scholarly journals Evaluation of the Ginsburg Scheme: Where Is Significant Prostate Cancer Missed?

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2502
Author(s):  
August Sigle ◽  
Cordula A. Jilg ◽  
Timur H. Kuru ◽  
Nadine Binder ◽  
Jakob Michaelis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cancer Detection ◽  
Logistic Regression Analysis ◽  
Anterior Region ◽  
Targeted Biopsy ◽  
Detection Rates ◽  
Clinically Significant ◽  
Systematic Biopsy

Background: Systematic biopsy (SB) according to the Ginsburg scheme (GBS) is widely used to complement MRI-targeted biopsy (MR-TB) for optimizing the diagnosis of clinically significant prostate cancer (sPCa). Knowledge of the GBS’s blind sectors where sPCa is missed is crucial to improve biopsy strategies. Methods: We analyzed cancer detection rates in 1084 patients that underwent MR-TB and SB. Cancerous lesions that were missed or underestimated by GBS were re-localized onto a prostate map encompassing Ginsburg sectors and blind-sectors (anterior, central, basodorsal and basoventral). Logistic regression analysis (LRA) and prostatic configuration analysis were applied to identify predictors for missing sPCa with the GBS. Results: GBS missed sPCa in 39 patients (39/1084, 3.6%). In 27 cases (27/39, 69.2%), sPCa was missed within a blind sector, with 17/39 lesions localized in the anterior region (43.6%). Neither LRA nor prostatic configuration analysis identified predictors for missing sPCa with the GBS. Conclusions: This is the first study to analyze the distribution of sPCa missed by the GBS. GBS misses sPCa in few men only, with the majority localized in the anterior region. Adding blind sectors to GBS defined a new sector map of the prostate suited for reporting histopathological biopsy results.

scholarly journals Magnetic resonance/ultrasound fusion targeted biopsy of the prostate can be improved by adding systematic biopsy

2021 ◽  
Author(s):  
Victor Mihail Cauni ◽  
Dan Stanescu ◽  
Florin Tanase ◽  
Bogdan Mihai ◽  
Cristian Persu
Keyword(s):  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Cancer Detection ◽  
Detection Rate ◽  
Specific Antigen ◽  
Cancer Detection Rate ◽  
Targeted Biopsy ◽  
Detection Rates ◽  
Clinically Significant ◽  
Systematic Biopsy

Aim: Magnetic resonance/ ultrasound fusion targeted biopsy (Tbs) is widely used for diagnosing prostate cancer (PCa). The aim of our study was to compare the cancer detection rate (CDR) and the clinically significant prostate cancer detection rate (csPCa) of the magnetic resonance/ultrasound fusion targeted biopsy with those of the standard systematic biopsy (Sbs) and of the combination of both techniques.Material and methods: A total of 182 patients underwent magnetic resonance/ultrasound fusion Tbs on the prostate for PCa suspicion based on multiparametric magnetic resonance imaging (mMRI) detection of lesions with PI-RADSv2 score ≥3. A total of 78 patients had prior negative biopsies. Tb was performed by taking 2-4 cores from each suspected lesion, followed by Sb with 12 cores. We evaluated the overall detection rate of PCa and clinically significant prostate cancer, defined as any PCa with Gleason score ≥3+4.Results: Median prostate specific antigen (PSA) level pre-biopsy was 7.4 ng/ml and median free-PSA/PSA ratio was 10.2%. Patient median age was 62 years old. PIRADSv2 score was 3 in 54 cases, 4 in 96 cases and 5 in 32 cases. PI-RADS-dependent detection rate of Tbs for scores 3, 4 and 5 was 25.9%, 65.6% and 84.4%, respectively, with csPCa detection rates of 24.1%, 54.2%, and 71.9%. Overall detection rate was 57.1% for Tbs, which increased to 60.4% by adding Sbs results. Detection rate for clinically significant prostate cancer (csPCa) was 48.4% and increased to 51.1% by adding Sbs. Overall detection rate for repeated biopsy was 50% and 68.3% for biopsy in naïve patients. Sbs detection rate was 55.5%, 8 patients having a negative biopsy on Tbs.Conclusions: When Tbs is considered due to a PI-RADS ≥3 lesion on mMRI, combined Tbs + Sbs increases the overall CDR and csPCa detection rates.

