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.

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 Prediction of prostate cancer by deep learning with multilayer artificial neural network

2018 ◽  
Vol 13 (5) ◽  
Author(s):  
Takumi Takeuchi ◽  
Mami Hattori-Kato ◽  
Yumiko Okuno ◽  
Satoshi Iwai ◽  
Koji Mikami
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Stepwise Logistic Regression ◽  
Artificial Neural

Introduction: To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated. Methods: A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model. Results: With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5–10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6. Conclusions: ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

scholarly journals Urinary continence outcomes of four years of follow-up and predictors of early and late urinary continence in patients undergoing robot-assisted radical prostatectomy

2020 ◽  
Author(s):  
Xing Li ◽  
Huan Zhang ◽  
Zhuo Jia ◽  
Yunpeng Wang ◽  
Yong Song ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Factor ◽  
Radical Prostatectomy ◽  
Logistic Regression Analysis ◽  
Urinary Continence ◽  
Robot Assisted ◽  
Significant Difference ◽  
Late Stages

Abstract Background: The robot-assisted radical prostatectomy (RARP) has been widely applied in recent years; however, only a few studies are reported about long-term urinary continence after surgery. The present study aimed to examine the outcomes of continence rates (CRs) and determine the risk and protective factors of urinary continence in patients with prostate cancer (PCa) undergoing RARP. Methods: This retrospective study included 650 patients treated with RARP with perioperative data and at least one year of follow-up from January 2009 to November 2017. Also, the preoperative, intraoperative, and postoperative parameters of the patients were analyzed. Continence was defined as no pad use. Early and late continence was defined as the return of urinary continence within 3 months and beyond 12 months post-surgery, respectively. CRs were examined from 1–48 months postoperatively. Logistic regression analysis evaluated the association between the predictive factors and urinary continence in the early and late stages. Results: No significant difference was detected in the CR from 12–48 months postoperatively (P=0.766). Logistic regression analysis proved that pelvic lymph node dissection (PLND) was a significant risk factor of urinary continence at 1 month. Nerve-sparing (NS) was a significant protective factor of urinary continence at 1, 3, and 6 months. Advanced age was an independent risk factor of urinary continence at 6, 12, and 24 months. Other variables were not statistically significant predictors of urinary continence. Conclusions: The current results demonstrated that CR gradually improved with time within 1 year and stabilized 1 year after the surgery. PLND, NS, and age were significant determinants of continence in the early and late stages, respectively. These parameters could be used for preoperative identification of patients at high risk and counseling about postoperative expectations for urinary continence.

scholarly journals Prediction of prostate cancer by deep learning with multilayer artificial neural network

2018 ◽  
Author(s):  
Takumi Takeuchi ◽  
Mami Hattori-Kato ◽  
Yumiko Okuno ◽  
Satoshi Iwai ◽  
Koji Mikami
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Deep Learning ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Stepwise Logistic Regression ◽  
Artificial Neural

AbstractObjectivesTo predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning utilizing a multilayer artificial neural network was investigated.Materials and methodsA total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs. 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.ResultsWith any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and 9 explanatory variables from 22, respectively. In common between them, age at biopsy, findings on digital rectal examination, findings in the peripheral zone on MRI diffusion-weighted imaging, and body mass index were positively influential variables, while numbers of previous prostatic biopsy and prostate volume were negatively influential. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared with that with logistic regression analysis (LR). The AUCs with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise logistic regression compared with the AUC with LR. The ANN had a higher net-benefit than LR between prostate cancer probability cut-off values of 0.38 and 0.6.ConclusionANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

scholarly journals Urinary continence outcomes of four years of follow-up and predictors of early and late urinary continence in patients undergoing robot-assisted radical prostatectomy

2020 ◽  
Author(s):  
Xing Li ◽  
Huan Zhang ◽  
Zhuo Jia ◽  
Yunpeng Wang ◽  
Yong Song ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Factor ◽  
Radical Prostatectomy ◽  
Logistic Regression Analysis ◽  
Urinary Continence ◽  
Robot Assisted ◽  
Significant Difference ◽  
Late Stages

Abstract Background: The robot-assisted radical prostatectomy (RARP) has been widely applied in recent years; however, only a few studies are reported about long urinary continence after surgery. The present study aimed to examine the outcomes of continence rates (CRs) and determine the risk and protective factors of urinary continence in patients with prostate cancer (PCa) undergoing RARP. Methods: This retrospective study included 650 patients treated with RARP with perioperative data and at least one year of follow-up from January 2009 to November 2017. Also, the preoperative, intraoperative, and postoperative parameters of the patients were analyzed. Continence was defined as no pad use. Early and late continence was defined as the return of urinary continence within 3 months and beyond 12 months post-surgery, respectively. CRs were examined from 1–48 months postoperatively. Logistic regression analysis evaluated the association between the predictive factors and urinary continence in the early and late stages. Results: No significant difference was detected in the CR from 12–48 months postoperatively (P=0.766). Logistic regression analysis proved that pelvic lymph node dissection (PLND) was a significant risk factor of urinary continence at 1 month. Nerve-sparing (NS) was a significant protective factor of urinary continence at 1, 3, and 6 months. Advanced age was an independent risk factor of urinary continence at 6, 12, and 24 months. Other variables were not statistically significant predictors of urinary continence. Conclusions: The current results demonstrated that CR gradually improved with time within 1 year and stabilized 1 year after the surgery. PLND, NS, and age were significant determinants of continence in the early and late stages, respectively. These parameters could be used for preoperative identification of patients at high risk and counseling about postoperative expectations for urinary continence.

