scholarly journals Fuzzy Expert System for Prediction of Prostate Cancer

2020 ◽  
Vol 16 (01) ◽  
pp. 163-176
Author(s):  
Juthika Mahanta ◽  
Subhasis Panda
Keyword(s):  
Prostate Cancer ◽  
Expert System ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Fuzzy Expert System ◽  
Knowledge Based ◽  
Free Psa ◽  
Clinical Dataset ◽  
Positive Biopsy

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.

Comparison of PHI and PHI Density for Prostate Cancer Detection in a Large Retrospective Caucasian Cohort

2021 ◽  
pp. 1-6
Author(s):  
Robert Peters ◽  
Carsten Stephan ◽  
Klaus Jung ◽  
Michael Lein ◽  
Frank Friedersdorff ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Roc Curves ◽  
Net Benefit ◽  
Threshold Probability ◽  
Free Psa ◽  
Prostate Health Index ◽  
Caucasian Cohort ◽  
Prostate Health

<b><i>Background:</i></b> Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine. <b><i>Objective:</i></b> The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group &#x3e;1,000 men. <b><i>Methods:</i></b> PHID values were used from available patient data with PSA, free PSA, and [−2]pro­PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA). <b><i>Results:</i></b> PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; <i>p</i> always &#x3c;0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; <i>p</i> = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm<sup>3</sup>, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, <i>p</i> = 0.014). <b><i>Conclusions:</i></b> Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Evaluation of prostate cancer risk in men with PSA < 1.5.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 64-64
Author(s):  
Whitney N. Stanton ◽  
E. David Crawford ◽  
Paul Arangua ◽  
John Hoenemeyer ◽  
Francisco G. La Rosa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Clinical Information ◽  
Mrna Levels ◽  
Protein Biomarkers ◽  
Cancer Awareness ◽  
Free Psa ◽  
Increased Risk ◽  
Total Psa

64 Background: Prostate Specific Antigen (PSA) screening remains controversial primarily because of over detection and treatment. There is an unmet clinical need to identify patients at increased risk for high-grade (HG – Gleason Score ≥7) prostate cancer (PCa) since PSA has low sensitivity. Combining PSA with well-validated prostate cancer biomarkers (PCM) can improve risk assessment. We investigated the performance of three PCMs (phi – prostate health index, 4KScore, and SelectMDx) on patients with PSA levels < 1.5 ng/mL that represent a “safe zone” where risk of any PCa is rare Methods: 652 men were screened for PCa during the annual Prostate Cancer Awareness Week at the University of Colorado Hospital. This study was supported by Prostate Condition Education Council and the Schramm Foundation. phi is evaluated using p2PSA, total PSA, and free PSA in serum. Phi < 52.7 suggests absence of HG PCa. 4KScore incorporates four kallikrein protein biomarkers: total PSA, free PSA, intact PSA, human kallikrein protein, and clinical information. A 4KScore < 20% suggests absence of HG PCa. The SelectMDx post-DRE urine test measures mRNA levels of the homeobox C6 and distal-less homeobox 1 biomarkers. SelectMDx score of 0% indicates absence of HG PCa. Results: No patients with a PSA < 1.5 had SelectMDx > 0% and/or phi > 52.7. One patient had a 4KScore of 27%, indicating a risk for HG PCa. For patients with PSA between 1.5-3.99, 2.9% (4/135), 7.4% (4/54), and 2.3% (2/85) had positive phi, 4KScore, and SelectMDx results, respectively. Conclusions: Men with PSA <1.5 ng/mL are at very low risk for HG PCa. Men with PSA between 1.5-3.99 with positive PCM results may be referred for further evaluation. [Table: see text]

scholarly journals Insulin-Like Growth Factor I Is Not a Useful Marker of Prostate Cancer in Men with Elevated Levels of Prostate-Specific Antigen1

2000 ◽  
Vol 85 (8) ◽  
pp. 2744-2747
Author(s):  
Patrik Finne ◽  
Anssi Auvinen ◽  
Hannu Koistinen ◽  
Wan-Ming Zhang ◽  
Liisa Määttänen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Growth Factor ◽  
Cancer Risk ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Elevated Serum ◽  
Prostate Cancer Risk ◽  
Factor I ◽  
Igf I ◽  
Igfbp 3

