scholarly journals A nomogram to Predict the Upgrading Rate of ISUP grades of Radical Prostatectomy in Patients Undergoing Transrectal Prostate Biopsy and Transperineal Prostate Cognitive Fusion Biopsy

2020 ◽  
Author(s):  
Zhilei Zhang ◽  
Fei Qin ◽  
Guofeng Ma ◽  
Hang Yuan ◽  
Yongbo Yu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Radical Prostatectomy ◽  
Prostate Biopsy ◽  
Curve Analysis ◽  
Cognitive Fusion ◽  
Decision Curve Analysis ◽  
Biopsy Tissue ◽  
Prostatectomy Specimen ◽  
Significant Factors

Abstract Backgroud: This study was aimed to develop and internally validate an ISUP (International Society of Urology Pathology) upgrade risk nomogram from the biopsy tissue to the specimen of radical prostatectomy. Methods: The clinical characteristics of 166 patients with prostate cancer were retrospectively analyzed, who were divided into two groups based on the upgrade of ISUP between the biopsy tissue and radical prostatectomy specimen. Logistic regression analysis was used to predict the significant independent factors of ISUP upgrade, a nomogram was established to predict ISUP upgrade of prostatectomy specimen based on the significant factors. 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 inclined to be younger age, smaller PV, lower GS scores and PB-ISUP 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 PSA≥20 ng/ml (OR=8.558, P=0.024), Gleason score of PCa≤6 (OR=9.026, P=0.004), PB-ISUP=3,4vs5 (OR=23.232, P=0.000417; OR=26.72, P=0.000241), ways of prostate biopsy (TP-SB+COG-TB (transperineal prostate biopsy + cognitive fusion targeted biopsy) VS TR-SB (transrectal prostate system biopsy), OR=033, P=0.036) and number of positive cores < 10 (OR=0.21, P=0.002) 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 0.843. The C-index for the prediction nomogram was 0.871 (95% CI: 0.817–0.925) and the nomogram showed good calibration. Decision curve analysis also demonstrated that the threshold value of RP-ISUP upgrade risk was 1% to 89%. Conclusion: A novel nomogram incorporating PSA, Gleason score of PCa, PB-ISUP, 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 radical prostatectomy specimen, especially for the low-risk prostate cancer diagnosed by TR-SB.

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.

scholarly journals MultiParametric Magnetic Resonance Imaging-Based Nomogram for Predicting Prostate Cancer and Clinically Significant Prostate Cancer in Men Undergoing Repeat Prostate Biopsy

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Cong Huang ◽  
Gang Song ◽  
He Wang ◽  
Guangjie Ji ◽  
Jie Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Prostate Biopsy ◽  
Curve Analysis ◽  
Resonance Imaging ◽  
Decision Curve Analysis ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Clinically Significant ◽  
Systematic Prostate Biopsy

Objective. To develop and internally validate nomograms based on multiparametric magnetic resonance imaging (mpMRI) to predict prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with a previous negative prostate biopsy. Materials and Methods. The clinicopathological parameters of 231 patients who underwent a repeat systematic prostate biopsy and mpMRI were reviewed. Based on Prostate Imaging and Reporting Data System, the mpMRI results were assigned into three groups: Groups “negative,” “suspicious,” and “positive.” Two clinical nomograms for predicting the probabilities of PCa and csPCa were constructed. The performances of nomograms were assessed using area under the receiver operating characteristic curves (AUCs), calibrations, and decision curve analysis. Results. The median PSA was 15.03 ng/ml and abnormal DRE was presented in 14.3% of patients in the entire cohort. PCa was detected in 75 patients (32.5%), and 59 (25.5%) were diagnosed with csPCa. In multivariate analysis, age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE), and mpMRI finding were significantly independent predictors for PCa and csPCa (all p < 0.01). Of those patients diagnosed with PCa or csPCa, 20/75 (26.7%) and 18/59 (30.5%) had abnormal DRE finding, respectively. Two mpMRI-based nomograms with super predictive accuracy were constructed (AUCs = 0.878 and 0.927, p < 0.001), and both exhibited excellent calibration. Decision curve analysis also demonstrated a high net benefit across a wide range of probability thresholds. Conclusion. mpMRI combined with age, PSA, PV, and DRE can help predict the probability of PCa and csPCa in patients who underwent a repeat systematic prostate biopsy after a previous negative biopsy. The two nomograms may aid the decision-making process in men with prior benign histology before the performance of repeat prostate biopsy.

