scholarly journals Development of Interpretable Predictive Models for BPH and Prostate Cancer

2015 ◽  
Vol 9 ◽  
pp. CMO.S19739 ◽  
Author(s):  
Pablo Bermejo ◽  
Alicia Vivo ◽  
Pedro J. Tárraga ◽  
J. A. Rodríguez-Montes
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Decision Tree ◽  
Predictive Models ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Statistical Dependence ◽  
P Value ◽  
Prostate Hyperplasia ◽  
Logistic Regression Models

Background Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. Methods An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Results Statistical dependence with PC and BPH was found for prostate volume ( P-value < 0.001), PSA ( P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age ( P-value < 0.002), antecedents ( P-value < 0.006), and meat consumption ( P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. Conclusion PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

scholarly journals Prostate cancer screening: A survey of medical students’ knowledge in Lome, Togo, and associated determinants in a resource-limited African context

2021 ◽  
Vol 9 ◽  
pp. 205031212110328
Author(s):  
Tchin Darré ◽  
Toukilnan Djiwa ◽  
Tchilabalo Matchonna Kpatcha ◽  
Albadia Sidibé ◽  
Edoé Sewa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Screening ◽  
Medical Students ◽  
Confidence Interval ◽  
Prostate Cancer Screening ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
P Value ◽  
Theoretical Knowledge ◽  
Practical Level

Objectives: The aims of this study were to assess the knowledge of medical students in Lomé about these means of screening for prostate cancer in a context of limited resources and controversy about prostate cancer screening, and to identify the determinants associated with these results. Methods: This was a prospective descriptive and cross-sectional study conducted in the form of a survey of medical students regularly enrolled at the Faculty of Health Sciences of the University of Lomé for the 2019–2020 academic years. Results: Of the 1635 eligible students, 1017 correctly completed the form, corresponding to a rate of 62.20%. The average age was 22 ± 3.35 years. The sex ratio (M/F) was 2.5. Undergraduate students were the most represented (53.69%). Students who had not received any training on prostate cancer were the most represented (57.13%). Only 12.88% of the students had completed a training course in urology. Concerning the prostate-specific antigen blood test, there was a statistically significant relationship between the students’ knowledge and some of their socio-demographic characteristics, namely age (p value = 0.0037; 95% confidence interval (0.50–1.77)); gender (p value = 0.0034; 95% confidence interval (1.43–2.38)); study cycle (p value ˂ 0.0001; 95% confidence interval (0.56–5.13)) and whether or not they had completed a placement in a urology department (p value ˂ 0.0001; 95% confidence interval (0.49–1.55)). On the contrary, there was no statistically significant relationship between students’ knowledge of the digital rectal examination and their study cycle (p value = 0.082; 95% confidence interval (0.18–3.44)). Conclusion: Medical students in Lomé have a good theoretical knowledge and a fair practical level of the digital rectal examination clinical examination and an average theoretical knowledge and a below average practical level of prostate-specific antigen, increasing however along the curriculum in the context of prostate cancer screening.

scholarly journals Estimation of Prostate Cancer Probability by Logistic Regression: Free and Total Prostate-specific Antigen, Digital Rectal Examination, and Heredity Are Significant Variables

1999 ◽  
Vol 45 (7) ◽  
pp. 987-994 ◽  
Author(s):  
Arja Virtanen ◽  
Mehran Gomari ◽  
Ries Kranse ◽  
Ulf-Håkan Stenman
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Clinical Findings ◽  
Explanatory Variables ◽  
Rectal Examination ◽  
Screening Population ◽  
Total Psa

Abstract Background: Despite low specificity, serum prostate-specific antigen (PSA) is widely used in screening for prostate cancer. Specificity can be improved by measuring free and total PSA and by combining these results with clinical findings. Methods such as neural networks and logistic regression are alternatives to multistep algorithms for clinical use of the combined findings. Methods: We compared multilayer perceptron (MLP) and logistic regression (LR) analysis for predicting prostate cancer in a screening population of 974 men, ages 55–66 years. The study sample comprised men with PSA values &gt;3 μg/L. Explanatory variables considered were age, free and total PSA and their ratio, digital rectal examination (DRE), transrectal ultrasonography, and a family history of prostate cancer. Results: When at least 90% sensitivity in the training sets was required, the mean sensitivity and specificity obtained were 87% and 41% with LR and 85% and 26% with MLP, respectively. The cancer specificity of an LR model comprising the proportion of free to total PSA, DRE, and heredity as explanatory variables was significantly better than that of total PSA and the proportion of free to total PSA (P &lt;0.01, McNemar test). The proportion of free to total PSA, DRE, and heredity were used to prepare cancer probability curves. Conclusion: The probability calculated by logistic regression provides better diagnostic accuracy for prostate cancer than the presently used multistep algorithms for estimation of the need to perform biopsy.