High serum levels of insulin-like growth factor I (IGF-I) and low levels of IGF-binding protein-3 (IGFBP-3) have been shown to correlate with increased prostate cancer risk. To evaluate this, IGF-I, IGFBP-3, and prostate-specific antigen (PSA) were measured in serum from 665 consecutive men (179 with prostate cancer), aged 55–67 yr, with elevated serum prostate-specific antigen (PSA; ≥4 μg/L) in a screening trial. Men in the highest quartile of IGF-I levels had an odds ratio (OR) for prostate cancer of 0.50 [95% confidence interval (CI) 0.26–0.97] when adjusting for serum IGFBP-3. IGFBP-3 itself was not significantly associated with prostate cancer risk (OR, 1.24; 95% CI, 0.68–2.24). Prostate volume was larger in men without than in those with prostate cancer (P &lt; 0.001), and after adjustment for prostate volume, the negative association between serum IGF-I and prostate cancer risk was no longer significant (OR, 0.57; 95% CI, 0.28–1.16). In screen-positive men with elevated serum PSA, serum IGF-I is not a useful diagnostic test for prostate cancer, but it may be associated with benign prostatic hyperplasia and enlargement.

Nomograms based on the Tyrol screening data of 2,271 patients to predict prostate cancer biopsy positivity.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 203-203
Author(s):  
P. Sooriakumaran ◽  
M. John ◽  
J. Bektic ◽  
G. Bartsch ◽  
M. Herman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Biopsy ◽  
Predictive Accuracy ◽  
Prostate Volume ◽  
External Validation ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Ability ◽  
Free Psa ◽  
Better Than

203 Background: There are no published nomograms that predict prostate cancer in a screened population. We describe three nomograms that predict for prostate cancer on biopsy derived from a large screening population. Methods: Patients from the Tyrol screening study of known age, total prostate-specific antigen (tPSA), digital rectal examination (DRE), prostate volume, and percent free PSA (%fPSA), and who underwent an initial prostate biopsy from January 1992 to June 2004, were included (n=2271). Multivariable logistic regression models were used to develop the biopsy positivity predictive nomograms: nomogram 1- age, DRE, tPSA; nomogram 2- age, DRE, tPSA, prostate volume; nomogram 3- age, DRE, tPSA, prostate volume, %fPSA. The predictive accuracy of the models was assessed in terms of discrimination and calibration. External validation of the nomograms was performed by comparison with a urologically referred population of patients who underwent prostate biopsy (n=599). Results: All three nomograms discriminated well between biopsy positive and biopsy negative patients for both the screening and urologically referred cohorts (nomogram 3 better than nomogram 2 better than nomogram 1). All three nomograms were well calibrated internally, but the nomograms under-predicted the probability of a positive biopsy in the urologically referred cohort. Conclusions: Our nomogram based on age, total PSA, and DRE has a good predictive ability to differentiate between screened patients that will show cancer on initial prostate biopsy and those that will not. Adding prostate volume and percent free PSA improves this predictive power further. All three nomograms under-predict prostate cancer in a urologically referred cohort. These simple nomograms may be of value in counseling screened men with raised PSA and/or abnormal DRE regarding the need for biopsy. No significant financial relationships to disclose.

scholarly journals Predictive Clinicopathologic Features and Prognostic assessment of pT0 prostate cancer: a case control study

2020 ◽  
Author(s):  
Jiangnan Xu ◽  
Chao Wang ◽  
Jun Ouyang ◽  
Jianglei Zhang ◽  
Zekun Xu
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Clinicopathological Features ◽  
Control Group ◽  
Prognostic Assessment ◽  
Positive Biopsy ◽  
Biopsy Core ◽  
Biopsy Gleason Score