scholarly journals The presence of chronic inflammation in positive prostate biopsy is associated with upgrading in radical prostatectomy

2021 ◽  
Vol 93 (3) ◽  
pp. 280-284
Author(s):  
Ekrem Guner ◽  
Yavuz Onur Danacioglu ◽  
Yusuf Arikan ◽  
Kamil Gokhan Seker ◽  
Salih Polat ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Radical Prostatectomy ◽  
Chronic Inflammation ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Chronic Prostatitis ◽  
Significant Difference ◽  
The Impact

Objective: This study aimed to determine the predictive effect of the presence of chronic prostatitis associated with prostate cancer (PCa) in prostate biopsy on Gleason score upgrade (GSU) in radical prostatectomy (RP) specimens. Materials and methods: The data of 295 patients who underwent open or robotic RP with a diagnosis of localized PCa following biopsy were retrospectively analyzed. Patients were divided into two groups with and without GSU following RP. Predictive factors affecting GSU on biopsy were determined. The impact of chronic prostatitis associated with prostate cancer on GSU was examined via logistic regression analysis. Results: Out of 224 patients with Gleason 3+3 scores on biopsy, 145 (64.7%) had Gleason upgrade, and 79 (35.2%) had no upgrade. Whilst comparing the two groups with and without Gleason upgrade in terms of patient age, prostate-specific antigen (PSA) value, PSA density (PSAD), prostate volume (PV), neutrophil/lymphocyte (N/L) ratio, number of positive cores, percentage of positive cores, and Prostate Imaging Reporting and Data System version 2 score, no statistically significant difference was detected. The presence of chronic prostatitis associated with PCa was higher in the patient cohort with GSU in contrast to the other group (p < 0.001). According to the univariate logistic regression analysis, the presence of chronic prostatitis was identified to be an independent marker for GSU. Conclusions: Pathologists and urologists should be careful regarding the possibility of a more aggressive tumor in the presence of chronic inflammation associated with PCa because inflammation within PCa was revealed to be linked with GSU after RP.

Validation of a genomic-clinical classifier for predicting clinical progression in high-risk prostate cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 4565-4565
Author(s):  
Christine Buerki ◽  
Anirban Pradip Mitra ◽  
Peter C. Black ◽  
Mercedeh Ghadessi ◽  
Eric J. Bergstralh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Radical Prostatectomy ◽  
Disease Progression ◽  
Mayo Clinic ◽  
Curve Analysis ◽  
Clinical Progression ◽  
Prognostic Information ◽  
Decision Curve Analysis ◽  
High Risk Cohort

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

MP20-07 ASSOCIATION OF ATROPHY IN BASELINE PROSTATE BIOPSY AND LOWER PROSTATE CANCER GRADE IN RADICAL PROSTATECTOMY SPECIMENS

2017 ◽  
Vol 197 (4S) ◽  
Author(s):  
Daniel MO Freitas ◽  
Gerald Andriole ◽  
Ramiro Castro-Santamaria ◽  
Stephen J. Freedland ◽  
Daniel M. Moreira
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Prostate Biopsy

scholarly journals Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruohui Mo ◽  
Rong Shi ◽  
Yuhong Hu ◽  
Fan Hu
Keyword(s):  
Diabetic Retinopathy ◽  
Regression Analysis ◽  
Roc Curve ◽  
External Validation ◽  
Curve Analysis ◽  
Postprandial Blood Glucose ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Decision Curve Analysis ◽  
Course Of Disease

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

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