Modeling acute urinary toxicity after radiotherapy for prostate cancer.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 156-156
Author(s):  
Viviana Carillo ◽  
Tiziana Rancati ◽  
Cesare Cozzarini ◽  
Sergio Villa ◽  
Andrea Botti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Variable Selection ◽  
Model Building ◽  
Selection Process ◽  
Final Multivariable Model ◽  
Improve Model ◽  
Quantitative Analyses ◽  
Moderate Hypofractionation ◽  
Delivered Dose

156 Background: DUE-01 is a multi-centric observational study aimed at developing predictive models of genito-urinary toxicity and erectile dysfunction for prostate cancer patients treated with conventional (1.8-2Gy/fr, CONV) or moderate hypofractionation (2.5-2.7Gy/fr, HYPO). Current analysis focused on modelling the relationship between the risk of IPSS≥15 (IPSS15end) at the end of radiotherapy and clinincal/dosimetric risk factors. Methods: Planning data and relevant clinical factors were prospectively collected, including DVH/DSH referred to the whole treatment and to the weekly delivered dose (DVHw/DSHw). Best discriminating DVH/DSH parameters were selected by the differences between patients with/without IPSS15end=1 (t-test). Bootstrap variable selection techniques (300 resamples) in the framework of logistic backward feature selection was used to improve model building (El Naqa, IJROBP 2006). Graphical and quantitative analyses of the variable selection process applied to bootstrap data replicates was used to avoid underfitting/overfitting and to assess the final multivariable model. Results: 247 patients were available (CONV:116, HYPO:131). Seventy one out of 247 (28.7%) reported IPSS15end=1. The most predictive dosimetric tools were the absolute weekly delivered dose (DSHw and DVHw). DSHw and DVHw were alternatively inserted in the bootstrap variable selection flow, together with clinical risk factors. Due to the number of events, a logistic model containing six variables was accepted On the basis of observed frequency of variables in the top six positions, a model including basal IPSS (median OR=1.22, p=0.00001), use of anti-hypertensives (median OR=2.7, p=0.01), absolute bladder surface receiving more than 10.5 Gy/week (s10.5w, median OR=1.16, p=0.0001), and s12.5w (median OR=1.07, p=0.005), was choosen. AUC of this model was 0.80. Silmilar results were obtained when using DVHw. Conclusions: Basal IPSS, use of anti-hypertensive drugs, s10.5w/v10.5w and s12.5w/v12.5w are the main predictors of IPSS>=15 at the end of radiotherapy Bootstrap variable selection technique gives the modeler more insight into the importance and stability of the different variables selected and allows development of more robust models

scholarly journals Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David E. Booth ◽  
Venugopal Gopalakrishna-Remani ◽  
Matthew L. Cooper ◽  
Fiona R. Green ◽  
Margaret P. Rayman
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Logistic Regression ◽  
Variable Selection ◽  
Disease Risk ◽  
Gradient Boosting ◽  
Stepwise Logistic Regression ◽  
Anticancer Effect ◽  
Cancer Data ◽  
Snp Interaction

AbstractWe begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors using lasso or boosting variable selection. We then illustrate the use of the protocol with a set of prostate cancer data and show that it recovers known risk factors. Finally, we use the protocol to identify new and important SNP based risk factors for prostate cancer and further seek evidence for or against the hypothesis of an anticancer function for Selenium in prostate cancer. We find that the anticancer effect may depend on the SNP-SNP interaction and, in particular, which alleles are present.

214: Incidence and Risk Factors of Symptomatic DVT in Prostate Cancer Patients Undergoing Laparoscopic Radical Prostatectomy

2006 ◽  
Vol 175 (4S) ◽  
pp. 70-71
Author(s):  
Fernando P. Secin ◽  
Clément-Claude Abbou ◽  
Inderbir S. Gill ◽  
Georges Fournier ◽  
Thierry Piéchaud ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Laparoscopic Radical Prostatectomy

259: Evaluation and Clinical Relevance of Prognostic Molecular Risk Factors in Prostate Cancer

2006 ◽  
Vol 175 (4S) ◽  
pp. 86-86
Author(s):  
Roland Bonfig ◽  
Hubertus Riedmiller ◽  
Burkhardt Kneitz ◽  
Philipp Stroebel
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Clinical Relevance

Prostate Cancer

2018 ◽  
Author(s):  
Kathryn M. Wilson ◽  
Lorelei Mucci
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Prostate Specific Antigen ◽  
Advanced Prostate Cancer ◽  
Advanced Disease ◽  
African Ancestry ◽  
Specific Antigen ◽  
Dietary Factors ◽  
Increased Risk

Prostate cancer is among the most commonly diagnosed cancers among men, ranking second in cancer globally and first in Western countries. There are marked variations in incidence globally, and its incidence must be interpreted in the context of diagnostic intensity and screening. The uptake of prostate-specific antigen screening since the 1990s has led to dramatic increases in incidence in many countries, resulting in an increased proportion of indolent cancers that would never have come to light clinically in the absence of screening. Risk factors differ when studying prostate cancer overall versus advanced disease. Older age, African ancestry, and family history are established risk factors for prostate cancer. Obesity and smoking are not associated with risk overall, but are associated with increased risk of advanced prostate cancer. Several additional lifestyle factors, medications, and dietary factors are now emerging as risk factors for advanced disease.

scholarly journals MO27-2 Risk factors for metastasis in prostate cancer patients: A systematic review

2021 ◽  
Vol 32 ◽  
pp. S315
Author(s):  
Billy Susanto ◽  
Griffin Geraldo ◽  
Jennifer Jesse Limanto ◽  
Andree Kurniawan
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Cancer Patients

scholarly journals Cardiovascular Disease Risk Factors: How Relevant in African Men With Prostate Cancer Receiving Androgen-Deprivation Therapy?

