THE USE OF NEURAL NETWORKS AND LOGISTIC REGRESSION ANALYSIS FOR PREDICTING PATHOLOGICAL STAGE IN MEN UNDERGOING RADICAL PROSTATECTOMY: A POPULATION BASED STUDY

Abstract Background: Identification of risk and protective factors is of great importance in designing preventive and interventional programs. The aim of the present study has been to investigate peer/individual, family, school, and community risk and protective factors as predictors of tobacco and alcohol use among Iranian adolescents. Methods: In a cross-sectional population-based study, 870 Iranian adolescents aged 15–18 years old, filled out the adopted form of “Communities That Care Youth Survey”. Thirty-two risk and protective factors were entered in adjusted logistic regression analyses to predict the lifetime cigarette and alcohol use. Results: Sixteen risk and seven protective factors predicted both lifetime cigarette and alcohol use in the bivariate logistic regression analysis. In the multivariate logistic regression analysis 12 risk factors including friends’ use of drugs, interaction with antisocial peers, sensation seeking, intention to use, perceived risks of drug use, family history of drug use, poor family management, parental attitudes favorable toward drug use, family conflict, academic failure, school low commitment, perceived availability of drugs predicted both lifetime cigarette and alcohol use as well as four protective factors including religiosity, self-esteem, family rewards for prosocial involvement, and school rewards for prosocial involvement. The highest OR were related to the risk factor of “Rewards for antisocial involvement” [3.9(1.5–10)], and protective factor of “Religiosity” [0.1(0.1–0.3)]. Conclusion: The present study has produced evidences about risk and protective factors related to adolescents substance use and can help designing and implementing of preventive interventions for maintaining and promoting adolescents health.

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

10.21203/rs.3.rs-360077/v1 ◽

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.

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ESTIMATION OF THE SECTORS OF THE INVESTMENTS MADE ON VENTURE CAPITAL COMPANIES WITH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LOGISTIC REGRESSION ANALYSIS

Journal of Life Economics ◽

10.15637/jlecon.7.022 ◽

2020 ◽

Vol 7 (4) ◽

pp. 297-308

Author(s):

Kubilay ERİSLİK ◽

Özlem DENİZ BAŞAR

Keyword(s):

Neural Networks ◽

Logistic Regression ◽

Regression Analysis ◽

Artificial Neural Networks ◽

Venture Capital ◽

Logistic Regression Analysis ◽

Multiple Logistic Regression Analysis ◽

Multiple Logistic Regression ◽

Core Phase ◽

Artificial Neural

Venture capital companies undergo three different phases as core, growth and maturity phases as of their establishment. There are different stages in these phases in terms of providing the finance. The stage of providing finance for the first introduction of the product to the market in the core phase is called Serial A, the stage of providing the increasing finance need during the continuation of the growth is called Serial B and the stage of providing the finance needed in the growth and maturity phases is called Serial C and it continues as Serial D. In this study, it has been aimed to estimate the sectors of the venture capital companies by benefiting from the phases and amounts of the investments made by the investors to the venture capital companies. In the study, 5 sectors with the highest investment from investors have been selected and the investment data of 709 venture capital companies taking place in this sector have been benefited. Artificial Neural Networks and Multiple Logistic Regression Analysis have been used in the estimation of the sectors covering the companies with the data attained from the investment series. When the attained results have been examined, it has been determined that the results attained with Artificial Neural Networks are more successful than the results attained with Multiple Logistic Regression analysis.

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

Archivio Italiano di Urologia e Andrologia ◽

10.4081/aiua.2021.3.280 ◽

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.

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Neural networks and logistic regression: Analysis of a case-control study on myocardial infarction

Journal of Clinical Epidemiology ◽

10.1016/s0895-4356(97)00163-7 ◽

1997 ◽

Vol 50 (11) ◽

pp. 1309-1310 ◽

Cited By ~ 13

Author(s):

Paolo Vineis ◽

Alberto Rainoldi

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Case Control Study ◽

Case Control ◽

Control Study

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Prevalence and correlates of psychological distress among a national population-based sample of adults in Solomon Islands

International Journal of Social Psychiatry ◽

10.1177/0020764020969757 ◽

2020 ◽

pp. 002076402096975

Author(s):

Supa Pengpid ◽

Karl Peltzer

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Psychological Distress ◽

Logistic Regression Analysis ◽

Solomon Islands ◽

Population Based ◽

Cross Sectional ◽

Steps Survey ◽

Nationally Representative ◽

Adjusted Logistic Regression

Background: Psychological distress is an undersearched cause of poorer health in the Pacific island countries. Aims: The study aimed to investigate the prevalence and associated factors of psychological distress in a population-based survey among 18–69 year-old persons in Solomon Islands. Method: Cross-sectional nationally representative data of 2,533 18–69 year-old persons (38 years were the median age) from the 2015 Solomon Islands STEPS survey were analysed. Results: The results indicate that 18.9% of participants had psychological distress (⩾20 scores), 13.4% had mild (20–24 scores), 4.3% moderate (25–29 scores) and 1.1% severe (30–50 scores) psychological distress. In adjusted logistic regression analysis, being divorced, separated, or widowed (AOR: 2.76, 95% CI: 1.60–4.78), having had a heart attack or stroke (AOR: 3.34, 95% CI: 2.23–4.99), alcohol dependence (AOR: 1.72, 95% CI: 1.04–2.84), and sedentary behaviour (AOR: 1.90, 95% CI: 1.28–2.84), were positively and the consumption of 3–4 servings of fruit and vegetables (AOR: 0.62, 95% CI: 0.42–0.92), were negatively associated with psychological distress (⩾20 scores). In addition, in a sex stratified adjusted logistic regression analysis among men, current smokeless tobacco use (AOR: 4.95, 95% CI: 1.66–14.75), was associated with psychological distress. Furthermore, in the overall unadjusted analysis, stop smoking attempts in the past 12 months and consumption of too much salt was associated with psychological distress. Conclusion: Almost one in five participants were reported psychological distress and several factors were detected which could be targeted in intervention activities.

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SURGICAL MARGIN STATUS FOR RADICAL PROSTATECTOMY IN PATHOLOGICAL STAGE T2 DISEASE: A POPULATION-BASED STUDY

The Journal of Urology ◽

10.1016/s0022-5347(09)60484-5 ◽

2009 ◽

Vol 181 (4S) ◽

pp. 168-169 ◽

Cited By ~ 1

Author(s):

Nathan Lawrentschuk ◽

Andrew Evans ◽

John Srigley ◽

Joseph L Chin ◽

Bishwajit Bora ◽

...

Keyword(s):

Radical Prostatectomy ◽

Surgical Margin ◽

Population Based ◽

Margin Status ◽

Pathological Stage ◽

Population Based Study

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

10.21203/rs.2.16991/v4 ◽

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.

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