Changes in prostate cancer detection rate of fusion versus systematic biopsy over time: A single center experience.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 15-15
Author(s):  
Brian P. Calio ◽  
Abhinav Sidana ◽  
Dordaneh Sugano ◽  
Amit L Jain ◽  
Mahir Maruf ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Learning Curves ◽  
Low Risk ◽  
Fusion Biopsy ◽  
Detection Rates ◽  
Single Center Experience ◽  
Significant Difference ◽  
Clinically Significant ◽  
Systematic Biopsy

15 Background: To determine the effect of learning curves and changes in fusion platform during 9 years of NCI’s experience with multiparametric MRI (mpMRI)/TRUS fusion biopsy. Methods: A review was performed of a prospectively maintained database of patients undergoing mpMRI followed by fusion biopsy (Fbx) and systematic biopsy (Sbx) from 2007−2016. The patients were stratified based on the timing of first biopsy in 3 groups. Cohort 1 included patients biopsied between 7/2007−12/2010, accounting for learning curve at our institution. Cohort 2 included patients biopsied from 1/2011 up to the debut of UroNav (Invivo) platform in 5/2013. Cohort 3 included patients biopsied after 5/2013. Clinically significant (CS) disease was defined as Gleason 7 (3+4) or higher. Cancer detection rates (CDR) between Sbx and Fbx during different time periods were compared using McNemar’s test. Age and PSA standardized CDRs were calculated for comparison between 3 cohorts. Results: 1528 patients were included in the study with 219, 549 and 761 patients included in 3 respective cohorts. Mean age, PSA and race distribution were similar across 3 cohorts. In cohort 1 there was no significant difference between CDR of CS disease by Fbx (24.7%) vs Sbx (21.5%), p = 0.377. Fbx was significantly better than Sbx in detection of CS disease in cohort 2 and cohort 3 (31.5% vs 25.3%, p = 0.001; 36.5% vs 30.2%, p < 0.001, respectively). There was significant decline in detection of low risk disease by Fbx compared to Sbx in the same period (cohort 2: 14.2% vs 20.9%, p < 0.001; cohort 3: 12.5% vs 19.5%, p < 0.001). Age and PSA standardized CDR of CS cancer by Fbx increased significantly between each successive cohort (cohort 1 and 2: 5.2%, 95% CI [2.1-8.5]), 2 and 3 (5.2%, 95% CI [1.8-8.6]). Conclusions: Our results show that after an early learning period using Fbx, CS prostate cancer was detected at significantly higher rates with Fbx than with Sbx, and low risk disease was detected at lower rates. Advances in software allowed for even greater detection of CS disease in the last cohort. This study shows that accuracy of Fbx is dependent on multiple factors; surgeon/radiologist experience and software improvements together produce improved accuracy.

scholarly journals A nomogram to predict the upgrading rate of ISUP grades of RP in patients undergoing transrectal prostate biopsy and transperineal prostate biopsy

2021 ◽  
Author(s):  
Zhilei Zhang ◽  
Fei Qin ◽  
Guofeng Ma ◽  
Hang Yuan ◽  
Yongbo Yu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Radical Prostatectomy ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Biopsy Tissue ◽  
Systematic Biopsy