2017 ◽  
Vol 3 (1) ◽  
pp. 7-14
Author(s):  
Okon Ekwere Essien ◽  
Iya Eze Bassey ◽  
Rebecca Mtaku Gali ◽  
Alphonsus Ekpe Udoh ◽  
Uwem Okon Akpan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Waist Circumference ◽  
Total Cholesterol ◽  
Ldl Cholesterol ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Treatment Naïve

Purpose Cardiovascular disease risk factors have been associated with androgen-deprivation therapy (ADT) in white and Hispanic populations. It is therefore relevant to determine if there exists a relationship between these parameters in the African population. Patients and Methods The design of the study was cross sectional. Prostate-specific antigen concentration, waist circumference, body mass index (BMI), lipid profile, glucose level, and insulin level were determined in 153 patients with prostate cancer and 80 controls. The patients with prostate cancer were divided into subgroups of treatment-naïve patients and those receiving ADT. Results Mean total cholesterol ( P = .010), LDL cholesterol ( P = .021), BMI ( P = .001), and waist circumference ( P = .029) values were significantly higher in patients treated with ADT when compared with treatment-naïve patients. In patients treated with ADT for up to 1 year, only mean BMI was significantly higher than in treatment-naïve patients, whereas those treated with ADT for more than 1 year had significantly higher mean BMI, waist circumference, total cholesterol, and LDL cholesterol values when compared with treatment-naïve patients. There were no significant differences in insulin or glucose levels. Those undergoing hormone manipulation after orchiectomy had fewer cardiovascular risk factors compared with those undergoing hormone manipulation alone. Conclusion This study shows that ADT results in elevated total cholesterol, LDL cholesterol, BMI, and waist circumference values, all of which are risk factors of cardiovascular disease. Screening for cardiovascular risk factors should be included in treatment plans for patients with prostate cancer.

Influence of daily setup measurements and corrections on the estimated delivered dose during IMRT treatment of prostate cancer patients

2009 ◽  
Vol 90 (3) ◽  
pp. 291-298 ◽  
Author(s):  
Paul M.A. van Haaren ◽  
Arjan Bel ◽  
Pieter Hofman ◽  
Marco van Vulpen ◽  
Alexis N.T.J. Kotte ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Delivered Dose

scholarly journals Risk Factors for Prostate Cancer Detection After a Negative Biopsy: A Novel Multivariable Longitudinal Approach

2010 ◽  
Vol 28 (10) ◽  
pp. 1714-1720 ◽  
Author(s):  
Peter H. Gann ◽  
Angela Fought ◽  
Ryan Deaton ◽  
William J. Catalona ◽  
Edward Vonesh
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Specific Antigen ◽  
Time Dependent ◽  
Negative Biopsy ◽  
Gland Volume ◽  
Piecewise Exponential ◽  
Over Time ◽  
Negative Biopsies

Purpose To introduce a novel approach for the time-dependent quantification of risk factors for prostate cancer (PCa) detection after an initial negative biopsy. Patients and Methods Data for 1,871 men with initial negative biopsies and at least one follow-up biopsy were available. Piecewise exponential regression models were developed to quantify hazard ratios (HRs) and define cumulative incidence curves for PCa detection for subgroups with specific patterns of risk factors over time. Factors evaluated included age, race, serum prostate-specific antigen (PSA) concentration, PSA slope, digital rectal examination, dysplastic glands or prostatitis on biopsy, ultrasound gland volume, urinary symptoms, and number of negative biopsies. Results Four hundred sixty-five men had PCa detected, after a mean follow-up time of 2.8 years. All of the factors were independent predictors of PCa detection except for PSA slope, as a result of its correlation with time-dependent PSA level, and race. PSA (HR = 3.90 for > 10 v 2.5 to 3.9 ng/mL), high-grade prostatic intraepithelial neoplasia/atypical glands (HR = 2.97), gland volume (HR = 0.39 for > 50 v < 25 mL), and number of repeat biopsies (HR = 0.36 for two v zero repeat biopsies) were the strongest predictors. Men with high-risk versus low-risk event histories had a 20-fold difference in PCa detection over 5 years. Conclusion Piecewise exponential models provide an approach to longitudinal analysis of PCa risk that allows clinicians to see the interplay of risk factors as they unfold over time for individual patients. With these models, it is possible to identify distinct subpopulations with dramatically different needs for monitoring and repeat biopsy.

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