MRI图像行PI-RADS v2.1评分。采用电化学发光法检测总PSA(total PSA, tPSA)、游离PSA(free PSA, fPSA)水平,计算f/tPSA(fPSA/tPSA)。比较2组年龄、tPSA、fPSA、f/tPSA、前列腺体积、PSA密度(PSA density, PSAD)及PI-RADS v2.1评分。采用多因素logistic回归分析诊断灰区前列腺癌的影响因素。绘制ROC曲线,评估PSAD、PI-RADS v2.1评分单独及联合诊断灰区前列腺癌的效能。结果 前列腺癌组f/tPSA[0.13(0.12, 0.20)]低于非前列腺癌组[0.18(0.13, 0.24)...  相似文献   

6.
目的 探究前列腺影像报告和数据系统第二版(PI-RADS v2)评分联合表观扩散系数(ADC)对前列腺癌的诊断价值。方法 选取2017年7月—2021年7月治疗的80例前列腺疾病患者为研究对象,根据前列腺穿刺活检结果分为前列腺癌组51例和非前列腺癌组29例。所有患者在病情稳定状况下进行MRI平扫,测量ADC值,进行PI-RADS v2评分。比较2组PI-RADS v2评分及ADC值,采用受试者工作特征曲线评估PI-RADS v2评分联合ADC值对前列腺癌的诊断效能。结果 前列腺癌组PI-RADS v2评分明显高于非前列腺癌组,ADC值明显低于非前列腺癌组(P<0.01)。PI-RADS v2评分(阈值为4分)诊断前列腺癌的敏感度为82.35%,特异度为75.86%,准确度为80.00%,曲线下面积(AUC)为0.829;ADC值(阈值为850×10-6 mm2/s)诊断前列腺癌的敏感度为78.43%,特异度为79.31%,准确度为78.75%,AUC为0.816;二者联合诊断前列腺癌的敏感度为92.16%,特异度为89.66%,准确度为...  相似文献   

7.
目的旨在探讨多参数MRI(multi-parametric MRI,Mp-MRI)前列腺影像报告和数据系统(prostate imaging reporting and data system version 2,PI-RADS V2)评分与经直肠超声引导下穿刺病理的相关性。材料与方法回顾性分析经病理证实的128例前列腺病变患者的MRI资料,其中前列腺癌75例,良性前列腺增生48例、前列腺炎5例,所有患者均行3.0 T MRI扫描,获取完整的T2WI、DWI及DCE图像;由2名前列腺诊断医师在不知患者临床资料及病理的情况下采用PIRADS V2评分标准进行评分,评分结果分别记录;所有患者均行经直肠超声引导下病理穿刺,并由泌尿专业病理诊断医师进行诊断,对前列腺癌则进行Gleason评分。采用Spearman相关分析PI-RADS V2评分与穿刺病理的相关系数,并采用ROC曲线分析PI-RADS V2评分诊断前列腺癌的敏感性、特异性和准确性。结果 PI-RADS V2评分与穿刺病理呈正相关,r=0.887。PI-RADS V2评分诊断前列腺癌的ROC曲线下面积0.975,其敏感性为93.33%,特异性为96.23%,准确性为94.51%,阳性预测值97.22%,阴性预测值91.07%。Gleason评分≥8分的前列腺癌的PI-RADS V2评分为5分。结论 PI-RADS V2评分与经直肠超声引导下穿刺病理的相关性高,PI-RADS V2评分对前列腺疾病的诊断准确性高。  相似文献   

8.
目的 分析PI-RADS v2.1与v2对前列腺癌的评分变化。材料与方法 回顾性分析2012年11月至2017年7月行前列腺多参数磁共振成像患者资料53例。以前列腺逐层切片病理为金标准,入组癌灶数量89例。由两名影像科医师按照PI-RADS v2.1和v2标准进行分别评分。结果 总体上PI-RADS评分发生变化的癌灶11.2%,移行带占比20.5%、外周带占比4%。PI-RADS≥3病灶比例分别为73.0%、70.8%,差异无统计学意义(P>0.05)。PI-RADS v2.1与v2对所有、移行带及外周带临床显著癌的ROC曲线下面积分别为0.873、0.867、0.841与0.895、0.878、0.884,且差异均无统计学意义(Z=1.098,P=0.272;Z=0.301,P=0.763;Z=1.231,P=0.218)。结论 PI-RADS v2.1与v2最终评分变化主要发生在移行带。PI-RADS≥3分的病例数量无明显变化,对临床决策影响不大。  相似文献   

