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Objective@#To explore a predictive nomogram for the result of prostate biopsy based on Prostate Imaging Reporting and Data System version 2(PI-RADS v2)combined with prostate specific antigen (PSA) and its related parameters, and to assess its ability to diagnose prostate cancer by internal validation.@*Methods@#We retrospectively analyzed the clinical data of 509 patients who underwent transrectal prostate biopsy guided by ultrasound during the period from January 2014 to December 2018 in the Department of Urology, First Affiliated Hospital of Xiamen University. In 509 cases, the mean age was (68.1±7.2) years. The mean prostate volume(PV) was (55.8±30.7) ml. The mean tPSA value was (19.86±18.94) ng/ml. The mean value of fPSA was (2.63±3.60) ng/ml and the mean f/tPSA was 0.14±0.08. The mean PSAD was (0.46±0.52) ng/ml2. Based on the PI-RADS v2, score 1 point have 37 cases, score 2 point have 131 cases, score 3 point have 152 cases, score 4 point have 102 cases, score 5 point have 87 cases. Of these patients, we randomly selected 80% (407 cases) as development group, and the other 20% (102 cases) as validation group. Univariate and multivariate logistic regression analysis of the development group was performed to identify the independent influence factors that can predict prostate cancer (PCa), thereby establishing a predictive model for the result of prostate biopsy. In the development group, validation group and tPSA was between 4.1-20.0 ng/ml, the model was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve and decision curve, and compared to PSA, fPSA, f/tPSA, PSAD, PI-RADS v2.@*Results@#Among the 509 patients enrolled in the study, the detection rate of PCa was 43.0% (219/509). In the development group, the logistic regression analysis demonstrated that patient age (OR=1.113), f/tPSA (OR=0.004), PV (OR=0.986), PSAD (OR=11.023), digital rectal examination (DRE) texture (OR=2.295), transabdominal ultrasound (TAUS) with or without hypoechoic (OR=2.089), and PI-RADS v2 (OR=1.920) were independent factors for PCa (P<0.05). The nomogram based on all variables was established. In the development group, the area under the curve (AUC) of the model (0.883) was greater than those of tPSA (0.686), fPSA (0.593), f/tPSA (0.626), PSAD (0.777), PI-RADS v2 (0.761). In the validation group, the area under the curve of the model (0.839) was greater than those of tPSA (0.758), fPSA (0.666), f/tPSA (0.648), PSAD (0.832), PI-RADS v2 (0.803). In patients whose tPSA was between 4.1-20.0 ng/ml, the area under the curve of the model (0.801) was greater than those of tPSA (0.570), fPSA (0.426), f/tPSA (0.657), PSAD (0.707), PI-RADS v2 (0.701). The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, and the Hosmer-Lemeshow showed thatχ2=5.434, P=0.710, both suggested that the prediction model had better calibration ability. The decision curve showed that the model based on PI-RADS v2 had high clinical application value.@*Conclusions@#The nomogram based on PI-RADS v2 had a high predictive value for prostate cancer and could significantly improve the diagnostic performance. It had better diagnostic value than PSA and its related parameters. It also provided important guidance for the prostate cancer on clinical treatment of patients to some extent.