scholarly journals Decision tree analysis for prostate cancer prediction

2019 ◽  
Vol 147 (1-2) ◽  
pp. 52-58
Author(s):  
Miroslav Stojadinovic ◽  
Milorad Stojadinovic ◽  
Damjan Pantic
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Biopsy ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Cart Model

Introduction/Objective. The use of serum prostate-specific antigen (PSA) test has dramatically increased the number of men undergoing prostate biopsy. However, the best possible strategies for selecting appropriate patients for prostate biopsy have yet to be defined. The aim of the study was to develop a classification and regression tree (CART) model that could be used to identify patients with significant prostate cancer (PCa) on prostate biopsy in patients referred due to abnormal PSA, digital rectal examination (DRE) findings, or both, regardless of the PSA level. Methods. The data on clinicopathological characteristics regarding prebiopsy assessment collected from patients who had undergone ultrasound-guided prostate biopsies included the following: age, PSA, DRE, volume of the prostate, and PSA density (PSAD). The CART analysis was carried out using all predictors identified by univariate logistic regression analysis. Different aspects of predictive performance and clinical utility risk prediction model were assessed. Results. In this retrospective study, significant PCa was detected in 92 (41.6%) out of 221 patients. The CART model had three splits based on PSAD, as the most decisive variable, prostate volume, DRE, and PSA. Our model resulted in an 83.3% area under the receiver operating characteristic curve. Decision curve analysis showed that the regression tree provided net benefit for relevant threshold probabilities compared with the logistic regression model, PSAD, and the strategy of biopsying all patients. Conclusion. The model helps to reduce unnecessary biopsies without missing significant PCa.

Alpha-methylacyl-CoA Racemase and Hepsin as Urinary Prostate Cancer Markers

2015 ◽  
Vol 30 (4) ◽  
pp. 401-406 ◽  
Author(s):  
Wiktor Dariusz Sroka ◽  
Marek Adamowski ◽  
Piotr Słupski ◽  
Joanna Siódmiak ◽  
Piotr Jarzemski ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
False Negative ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Tumor Stage ◽  
Urine Samples ◽  
True Positive ◽  
Prostate Hyperplasia ◽  
Before And After

Background Because of the numerous limitations of prostate-specific antigen (PSA), α-methylacyl-CoA racemase (AMACR) and hepsin have recently been suggested as potential biomarkers in prostate cancer (PC). This report presents a comparison study of the presence of AMACR and hepsin in urine collected before and after digital rectal examination (DRE) as a previously suggested diagnostic marker for PC. Methods Seventy-six urine samples (38 before and 38 after prostate massage) from patients with benign prostate hyperplasia (BPH) and 66 urine samples (33 before and 33 after prostate massage) from patients with PC were analyzed. PC was confirmed by prostate biopsy. Urinary levels of AMACR and hepsin were determined by ELISA and related to the tumor stage, Gleason score and PSA level. Results AMACR and hepsin levels in urine collected after prostate massage were higher only in the PC group. There were no correlations between AMACR levels, hepsin levels, tumor stage and Gleason score. AMACR and hepsin did not differentiate between BPH and PC with better true positive and false negative rates than serum PSA. Conclusions AMACR and hepsin were unable to diagnose PC with better true positive and false negative rates than PSA. An additional procedure combined with other markers should be applied for the reliable diagnosis of PC.

scholarly journals The prediction of Prostate Cancer Based on Normal Digital Rectal Examination and Normal Prostate Specific Antigen in Clinical Benign Prostate Hyperplasia

2018 ◽  
Vol 5 (4) ◽  
pp. 3760-3763
Author(s):  
Zuhirman Zamzami
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Benign Prostate Hyperplasia ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Ethical Review ◽  
Normal Prostate ◽  
Prostate Hyperplasia ◽  
Prostate Enlargement ◽  
Benign Prostate

Purpose: To evaluate the prediction of prostate cancer based on normal digital examination (DRE) and normal prostate specific antigen (PSA) in clinical Benign Prostate Hyperplasia. Materials and Methods: We reviewed medical records of prostate cancer in prostate enlargement patients with urinary retension underwent transurethral resection of the prostate  (TURP) based on  normal DRE, and normal PSA in Arifin Achmad Regional General Hospital, Pekanbaru, Riau Province, Indonesia in January 2010 – Desember 2016. Statistical analysis of univariate was used. Approval on the study was obtained from the Ethical Review Board for Medicine and Health Research, Medical Faculty, University of Riau. Results: There were 644 prostate enlargement patients with urinary retension underwent TURP) in this study in which mostly (51%) in 60-69 year age group,  Most (69.7%) DRE were normal and PSA levels of ≤ 4 ng/ml  were in 122 (19%) patients. There were 19 (18.5%) prostate cancer in patients with normal DRE and PSA. Conclusion: We found there were 19 (18.5%) prostate cancers in prostate enlargement patients with normal DRE and PSA findings as the prediction.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

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