9.
目的初步探讨3.0 T磁共振成像条件下,前列腺影像报告和数据系统第2版(prostate image report and data system version 2,PI-RADS V2)评分诊断方法在前列腺中央腺体癌诊断中的应用价值。材料与方法回顾性分析50例经病理证实的前列腺癌(prostate cancer,Pca)患者的多参数磁共振成像(mutli-parameter magnetic resonance imaging,Mp-MRI)资料和临床资料。根据6分区切割模型进行前列腺中央腺体分区。两位观察者根据PI-RADS V2评分标准及常规阅片,对入组病例Mp-MRI前列腺图像的有效预定义分区进行评分,分析评分结果的一致性。评分结果与该分区相应的病理结果进行对照,分析PI-RADS V2、常规阅片对前列腺中央腺体诊断中的敏感度、特异度、准确度、阳性预测值和阴性预测值,评价PI-RADS V2在前列腺中央腺体癌的诊断效能。结果 50例患者的Mp-MRI前列腺图像共分割为300个前列腺中央腺体分区,获得有效预定义的分区238个。结果显示,2位观察者PI-RADS V2诊断结果一致性极佳(K=0.84)。PI-RADS V2评分"4"分为诊断界值时,诊断结果准确度为79.2%,敏感度为70.4%,特异度为83.8%。常规阅片诊断结果准确度为72.7%,敏感度为49.7%,特异度为92.3%。PI-RADS V2评分诊断效能优于常规阅片。结论在3.0 T磁共振成像系统,Mp-MRI前列腺中央腺体癌诊断中,应用PI-RADS V2进行评分诊断结果的一致性较高,具有较好的诊断效能和临床应用价值。  相似文献   

10.
目的 探讨动态对比增强MRI (DCE-MRI)鉴别低级别与高级别前列腺癌的价值。方法 回顾性分析经前列腺癌根治术后病理证实并于术前接受前列腺DCE-MRI的26例前列腺癌患者的资料,根据病理结果分为低级别组(n=10)和高级别组(n=16),测量并比较2组间前列腺癌转运常数(Ktrans)、速率常数(Kep)及血管外细胞外间隙体积百分数(Ve)的差异,绘制ROC曲线,评价各参数值鉴别低级别与高级别前列腺癌的诊断效能,并分析各参数与Gleason评分的相关性。结果 低级别前列腺癌组Ktrans、Kep及Ve值分别为(0.22±0.07)/min、(1.24±0.57)/min和0.21±0.08,高级别组分别为(0.36±0.10)/min、(1.82±0.66)/min和0.21±0.10,2组间Ktrans及Kep值差异均有统计学意义(P均<0.05),Ve值差异无统计学意义(P=0.994)。Ktrans、Kep值区分前列腺高级别癌和低级别癌的ROC曲线下面积分别为0.872和0.737。前列腺癌Ktrans、Kep、Ve值与Gleason评分均无相关(P均>0.05)。结论 DCE-MRI定量参数Ktrans和Kep有助于鉴别低级别与高级别前列腺癌。  相似文献   