ABSTRACT
Objective To explore a predictive nomogram for the result of prostate biopsy based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) combined with prostate specific antigen (PSA) and its related parameters,and to assess its ability to diagnose prostate cancer by internal validation.Methods We retrospectively analyzed the clinical data of 509 patients who underwent transrectal prostate biopsy guided by ultrasound during the period from January 2014 to December 2018 in the Department of Urology,First Affiliated Hospital of Xiamen University.In 509 cases,the mean age was (68.1 ± 7.2) years.The mean prostate volume (PV) was (55.8 ± 30.7) ml.The mean tPSA value was (19.86 ± 18.94) ng/ml.The mean value of fPSA was (2.63 ± 3.60) ng/ml and the mean f/tPSA was 0.14 ± 0.08.The mean PSAD was (0.46 ±0.52) ng/ml2.Based on the PI-RADS v2,score 1 point have 37 cases,score 2 point have 131 cases,score 3 point have 152 cases,score 4 point have 102 cases,score 5 point have 87 cases.Of these patients,we randomly selected 80％ (407 cases) as development group,and the other 20％ (102 cases) as validation group.Univariate and multivariate logistic regression analysis of the development group was performed to identify the independent influence factors that can predict prostate cancer (PCa),thereby establishing a predictive model for the result of prostate biopsy.In the development group,validation group and tPSA was between 4.1-20.0 ng/ml,the model was evaluated by analyzing the receiver operating characteristic (ROC) curve,calibration curve and decision curve,and compared to PSA,fPSA,f/tPSA,PSAD,PI-RADS v2.Results Among the 509 patients enrolled in the study,the detection rate of PCa was 43.0％ (219/509).In the development group,the logistic regression analysis demonstrated that patient age (OR =1.113),f/tPSA (OR =0.004),PV (OR =0.986),PSAD (OR =11.023),digital rectal examination (DRE) texture (OR =2.295),transabdominal ultrasound (TAUS) with or without hypoechoic (OR =2.089),and PI-RADS v2 (OR =1.920) were independent factors for PCa (P ＜0.05).The nomogram based on all variables was established.In the development group,the area under the curve (AUC) of the model (0.883) was greater than those of tPSA (0.686),fPSA (0.593),f/tPSA (0.626),PSAD (0.777),PI-RADS v2 (0.761).In the validation group,the area under the curve of the model (0.839) was greater than those of tPSA (0.758),fPSA (0.666),f/tPSA (0.648),PSAD (0.832),PI-RADS v2 (0.803).In patients whose tPSA was between 4.1-20.0 ng/ml,the area under the curve of the model (0.801) was greater than those of tPSA (0.570),fPSA (0.426),f/tPSA (0.657),PSAD (0.707),PI-RADS v2 (0.701).The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve,and the Hosmer-Lemeshow showed thatx2 =5.434,P =0.710,both suggested that the prediction model had better calibration ability.The decision curve showed that the model based on PI-RADS v2 had high clinical application value.Conclusions The nomogram based on PI-RADS v2 had a high predictive value for prostate cancer and could significantly improve the diagnostic performance.It had better diagnostic value than PSA and its related parameters.It also provided important guidance for the prostate cancer on clinical treatment of patients to some extent.
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<p><b>Background</b>One of the main aims of the updated Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) is to diminish variation in the interpretation and reporting of prostate imaging, especially among readers with varied experience levels. This study aimed to retrospectively analyze diagnostic consistency and accuracy for prostate disease among six radiologists with different experience levels from a single center and to evaluate the diagnostic performance of PI-RADS v2 scores in the detection of clinically significant prostate cancer (PCa).</p><p><b>Methods</b>From December 2014 to March 2016, 84 PCa patients and 99 benign prostatic shyperplasia patients who underwent 3.0T multiparametric magnetic resonance imaging before biopsy were included in our study. All patients received evaluation according to the PI-RADS v2 scale (1-5 scores) from six blinded readers (with 6 months and 2, 3, 4, 5, or 17 years of experience, respectively, the last reader was a reviewer/contributor for the PI-RADS v2). The correlation among the readers' scores and the Gleason score (GS) was determined with the Kendall test. Intra-/inter-observer agreement was evaluated using κ statistics, while receiver operating characteristic curve and area under the curve analyses were performed to evaluate the diagnostic performance of the scores.</p><p><b>Results</b>Based on the PI-RADS v2, the median κ score and standard error among all possible pairs of readers were 0.506 and 0.043, respectively; the average correlation between the six readers' scores and the GS was positive, exhibiting weak-to-moderate strength (r = 0.391, P = 0.006). The AUC values of the six radiologists were 0.883, 0.924, 0.927, 0.932, 0.929, and 0.947, respectively.</p><p><b>Conclusion</b>The inter-reader agreement for the PI-RADS v2 among the six readers with different experience is weak to moderate. Different experience levels affect the interpretation of MRI images.</p>
ABSTRACT
Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the 'gray zone' (4-10 ng ml-1). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density (PSAD) were independent predictors of PCa and CSPCa. A PI-RADS v2 score ≥4 provided high negative predictive values (91.39% for PCa and 95.69% for CSPCa). A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group (PI-RADS score = 5 and PSAD ≥0.15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score ≥4 with high tPSA level) with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.
