scholarly journals Urban–Rural Differences in Clinical Characteristics of Prostate Cancer at Initial Diagnosis: A Single-Center Observational Study in Anhui Province, China

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Long Song ◽  
Yinfeng Qian ◽  
Xuhong Min ◽  
Xiao Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Calibration Curve ◽  
Clinical Characteristics ◽  
Disease Free Survival ◽  
Initial Diagnosis ◽  
Anhui Province ◽  
High Grade ◽  
Place Of Residence ◽  
Urban Rural

BackgroundPeople residing in rural areas have higher prostate cancer (PCa) mortality to incidence ratio (M/I) and worse prognosis than those in urban areas of China. Clinical characteristics at initial diagnosis are significantly associated with biochemical recurrence, overall survival, and PCa disease-free survival.ObjectiveThis study aimed at investigating the clinical characteristics at initial diagnosis of urban and rural PCa patients and to establish a logistic regression model for identifying independent predictors for high-grade PCa.Materials and MethodsClinical characteristics for PCa patients were collected from the largest prostate biopsy center in Anhui province, China, from December 2015 to March 2019. First, urban–rural disparities in clinical characteristics were evaluated at initial diagnosis. Second, based on pathological findings, we classified all participants into the benign+ low/intermediate-grade PCa or high-grade PCa groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors for predicting high-grade PCa, while a nomogram for predicting high-grade PCa was generated based on all independent factors. The model was evaluated using area under receiver-operating characteristic (ROC) curve as well as calibration curve analyses and compared to a model without the place of residence factor of individuals.ResultsStatistically significant differences were observed between urban and rural PCa patients with regard to tPSA, PSA density (PSAD), and Gleason score (GS) (p < 0.05). Logistic regression analysis revealed that tPSA [OR = 1.060, 95% confidence interval (CI): 1.024, 1.098], PSAD (OR = 14.678, 95%CI: 4.137, 52.071), place of residence of individuals (OR = 5.900, 95%CI: 1.068, 32.601), and prostate imaging reporting and data system version 2 (PI-RADS v2) (OR = 4.360, 95%CI: 1.953, 9.733) were independent predictive factors for high-grade PCa. The area under the curve (AUC) of the nomogram was greater than that of the model without the place of residence of individuals. The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, suggesting that the prediction model had a better calibration ability.ConclusionsCompared to urban PCa patients, rural PCa patients presented elevated tPSA, PSAD levels, and higher pathological grades. The place of residence of the individuals was an independent predictor for high-grade PCa in Anhui Province, China. Therefore, appropriate strategies, such as narrowing urban-rural gaps in access to health care and increasing awareness on the importance of early detection should be implemented to reduce PCa mortality rates.

Diabetes mellitus, race, and the odds of high-grade prostate cancer in men diagnosed with prostate cancer in the United States.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 180-180 ◽  
Author(s):  
T. Mitin ◽  
M. Chen ◽  
B. J. Moran ◽  
D. E. Dosoretz ◽  
M. J. Katin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diabetes Mellitus ◽  
United States ◽  
Logistic Regression ◽  
African American ◽  
Risk Factor ◽  
The United States ◽  
High Grade ◽  
Newly Diagnosed ◽  
Independent Association

180 Background: African American (AA) men present more frequently with high-grade prostate cancer (PCa) and are also more likely to have diabetes mellitus (DM). We evaluated whether there is an independent association between DM and the risk of high-grade PCa in men diagnosed with PCa, adjusting for the known predictors of high-grade PCa including AA race. Methods: Between 1991 and 2009 15,377 men newly diagnosed with PCa and treated at 1 of 26 centers, were analyzed in 2 cohorts. Multivariable logistic regression was performed to evaluate whether a diagnosis of DM was associated with the odds of Gleason 7 or 8 to 10 PCa, adjusting for AA race, advancing age, PSA level, and DRE findings. Results: AA men (AOR, 1.87; 95% CI, 1.04-3.37, P=0.04) and non-AA men (AOR, 1.61; 95% CI, 1.34-1.93; P<0.001) with diabetes were more likely to have GS 8 to 10 versus GS 6 or less PCa, compared to non-diabetic men. AA as compared to non-AA race was not significantly associated with the odds of having GS 8 to 10 as compared to 6 or less PCa, both in men with a diagnosis of DM (AOR, 1.47; 95% CI, 0.87-2.50; P=0.15) and without DM (AOR, 1.27; 95% CI, 0.92-1.74, P=0.14). AA race, however (AOR, 1.37; 95% CI, 1.17-1.60, P<0.001), but not DM (AOR 1.09; 95% CI, 0.97-1.22, P=0.16), was associated with GS 7 versus 6 or less PCa. Conclusions: A diagnosis of DM is a risk factor for presenting with Gleason 8 to 10 PCa independent of race. [Table: see text] No significant financial relationships to disclose.