Abstract Background: pT0 prostate cancer is relatively rare. We wanted to share and explore the predictive clinicopathological features and prognosis of biopsy-proven pT0 prostate cancer in Chinese population.Methods: We retrospectively analyzed the clinicopathological and prognostic data of 8 patients with pT0 prostate cancer who received radical prostatectomy (RP) at our institution between 2006 and 2019. pT0 group was compared with a control group of 96 patients who underwent RP during the same period. Exclusion criteria included patients undergoing neoadjuvant hormone therapy or transurethral resection of the prostate (TURP) before the operation．Results: There were significant differences in the exposure rates of six clinicopathological features between two groups. Apart from finasteride use, the other five items were particularly frequent in the pT0 group: prostate-specific antigen (PSA) <10 ng/ml (7/8), one positive biopsy core only (7/8), biopsy Gleason score <7 (8/8), and prostate volume>40ml (7/8), length of biopsy positive for cancer≤2mm. When these five parameters were combined as predictive model, the sensitivity was 75%, the specificity was 99%. The 8 patients were followed up for an average of 67 months without biochemical recurrence or progression.Conclusions: Preoperative PSA, number of positive biopsy core, Gleason score, prostate volume, and the length of cancer can help predict pT0 stage of prostate cancer. Patients with pT0 stage had a relatively favorable prognosis.

Effect of obesity on PSA density (PSAD): A new clinical predictor of prostate cancer

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 4617-4617
Author(s):  
M. Garzotto ◽  
S. Mongoue-Tchokote ◽  
J. Shannon ◽  
L. Peters ◽  
M. H. Sokoloff ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Multivariate Logistic Regression Analysis ◽  
Prostate Cancer Risk ◽  
The United States ◽  
Multivariate Logistic Regression ◽  
Psa Density ◽  
The Mean

4617 Background: The escalating rate of obesity in the Western world presents a diagnostic challenge when screening for prostate cancer. Increased body-mass index (BMI) disrupts the ability to effectively screen this population due to an associated decrease in serum prostate-specific antigen (PSA) levels and an increase in prostate volume. We therefore sought to understand how BMI impacts the probability of harboring prostate cancer. Methods: Data were collected on 647 referred men with a serum PSA of ≤ 10 ng/ml who underwent an ultrasound-guided prostate biopsy. Variables analyzed included: age, BMI, digital rectal exam (DRE), PSA, PSAD (i.e. PSA ÷ prostate volume), prostate volume, hypoechoic lesions on ultrasound and cancer on biopsy. A one-way ANOVA was performed to determine differences in log2 (PSAD) among BMI groups (<25 kg/m2 vs. 25–30 kg/m2 vs. >30 kg/m2). Multivariate logistic regression analysis was performed to estimate the odds-ratio (OR) and 95% confidence intervals (CI). Results: Prostate cancer was detected in 19.2 % of patients. ANOVA showed the mean PSAD to be significantly different in the three BMI groups (F value = 7.1, p = .0009). The mean PSAD significantly decreased as the BMI level increased (p = .0002). Independent pre-biopsy predictors of prostate cancer were tabulated (see below). Conclusions: Obesity was associated with a decrease in PSA density. The multivariate logistic regression revealed that the effect of PSAD on cancer detection was significantly modified by BMI. Specifically, the OR associated with a doubling of PSAD was 1.7 when BMI was <25 kg/m2 vs. 2.1 when BMI was ≥25 kg/m2). This interaction was an independent predictor of prostate cancer risk, along with DRE and ultrasound findings. These data underscore the need to consider BMI as a potential effect modifier of traditional clinical risk factors for prostate cancer. The dramatic rise in obesity in the United States makes this effect modification particularly relevant. [Table: see text] No significant financial relationships to disclose.

scholarly journals Between-Method Differences in Prostate-Specific Antigen Assays Affect Prostate Cancer Risk Prediction by Nomograms

2011 ◽  
Vol 57 (7) ◽  
pp. 995-1004 ◽  
Author(s):  
Carsten Stephan ◽  
Kerstin Siemßen ◽  
Henning Cammann ◽  
Frank Friedersdorff ◽  
Serdar Deger ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Risk Prediction ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Roc Curves ◽  
Median Percentage ◽  
Free Psa ◽  
Different Types ◽  
Total Psa