11.
BACKGROUNDProstate cancer (PCa) is one of the most common cancers among men. Various strategies for targeted biopsy based on multiparametric magnetic resonance imaging (mp-MRI) have emerged, which may improve the accuracy of detecting clinically significant PCa in recent years.AIMTo investigate the diagnostic efficiency of a template for cognitive MRI-ultrasound fusion transperineal targeted plus randomized biopsy in detecting PCa.METHODSData from patients with an increasing prostate-specific antigen (PSA) level but less than 20 ng/mL and at least one lesion suspicious for PCa on MRI from December 2015 to June 2018 were retrospectively analyzed. All patients underwent cognitive fusion transperineal template-guided targeted biopsy followed by randomized biopsy outside the targeted area. A total of 127 patients with complete data were included in the final analysis. A multivariable logistic regression analysis was conducted, and a two-sided P < 0.05 was considered statistically significant.RESULTSPCa was detected in 66 of 127 patients, and 56 cases presented clinically significant PCa. Cognitive fusion targeted biopsy alone detected 59/127 cases of PCa, specifically 52/59 cases with clinically significant PCa and 7/59 cases with clinically insignificant PCa. A randomized biopsy detected seven cases of PCa negative on targeted biopsy, and four cases had clinically significant PCa. PSA density (OR: 1.008, 95%CI: 1.003-1.012, P = 0.001; OR: 1.006, 95%CI: 1.002-1.010, P = 0.004) and Prostate Imaging-Reporting and Data System (PI-RADS) scores (both P < 0.001) were independently associated with the results of cognitive fusion targeted biopsy combined with randomized biopsy and targeted biopsy alone.CONCLUSIONThis single-centered study proposed a feasible template for cognitive MRI-ultrasound fusion transperineal targeted plus randomized biopsy. Patients with higher PSAD and PI-RADS scores were more likely to be diagnosed with PCa.  相似文献   

12.
目的探讨基于前列腺影像报告和数据系统版本2.1(PI-RADS V2.1)评分联合前列腺特异性抗原(PSA)及其他临床变量构建诺莫图对PSA"灰区"前列腺癌的预测价值。方法回顾性分析陕西省人民医院2016年10月~2021年10月204例行多参数MRI检查并有病理学结果的患者资料。单因素和多因素Logistic回归分析患者的年龄、体质指数(BMI)、总PSA(tPSA)、游离PSA(fPSA)、游离/总PSA(f/tPSA)、PSA密度(PSAD)、前列腺体积、直肠指诊(DRE)、PI-RADS V2.1评分与前列腺癌的相关性。确定预测PSA"灰区"前列腺癌的独立危险因素,使用R软件构建诺莫图预测模型。并通过绘制ROC曲线比较诺莫图模型与其他独立预测因素的诊断效能。结果PSAD、体积、PI-RADS V2.1评分、年龄和tPSA是预测"灰区"前列腺癌的独立危险因素。基于上述5个临床指标构建的诺莫图模型AUC值最高,为0.896。其他临床独立指标中,PI-RADS V2.1评分AUC值最高(0.854),年龄、体积、PSAD、tPSA的AUC值分别为0.625、0.706、0.739、0.615。结论基于PI-RADS V2.1联合临床指标建立的个体化诺莫图预测模型,可用于预测血清PSA处于"灰区"的前列腺癌,有助于减少不必要的穿刺。  相似文献   

13.
BackgroundIn patients with suspected prostate cancer (PCa) according to current guidelines systematic transrectal ultrasound (TRUS)-guided biopsy of the prostate is performed to verify or rule out PCa. However, TRUS-guided biopsy can result in underdetection of clinically significant cancers as well as diagnosis of clinically insignificant cancers. Multiparametric MRI (mpMRI) might improve the diagnostic pathway and help to avoid unnecessary biopsies.Design and methodsThe PROKOMB (Prostata – Kooperatives MRT-Projekt Berlin) study is a prospective two-arm multicentre study designed to evaluate the potential role of mpMRI as a triage test before biopsy. Up to 600 biopsy-naïve men with suspicion for PCa undergo mpMRI at two dedicated imaging centers. Only patients with equivocal or suspicious lesions on mpMRI undergo prostate biopsy including systematic as well as MRI-guided targeted biopsies at several different community-based urologists or hospitals. The PROKOMB study is designed to evaluate how many biopsies can be avoided, how many clinically insignificant cancers are diagnosed on prostate biopsy in patients with positive findings on mpMRI, and how many clinically significant cancers are missed using this alternative diagnostic pathway. For the purpose of this study clinically significant PCa is defined as Gleason ≥ 3 + 4 cancer. In addition, the detection rates of different techniques for MRI-guided biopsy are evaluated as well as psychological distress before mpMRI and after the diagnosis of PCa.ConclusionThe PROKOMB study might help in defining the role of mpMRI in biopsy-naïve patients with suspected PCa in an ambulatory care setting.  相似文献   