scholarly journals Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Xia Zhe ◽  
Min Tang ◽  
Jing Zhang ◽  
Jialiang Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Grade ◽  
High Grade ◽  
Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Clinical utility of biomarkers 4K score, SelectMDx and ExoDx with MRI for the detection of high-grade prostate cancer.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 307-307
Author(s):  
Vittorio Fasulo ◽  
Claire Marie de la Calle ◽  
Janet E. Cowan ◽  
Annika Herlemann ◽  
Carissa Chu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Prostate Biopsy ◽  
Clinical Utility ◽  
Predictive Ability ◽  
Added Value ◽  
Gleason Grade ◽  
High Grade ◽  
Prostate Mri ◽  
New Biomarkers

307 Background: Although adoption of new biomarkers and MRI has become widespread, their utility when deciding to biopsy is unclear. We aim to evaluate and compare 4K, SelectMDx, ExoDx and their added value when combined with prostate MRI in the detection of high-grade prostate cancer (HG PC) and avoidance of unnecessary biopsies. Methods: Patients referred for consideration of prostate biopsy at UCSF between 2016-2019 were enrolled and had either 4K, SelectMDx or ExoDx testing with/without MRI. Logistic regression and AUC were used to determine the performance of each biomarker in detecting HG PC (≥Gleason grade (GG) 3+4). In the subgroup of patients that underwent biopsy, with PSA 2.5-10 and negative DRE, we determined the number of avoided unnecessary biopsies (with GG 3+3 cancer or no cancer) and missed HG PC for each biomarker with/without MRI. Results: A total of 896 patients were enrolled, 457 were biopsied. Mean age was 65.5 years, median PSA was 6.32. Logistic regression showed that having an abnormal biomarker score or PI-RADS 4/5 on MRI (P4/5) was strongly associated with finding HG PC: 4K OR 12.9 (CI 4.58-36.1), ExoDx OR 14.7 (CI 3.31-65.3), SelectMDx OR 3.62 (CI 1.44-9.11), P4/5 OR 6.20 (CI 3.93-9.79), TRUS ≥T2a OR 4.33 (CI 2.78-6.75), PSAD >0.15 OR 4.01 (CI 2.59-6.20), p<0.01). Combining biomarker and P4/5 lesion on MRI increased AUC for detecting HG PC. In the biopsy subgroup, a normal 4K or ExoDx test missed only 4-5% HG PC, while an abnormal test resulted in avoiding 14-20% unnecessary biopsies. Combining MRI with ExoDx or 4K missed 0-1.43% HG PC but avoided only 7-9% unnecessary biopsies (Table). Conclusions: 4K and ExoDx outperformed MRI and SelectMDx but combining the biomarkers with MRI resulted in the best predictive ability for detecting HG PC. Negative MRI avoided more biopsies than a normal 4K or ExoDxbut missed more aggressive cancers. Our data suggest that MRI alone is not sensitive enough to detect all HG PC and that 4K or ExoDx testing alone could be sufficient when deciding to proceed with biopsy.[Table: see text]

467: Differences in Clinical Characteristics and Disease-Free Survival for Latino, African-American, and Non-Latino White Men with Prostate Cancer: Data from Capsure™

2004 ◽  
Vol 171 (4S) ◽  
pp. 124-125 ◽  
Author(s):  
David M. Latini ◽  
Eric P. Elkin ◽  
Matthew R. Cooperberg ◽  
Natalia Sadetsky ◽  
Katrine L. Wallace ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
African American ◽  
Clinical Characteristics ◽  
Disease Free Survival ◽  
White Men ◽  
Free Survival ◽  
Cancer Data ◽  
Disease Free

Differences in clinical characteristics and disease-free survival for Latino, African American, and non-Latino white men with localized prostate cancer

Cancer ◽  
2006 ◽  
Vol 106 (4) ◽  
pp. 789-795 ◽  
Author(s):  
David M. Latini ◽  
Eric P. Elkin ◽  
Matthew R. Cooperberg ◽  
Natalia Sadetsky ◽  
Janeen DuChane ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
African American ◽  
Clinical Characteristics ◽  
Disease Free Survival ◽  
White Men ◽  
Localized Prostate Cancer ◽  
Free Survival ◽  
Disease Free

scholarly journals The predictive value of PI-RADS v2 score in high-grade prostate cancer: a multicenter retrospective study

2020 ◽  
Author(s):  
Song Chen ◽  
Yun Yang ◽  
Tianchen Peng ◽  
Xi Yu ◽  
Haiqing Deng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Cross Validation ◽  
Prostate Volume ◽  
Univariate Analysis ◽  
High Grade ◽  
Independent Risk Factors ◽  
Leave One Out

Abstract Background: To explore the predictive value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with prostate cancer related biomarkers. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off point. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer, the nomogram prediction model may help early diagnose the high risk prostate cancer.

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