Abstract 1534: Diabetes Mellitus Is An Independent Predictor Of Mortality In Defibrillator Recipients

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Carol Chen-Scarabelli ◽  
Tiziano M Scarabelli
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Mortality Risk ◽  
Independent Predictor ◽  
Mortality Data ◽  
Significant Difference ◽  
Baseline Characteristics ◽  
The Impact

Background: Previous studies have shown an increased morbidity and mortality risk in patients with diabetes mellitus (DM) undergoing percutaneous coronary interventions and cardiac surgery. However, the impact of DM on mortality in patients with implantable cardioverter defibrillators (ICDs) is unknown. Aim: to investigate whether DM was associated with increased mortality risk in patients with ICDs Methods: A retrospective review of all ICD patients followed within a consecutive 2.5 year period was performed. Baseline characteristics and mortality data were compared between DM and non-DM patients. Mann Whitney test was used to analyze the differences between the means, and multivariate logistic regression analysis was performed to identify independent predictors of mortality. Results: A total of 336 patients with ICDs (including 141 patients with DM and 195 without DM) were studied. The mean age was 66.8 ± 9.6 years in the overall population, with no significant difference in baseline characteristics between the 2 groups, except for beta-blocker therapy, creatinine, and estimated glomerular infiltration rate (EGFR). Use of beta-blockers was greater in the non-DM group (92.8% vs 84.4%, p=0.02). Renal function was worse in the DM group, as documented by a higher serum creatinine (1.6 ± 0.8 mg/dl in DM vs 1.38 ± 0.74 mg/dl in non-DM, p = 0.003) and a lower EGFR (55.9 ± 22.9 ml/min/1.73m 2 vs 63.5 ± 19.9 ml/min/1.73m 2 , p=0.0005). Although the incidence of VT was not significantly different between the two groups, there was a significantly higher mortality in the DM group (27.7% vs 11.3% in non-DM, p=0.0002). On multivariate stepwise logistic regression analysis, DM was a significant independent predictor of mortality (OR 2.38, 95% CI 1.3– 4.36, p =0.005). Conclusion: DM is an independent predictor of mortality in ICD patients. Prevention of DM, as well as early diagnosis and intervention, should be emphasized in order to reduce the growing epidemic of type 2 DM and its associated complications.

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 Impact of Rehabilitation Dose on Nutritional Status at Discharge from a Convalescent Rehabilitation Ward in Malnourished Patients with Hip Fracture

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 722
Author(s):  
Yusuke Ito ◽  
Hidetaka Wakabayashi ◽  
Shinta Nishioka ◽  
Shin Nomura ◽  
Ryo Momosaki
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hip Fracture ◽  
Nutritional Status ◽  
Logistic Regression Analysis ◽  
Short Form ◽  
Mini Nutritional Assessment ◽  
Functional Independence ◽  
Rehabilitation Ward ◽  
The Impact

The object of this study is to determine the impact of the rehabilitation dose on the nutritional status at discharge from a convalescent rehabilitation ward in malnourished patients with hip fracture. This retrospective case-control study involved malnourished patients with hip fracture aged 65 years or older who had been admitted to a convalescent rehabilitation ward and whose data were registered in the Japan Rehabilitation Nutrition Database. The primary outcome was nutritional status at discharge. Patients were classified according to whether nutritional status was improved or not at discharge, according to the Mini Nutritional Assessment-Short Form® (MNA-SF) score. The association between improved nutritional status and rehabilitation dose was assessed by a logistic regression analysis. Data were available for 145 patients (27 men, 118 women; mean age 85.1 ± 7.9 years). Daily rehabilitation dose was 109.5 (median 94.6–116.2) min and the MNA-SF score at admission was 5 (median 4–6). Nutritional status was improved in 97 patients and not improved in 48. Logistic regression analysis showed the following factors to be independently associated with nutritional status at discharge: Functional Independence Measure score (OR 1.042, 95% CI 1.016–1.068), energy intake (OR 1.002 CI 1.000–1.004), daily rehabilitation dose (OR 1.023, 95% CI 1.002–1.045), and length of hospital stay (OR 1.026, 95% CI 1.003–1.049). The daily rehabilitation dose in malnourished patients with hip fracture may positively impact nutritional status at discharge.

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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