BACKGROUND To date, no published nomogram for prostate cancer (PCa) risk prediction has considered the between-method differences associated with estimating concentrations of prostate-specific antigen (PSA). METHODS Total PSA (tPSA) and free PSA were measured in 780 biopsy-referred men with 5 different assays. These data, together with other clinical parameters, were applied to 5 published nomograms that are used for PCa detection. Discrimination and calibration criteria were used to characterize the accuracy of the nomogram models under these conditions. RESULTS PCa was found in 455 men (58.3%), and 325 men had no evidence of malignancy. Median tPSA concentrations ranged from 5.5 μg/L to 7.04 μg/L, whereas the median percentage of free PSA ranged from 10.6% to 16.4%. Both the calibration and discrimination of the nomograms varied significantly across different types of PSA assays. Median PCa probabilities, which indicate PCa risk, ranged from 0.59 to 0.76 when different PSA assays were used within the same nomogram. On the other hand, various nomograms produced different PCa probabilities when the same PSA assay was used. Although the ROC curves had comparable areas under the ROC curve, considerable differences were observed among the 5 assays when the sensitivities and specificities at various PCa probability cutoffs were analyzed. CONCLUSIONS The accuracy of the PCa probabilities predicted according to different nomograms is limited by the lack of agreement between the different PSA assays. This difference between methods may lead to unacceptable variation in PCa risk prediction. A more cautious application of nomograms is recommended.

scholarly journals Diagnostic Value of Platelet–To-Lymphocyte Ratio in Prostate Cancer

2019 ◽  
Vol 7 (7) ◽  
pp. 1093-1096 ◽  
Author(s):  
Kharisma Prasetya Adhyatma ◽  
Syah Mirsya Warli
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Diagnostic Value ◽  
Significant Difference ◽  
Diagnostic Values ◽  
Absolute Lymphocyte Counts ◽  
The Relationship

BACKGROUND: Previous studies demonstrated the promising value of platelet-to-lymphocyte (PLR) in prostate cancer. AIM: This study was conducted to evaluate its pre-biopsy values in predicting prostate cancer. METHODS: We included all benign prostatic hyperplasia (BPH) and prostate cancer (PCa) patients who underwent a prostate biopsy in Adam Malik Hospital between August 11th 2011 and August 31st 2015. The relationship between pre-biopsy variables which could be affecting the percentage of prostate cancer risk was evaluated, including age, prostate-specific antigen (PSA) level, and prostate volume (EPV). The PLR was calculated from the ratio of related platelets with their absolute lymphocyte counts. The values then analysed to evaluate their associations with the diagnosis of BPH and PCa. RESULTS: As many as 298 patients consisted of 126 (42.3%) BPH and 172 PCa (57.7%) patients are included in this study. Mean age for both groups are 66.36 ± 7.53 and 67.99 ± 7.48 years old (p = 0.64), respectively. There are statistically significant differences noted from PSA (19.28 ± 27.11 vs 40.19 ± 49.39), EPV (49.39 ± 23.51 vs 58.10 ± 30.54), PLR (160.27 ± 98.96 vs 169.55 ± 78.07), and NLR (3.57 ± 3.23 vs 4.22 ± 2.59) features of both groups (p < 0.05). The AUC of PLR is 57.9% with a sensitivity of 56.4% and specificity of 55.6% in the cut-off point of 143 (p = 0.02). Besides, the NLR cut-off point of 3.08 gives 62.8% AUC with 64.5% sensitivity and 63.5% specificity. We asked for permission from the preceding authors of Indonesian Prostate Cancer Risk Calculator (IPCRC) and calculated its value from 98 randomised patients consist of 45 (45.92%) BPH and 53 (54.08%) PCa. We found a comparable value between PLR/NLR with IPCRC in predicting prostate cancer (AUC of 67.6%, 75.3%, and 68.4%, respectively) with a statistically significant difference of all value in both groups (p < 0.05). CONCLUSIONS: PLR gives promising value in predicting prostate cancer in suspected patients. We suggest a further prospective study to validate its diagnostic values so it can be used as applicable routine calculation.

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