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Objective To evaluate the value of the prostate imaging reporting and data system version 2 (PI-RADS version 2) for the diagnosis of prostate cancer.Methods A total of 243 patients who underwent multiparametric 3T prostate MRI followed by prostate biopsy or radical prostatectomy were included.111 patients were in PSA gray zone(4.0-10.0 ng/ml).PI-RADS version 2 scores for each patient was assigned by two readers independently.Reference standard was obtained by histopathology.Positive predictive value (PPV) for prostate cancer and clinically significant cancer were compared among patients with different PI-RADS Version 2 scores using chi-square trend test.Receiver operating characteristic (ROC) curve was performed to assess diagnostic accuracy of the PI-RADS version 2 scores for prostate cancer detection,and evaluate the difference in diagnostic efficiency between transition zone and peripheral zone.Results Two hundred and eighty five suspicious foci from the 243 patients were finally recruited to this study,131 of which were diagnosed as prostate cancer according to pathology.There was significant difference in PPV for prostate cancer and clinically significant cancer among patients with different PI-RADS version 2 scores (score 1:8.0％;score 2:10.1％;score 3:49.2％;score 4:61.1％ score 5:87.9％,P＜0.01),(score 1:0;score 2:5.1％;score 3:31.1％;score 4:59.3％ score 5:88.9％,P ＜ 0.01).When PI-RADS version 2 score was 3,Youden index was maximum (0.53),the sensitivity was 92.4％ and the specificity was 61.0％.The ROC analysis revealed that the area under the curve (AUC) of prostate cancer incidence in transition zone was similar to that in peripheral zone with 0.86(95％ CI 0.78-0.95) vs.0.83(95％ CI 0.77-0.89).There were 111 patients in PSA gray zone,33 of whom were diagnosed as prostate cancer.If we used PI-RADS version 2 score 3 as the cut-off point,47 out of 111 patients would avoid unnecessary prostate biopsies with 4 misdiagnosed nonsignificant prostate cancer.Conclusions The value of PI-RADS version 2 score is positively associated with PPV for prostate cancer.PI-RADS version 2 seems to have good diagnostic accuracy in prostate cancer detection.Clinical application of PI-RADS version 2 may help to reduce the number of unnecessary biopsy.
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Objective To establish the Logistic regression model by reporting and data system version 2 (PI-RADS v2)and prostate specific antigen (PSA),and to evaluate the diagnostic efficiency in transition zone prostate cancer (PCa).Methods MRI and PSA data of 33 patients with PCa and 54 patients with non-PCa confirmed by pathology were analyzed retrospectively.The PI-RADS v2 was used to evaluate the risk of 2 groups (from low to high as 1 to 5 points).Total PSA (t-PSA),free to total PSA ratio (f-PSA/t-PSA),PSA density (PSAD) and PI-RADS v2 scores were compared between 2 groups.The Logistic regression models were established with parameters which were significantly different between 2 groups.The Logistic regression was divide into three protocols:PI-RADS v2-+ t-PSA (A),PI-RADS v2 + f-PSA/t-PSA (B),PI-RADS v2+PSAD (C).The ROC curves were constructed by the new parameters Logit (P) and PI-RADS v2 scores for assessing the diagnostic efficiency.Results The t-PSA,f-PSA/t-PSA,PSAD and PI-RADS v2 scores had significant differences between the 2 groups (all P＜0.01).Predictive multivariate model of A,B,C was established as Logit (P)=-8.682+1.507 PI-RADS v2+0.234 t-PSA (x2=65.993,P＜0.01),Logit(P)=-5.425+1.906 PI-RADS v2 13.921 f-PSA/t-PSA (x2 =65.993,P＜0.01),Logit(P)=-7.534+1.045 PI-RADS v2+13.318 PSAD (x2 =74.036,P＜0.01),their area underthe curve (0.945,0.919,0.960) were all higher than that of PI-RADS v2 score (0.861,all P ＜0.01).The protocol C had the best diagnostic efficiency,and the sensitivity and specificity were 87.88 ％ and 92.59 ％.The sensitivity and specificity of PI-RADS v2 score were 87.88％ and 77.78％.Conclusion The diagnostic efficiency of the Logistic regression model which includes the PI-RADS v2 score and PSA are superior to the PI-RADS v2 score alone for transition zone PCa,which can provide a reliable basis for patients whether need biopsy or not.