1
|
Ruan M, Liu Y, Yao K, Wang K, Fan Y, Wu S, Wang X. Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3. J Magn Reson Imaging 2024; 60:2130-2141. [PMID: 38363125 DOI: 10.1002/jmri.29275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required. PURPOSE To develop and validate machine learning (ML) models based on clinical and imaging parameters for csPCa detection in patients with PI-RADS v2.1 ≥ 3. STUDY TYPE Retrospective. SUBJECTS One thousand eighty-three patients with PI-RADS v2.1 ≥ 3, randomly split into training (70%, N = 759) and validation (30%, N = 324) datasets, and 147 patients enrolled prospectively for testing. FIELD STRENGTH/SEQUENCE 3.0 T scanners/T2-weighted fast spin echo sequence and DWI with diffusion-weighted single-shot gradient echo planar imaging sequence. ASSESSMENT The factors evaluated for csPCa detection were age, prostate specific antigen, prostate volume, and the diameter and location of the index lesion, PI-RADSv2.1. Five ML models for csPCa detection were developed: logistic regression (LR), extreme gradient boosting, random forest (RF), decision tree, and support vector machines. The csPCa was defined as Gleason grade ≥2. STATISTICAL TESTS Univariable and multivariable LR analyses to identify parameters associated with csPCa. Area under the receiver operating characteristic curve (AUC), Brier score, and DeLong test were used to assess and compare the csPCa diagnostic performance with the LR model. The significance level was defined as 0.05. RESULTS The RF model exhibited the highest AUC (0.880-0.904) and lowest Brier score (0.125-0.133) among the ML models in the validation and testing cohorts, however, there was no difference when compared to the LR model (P = 0.453 and 0.548). The sensitivity and negative predictive values in the validation and testing cohorts were 93.8%-97.6% and 82.7%-95.1%, respectively, at a threshold of 0.450 (99% sensitivity of the RF model). DATA CONCLUSION The RF model might help for assessing the risk of csPCa and preventing overdiagnosis and unnecessary biopsy for men with PI-RADSv2.1 ≥ 3. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Mingjian Ruan
- Department of Urology, Peking University First Hospital, Beijing, China
- Institute of Urology, Peking University, Beijing, China
- National Urological Cancer Center of China, Beijing, China
| | - Yi Liu
- Department of Urology, Peking University First Hospital, Beijing, China
- Institute of Urology, Peking University, Beijing, China
- National Urological Cancer Center of China, Beijing, China
| | - Kaifeng Yao
- Department of Urology, Peking University First Hospital, Beijing, China
- Institute of Urology, Peking University, Beijing, China
- National Urological Cancer Center of China, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Yu Fan
- Department of Urology, Peking University First Hospital, Beijing, China
- Institute of Urology, Peking University, Beijing, China
- National Urological Cancer Center of China, Beijing, China
- Drug Clinical Trial Institution, Peking University First Hospital, Beijing, China
| | - Shiliang Wu
- Department of Urology, Peking University First Hospital, Beijing, China
- Institute of Urology, Peking University, Beijing, China
- National Urological Cancer Center of China, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| |
Collapse
|
2
|
Denijs FB, van Harten MJ, Meenderink JJL, Leenen RCA, Remmers S, Venderbos LDF, van den Bergh RCN, Beyer K, Roobol MJ. Risk calculators for the detection of prostate cancer: a systematic review. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-024-00852-w. [PMID: 38830997 DOI: 10.1038/s41391-024-00852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm. METHODS We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men. RESULTS We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93. CONCLUSIONS This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.