14.
目的 观察超声多模态影像诊断前列腺癌(PC)及指导经直肠超声(TRUS)引导下靶向穿刺的价值。方法 收集128例前列腺病变患者,包括61例PC及67例良性病变;根据前列腺影像报告和数据系统(PI-RADS)将其分为A组(PI-RADS 3分,n=64)、B组(PI-RADS 4分,n=29)及C组(PI-RADS 5分,n=35),观察TRUS、经直肠实时弹性成像(TRTE)及经直肠超声造影(TR-CEUS)联合诊断PC的效能,分析其用于TRUS引导下靶向穿刺的价值。结果 以TRUS、TRTE及TR-CEUS中存在任意2项阳性结果为截断值,超声多模态影像诊断PC的准确率、敏感度、特异度、阳性预测值、阴性预测值及曲线下面积分别为73.44%、77.05%、70.15%、70.15%、77.05%及0.766[95%CI(0.686,0.845)]。随病灶PI-RADS得分升高,超声多模态影像诊断PC的敏感度呈大致下降、而特异度呈上升趋势。结论 超声多模态影像有助于诊断PC及制定穿刺活检策略。  相似文献   

15.
Abdominal Radiology - To determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2...  相似文献   

16.
Purpose

To validate a novel consensus method, called target-in-target, combining human analysis of mpMRI with automated CAD system analysis, with the aim to increasing the prostate cancer detection rate of targeted biopsies.

Methods

A cohort of 420 patients was enrolled and 253 patients were rolled out, due to exclusion criteria. 167 patients, underwent diagnostic 3T MpMRI. Two expert radiologists evaluated the exams adopting PI-RADSv2 and CAD system. When a CAD target overlapped with a radiologic one, we performed the biopsy in the overlapping area which we defined as target-in-target. Targeted TRUS-MRI fusion biopsy was performed in 63 patients with a total of 212 targets. The MRI data of all targets were quantitatively analyzed, and diagnostic findings were compared to pathologist’s biopsy reports.

Results

CAD system diagnostic performance exhibited sensitivity and specificity scores of 55.2% and 74.1% [AUC = 0.63 (0.54 ÷ 0.71)] , respectively. Human readers achieved an AUC value, in ROC analysis, of 0.71 (0.63 ÷ 0.79). The target-in-target method provided a detection rate per targeted biopsy core of 81.8 % vs. a detection rate per targeted biopsy core of 68.6 % for pure PI-RADS based on target definitions. The higher per-core detection rate of the target-in-target approach was achieved irrespective of the presence of technical flaws and artifacts.

Conclusions

A novel consensus method combining human reader evaluation with automated CAD system analysis of mpMRI to define prostate biopsy targets was shown to improve the detection rate per biopsy core of TRUS-MRI fusion biopsies. Results suggest that the combination of CAD system analysis and human reader evaluation is a winning strategy to improve targeted biopsy efficiency.

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17.
目的探讨多参数MRI(mpMRI)对前列腺癌精囊侵犯(SVI)的诊断价值。方法 52例前列腺癌患者行术前mpMRI检查,由一名影像科医师综合T_2WI、扩散加权成像(DWI)和动态对比增强(DCE)影像表现,评价左、右侧精囊是否受累,并与前列腺根治术后病理结果进行对比。结果病理证实104侧精囊中有15侧精囊受累,读片者准确识别11侧,漏诊4侧,误诊1侧。mpMRI诊断准确率、敏感度、特异度、阴性预测值和阳性预测值分别为95.2%、73.3%、98.9%、95.7%和91.7%,与病理结果一致性良好(K_(appa)值=0.794)。结论 mpMRI对前列腺癌精囊侵犯具有较高的临床价值。  相似文献   

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