Abstract Backgroud: This study was aimed to develop and internally validate a nomogram for risk of upgrade of ISUP (International Society of Urology Pathology) grade group from biopsy tissue to RP (radical prostatectomy) final histology.Methods: 166 patients with prostate cancer were retrospectively analyzed and divided into two groups based on ISUP upgrade status from needle biopsy to radical prostatectomy specimen, these being the 'ISUP upgrade' group and the 'no ISUP upgrade' group. Logistic regression analysis was used to predict the significant independent factors for ISUP upgrade. A nonogram was then developed based on these independent factors, which would predict risk of ISUP upgrade. The C-index, calibration plot, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the predicting model. Internal validation was evaluated by using the bootstrapping validation. Results: There were 47 patients in the ISUP upgrade group and 119 patients in the no ISUP upgrade group respectively. Patients in the ISUP upgrade group tended to be of younger age, smaller PV (prostate volume), lower GS (Gleason score) of PB (prostate biopsy) tissue than the no ISUP upgrade group (p=0.043, p=0.041, p < 0.001, p =0.04, respectively). Multivariate logistic regression analysis showed that GS ≤6 (OR=14.236, P=0.001), prostate biopsy approach (TB-SB (transperineal prostate systematic biopsy) VS TR-SB (transrectal prostate systematic biopsy), OR=0.361, P=0.03) and number of positive cores < 10 (OR=0.396, P=0.04) were the independent risk factors for ISUP upgrade. A prediction nomogram model of ISUP upgrade was built based on these significant factors above, the area under the receiver operating characteristic (AUC) curve of which was 0.802. The C-index for the prediction nomogram was 0.798 (95%CI: 0.655–0.941) and the nomogram showed good calibration. High C-index value of 0.772 could still be reached in the interval validation. Decision curve analysis also demonstrated that the threshold value of RP-ISUP upgrade risk was 3% to 67%. Conclusion: A novel nomogram incorporating PSA, GS of PCa, ways of prostate biopsy and number of positive cores was built with a relatively good accuracy to assist clinicians to evaluate the risk of ISUP upgrade in the RP specimen, especially for the low-risk prostate cancer diagnosed by TR-SB.

MRI-based nomogram for the prediction of prostate cancer diagnosis: A multi-centre validated patient–physician decision tool

2022 ◽  
pp. 205141582110659
Author(s):  
Edwin M Chau ◽  
Beth Russell ◽  
Aida Santaolalla ◽  
Mieke Van Hemelrijck ◽  
Stuart McCracken ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Gleason Grade ◽  
Grade Group ◽  
Group 2 ◽  
Clinically Significant ◽  
Significant Disease

Objective: To update and externally validate a magnetic resonance imaging (MRI)-based nomogram for predicting prostate biopsy outcomes with a multi-centre cohort. Materials and methods: Prospective data from five UK-based centres were analysed. All men were biopsy naïve. Those with missing data, no MRI, or prostate-specific antigen (PSA) > 30 ng/mL were excluded. Logistic regression analysis was used to confirm predictors of prostate cancer outcomes including MRI-PIRADS (Prostate Imaging Reporting and Data System) score, PSA density, and age. Clinically significant disease was defined as International Society of Urological Pathology (ISUP) Grade Group ⩾ 2 (Gleason grade ⩾ 7). Biopsy strategy included transrectal and transperineal approaches. Nomograms were produced using logistic regression analysis results. Results: A total of 506 men were included in the analysis with median age 66 (interquartile range (IQR) = 60–69). Median PSA was 6.6 ng/mL (IQR = 4.72–9.26). PIRADS ⩾ 3 was reported in 387 (76.4%). Grade Group ⩾ 2 detection was 227 (44.9%) and 318 (62.8%) for any cancer. Performance of the MRI-based nomogram was an area under curve (AUC) of 0.84 (95% confidence interval (CI) = 0.81–0.88) for Grade Group ⩾ 2% and 0.85 (95% CI = 0.82–0.88) for any prostate cancer. Conclusion: We present external validation of a novel MRI-based nomogram in a multi-centre UK-based cohort, showing good discrimination in identifying men at high risk of having clinically significant disease. These findings support this risk calculator use in the prostate biopsy decision-making process. Level of evidence: 2c

scholarly journals MRI Screening and MRI/US Fusion-Guided Transperineal Biopsy in Detecting Prostate Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110194
Author(s):  
Hongqing Yin ◽  
Jun Shao ◽  
Huan Song ◽  
Wei Ding ◽  
Bin Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Area Under The Curve ◽  
Vital Role ◽  
Targeted Biopsy ◽  
Detection Rates ◽  
Transperineal Biopsy ◽  
Systematic Biopsy