Collapse
Affiliation(s)
- Frederique B Denijs
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Meike J van Harten
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonas J L Meenderink
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Renée C A Leenen
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lionne D F Venderbos
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Roderick C N van den Bergh
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Katharina Beyer
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Monique J Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
3
|
Panaiyadiyan S, Kumar R. Prostate cancer nomograms and their application in Asian men: a review. Prostate Int 2024; 12:1-9. [PMID: 38523898 PMCID: PMC10960090 DOI: 10.1016/j.prnil.2023.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 03/26/2024] Open
Abstract
Nomograms help to predict outcomes in individual patients rather than whole populations and are an important part of evaluation and treatment decision making. Various nomograms have been developed in malignancies to predict and prognosticate clinical outcomes such as severity of disease, overall survival, and recurrence-free survival. In prostate cancer, nomograms were developed for determining need for biopsy, disease course, need for adjuvant therapy, and outcomes. Most of these predictive nomograms were based on Caucasian populations. Prostate cancer is significantly affected by race, and Asian men have a significantly different racial and genetic susceptibility compared to Caucasians, raising the concern in generalizability of these nomograms. We reviewed the existing literature for nomograms in prostate cancer and their application in Asian men. There are very few studies that have evaluated the applicability and validity of the existing nomograms in these men. Most have found significant differences in the performance in this population. Thus, more studies evaluating the existing nomograms in Asian men or suggesting modifications for this population are required.
Collapse
Affiliation(s)
- Sridhar Panaiyadiyan
- Department of Urology, All India Institute of Medical Sciences, New Delhi, India
| | - Rajeev Kumar
- Department of Urology, All India Institute of Medical Sciences, New Delhi, India
| |
Collapse
|
4
|
Wang CM, Yuan L, Liu XH, Chen SQ, Wang HF, Dong QF, Zhang B, Huang MS, Zhang ZY, Xiao J, Tao T. Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data. Asian J Androl 2024; 26:34-40. [PMID: 37750785 PMCID: PMC10846831 DOI: 10.4103/aja202342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/25/2023] [Indexed: 09/27/2023] Open
Abstract
The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.
Collapse
Affiliation(s)
- Chang-Ming Wang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Lei Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Xue-Han Liu
- Core Facility Center for Medical Sciences, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Shu-Qiu Chen
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210009, China
| | - Hai-Feng Wang
- Department of Urology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200000, China
| | - Qi-Fei Dong
- Department of Urology, Affiliated Anhui Provincial Hospital of Anhui Medical University, Hefei 230001, China
| | - Bin Zhang
- Department of Urology, Affiliated Anhui Provincial Hospital of Anhui Medical University, Hefei 230001, China
| | - Ming-Shuo Huang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Zhi-Yong Zhang
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Jun Xiao
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Tao Tao
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| |
Collapse
|
5
|
Ge Q, Zhang S, Xu H, Zhang J, Fan Z, Li W, Shen D, Xiao J, Wei Z. Development and validation of a novel nomogram predicting clinically significant prostate cancer in biopsy-naive men based on multi-institutional analysis. Cancer Med 2023; 12:21820-21829. [PMID: 38014481 PMCID: PMC10757090 DOI: 10.1002/cam4.6750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/31/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Prediction of clinically significant prostate cancer (csPCa) is essential to select biopsy-naive patients for prostate biopsy. This study was to develop and validate a nomogram based on clinicodemographic parameters and exclude csPCa using prostate-specific antigen density (PSAD) stratification. METHODS Independent predictors were determined via univariate and multivariate logistic analysis and adopted for developing a predictive nomogram, which was assessed in terms of discrimination, calibration, and net benefit. Different PSAD thresholds were used for deciding immediate biopsies in patients with Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. RESULTS A total of 932 consecutive patients who underwent ultrasound-guided transperineal cognitive biopsy were enrolled in our study. In the development cohort, age (odds ratio [OR], 1.075; 95% confidence interval [CI], 1.036-1.114), PSAD (OR, 6.003; 95% CI, 2.826-12.751), and PI-RADS (OR, 3.419; 95% CI, 2.453-4.766) were significant predictors for csPCa. On internal and external validation, this nomogram showed high areas under the curve of 0.943, 0.922, and 0.897, and low Brier scores of 0.092, 0.102, and 0.133 and insignificant unreliability tests of 0.713, 0.490, and 0.859, respectively. Decision curve analysis revealed this model could markedly improve clinical net benefit. The probability of excluding csPCa was 98.51% in patients with PI-RADS 3 lesions and PSAD <0.2 ng/ml2 . CONCLUSION This novel nomogram including age, PSAD, and PI-RADS could be applied to accurately predict csPCa, and 44.08% of patients with equivocal imaging findings plus PSAD <0.2 ng/ml2 could safely forgo biopsy.