Objective: Systematic biopsy plays a vital role in diagnosing prostate cancer, but it can lead to misdiagnoses or undertreatment. Advances in magnetic resonance imaging (MRI) and its guided targeting technology provide the possibility of improving the use of biopsies. This study aimed to evaluate the performance of MRI screening and MRI/ultrasound (MRI/US) fusion-guided transperineal biopsy in the detection of prostate cancer. Methods: We performed a retrospective study on patients with suspected prostate cancer in the Kunshan Hospital Affiliated with Jiangsu University from January 2017 to December 2019. All of the patients underwent MRI examinations, followed by a systematic biopsy (either alone or in combination with MRI/US fusion-guided targeted biopsy, based on MRI-visible lesions). We evaluated the diagnostic accuracy of MRI screening and compared biopsy methods by considering sensitivity, specificity, and area under the curve (AUC) values. Results: A total of 157 patients were enrolled, including 112 patients with MRI-visible lesions and 45 patients without MRI-visible lesions. The cancer detection rate (CDR) was higher in patients with MRI-visible lesions ( P < 0.001); however, the serum prostate-specific antigen (PSA) indicators were similar ( P > 0.05). The AUC of MRI was 0.63, which was superior to the AUC values of ultrasound (AUC = 0.55, P = 0.031) and digital rectal examination (AUC = 0.52, P = 0.041) for screening prostate cancer. Both overall CDR and clinically significant prostate cancer detection rates were improved if we combined systematic biopsy and MRI/US fusion-guided targeted biopsy procedures. Conclusion: Overall, prior MRI screening may serve as a classifier for avoiding the overuse of biopsies. A combination of systematic and MRI/US fusion-guided targeted biopsy procedures offers an optimal regimen for detecting prostate cancer.

Does PSA level affect the choice of prostate puncture methods among MRI-ultrasound fusion targeted biopsy, transrectal ultrasound systematic biopsy or the combination of both?

2021 ◽  
pp. 20210312
Author(s):  
Yunyun Liu ◽  
Lin Dong ◽  
Lihua Xiang ◽  
Boyang Zhou ◽  
Hanxiang Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Statistical Significance ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Targeted Biopsy ◽  
Detection Rates ◽  
Detection Approach ◽  
Clinically Significant ◽  
Histopathological Results ◽  
Systematic Biopsy

Objectives: To explore whether prostate-specific antigen (PSA) affects the choice of prostate puncture methods by comparing MRI-ultrasound fusion targeted biopsy (MRI-TBx) with transrectal ultrasound systematic biopsy (TRUS-SBx) in the detection of prostate cancer (PCa), clinically significant prostate cancer (csPCa) and non-clinically significant prostate cancer (nsPCa) in different PSA groups (<10.0,10.0–20.0 and>20.0 ng ml−1). Methods: A total of 190 patients with 215 lesions who underwent both MRI-TBx and TRUS-SBx were included in this retrospective study. PSA was measured pre-operatively and stratified to three levels. The detection rates of PCa, csPCa and nsPCa through different methods (MRI-TBx, TRUS-SBx, or MRI-TBx +TRUS SBx) were compared with stratification by PSA. Results: Among the 190 patients, the histopathological results revealed PCa in 126 cases, including 119 csPCa. In PSA <10.0 ng ml−1 group, although the detection rates of PCa and csPCa by MRI-TBx were higher than those of TRUS-SBx, no significant differences were observed (p = 0.741; p = 0.400). In PSA 10.0–20.0 ng ml−1 group, difference between the detection rate of csPCa with TRUS-SBx and the combined method was statistically significant (p = 0.044). As for PSA >20.0 ng ml−1, MRI-TBx had a higher csPCa rate than TRUS-SBx with no statistical significance noted (p = 0.600). Conclusion: MRI-TBx combined with TRUS-SBx could be suitable as a standard detection approach for csPCa in patients with PSA 10.0–20.0 ng ml−1. As for PSA >20.0 and <10.0 ng ml−1, both MRI-TBx and TRUS-SBx might provide effective solutions for tumor detection. Advances in knowledge: This study gives an account of choosing appropriate prostate puncture methods through PSA level.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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