Collapse
Affiliation(s)
- Qingyu Ge
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe First Affiliated Hospital of USTC, University of Science and Technology of ChinaHefeiAnhuiChina
| | - Sicong Zhang
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| | - Hewei Xu
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| | - Junjie Zhang
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| | - Zongyao Fan
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| | - Weilong Li
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| | - Deyun Shen
- Department of UrologyThe First Affiliated Hospital of USTC, University of Science and Technology of ChinaHefeiAnhuiChina
| | - Jun Xiao
- Department of UrologyThe First Affiliated Hospital of USTC, University of Science and Technology of ChinaHefeiAnhuiChina
| | - Zhongqing Wei
- Department of UrologyThe Second Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsuChina
- Department of UrologyThe Second Clinical Medical College of Nanjing Medical UniversityNanjingJiangsuChina
| |
Collapse
|
6
|
Wang C, Yuan L, Shen D, Zhang B, Wu B, Zhang P, Xiao J, Tao T. Combination of PI-RADS score and PSAD can improve the diagnostic accuracy of prostate cancer and reduce unnecessary prostate biopsies. Front Oncol 2022; 12:1024204. [PMID: 36465344 PMCID: PMC9709422 DOI: 10.3389/fonc.2022.1024204] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 10/20/2022] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES The purpose of this study is to evaluate the diagnostic accuracy of the clinical variables of patients with prostate cancer (PCa) and to provide a strategy to reduce unnecessary biopsies. PATIENTS AND METHODS A Chinese cohort that consists of 833 consecutive patients who underwent prostate biopsies from January 2018 to April 2022 was collected in this retrospective study. Diagnostic ability for total PCa and clinically significant PCa (csPCa) was evaluated by prostate imaging-reporting and data system (PI-RADS) score and other clinical variables. Univariate and multivariable logistic regression analyses were performed to figure out the independent predictors. Diagnostic accuracy was estimated by plotting receiver operating characteristic curves. RESULTS The results of univariate and multivariable analyses demonstrated that the PI-RADS score (P < 0.001, OR: 5.724, 95% CI: 4.517-7.253)/(P < 0.001, OR: 5.199, 95% CI: 4.039-6.488) and prostate-specific antigen density (PSAD) (P < 0.001, OR: 2.756, 95% CI: 1.560-4.870)/(P < 0.001, OR: 4.726, 95% CI: 2.661-8.396) were the independent clinical factors for predicting total PCa/csPCa. The combination of the PI-RADS score and PSAD presented the best diagnostic performance for the detection of PCa and csPCa. For the diagnostic criterion of "PI-RADS score ≥ 3 or PSAD ≥ 0.3", the sensitivity and negative predictive values were 94.0% and 93.1% for the diagnosis of total PCa and 99.2% and 99.3% for the diagnosis of csPCa, respectively. For the diagnostic criterion "PI-RADS score >3 and PSAD ≥ 0.3", the specificity and positive predictive values were 96.8% and 92.6% for the diagnosis of total PCa and 93.5% and 82.4% for the diagnosis of csPCa, respectively. CONCLUSIONS The combination of the PI-RADS score and PSAD can implement the extraordinary diagnostic performance of PCa. Many patients may safely execute active surveillance or take systematic treatment without prostate biopsies by stratification according to the PI-RADS score and the value of PSAD.
Collapse
Affiliation(s)
- Changming Wang
- Department of Urology, The First Affiliated Hospital of USTC of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lei Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Deyun Shen
- Department of Urology, The First Affiliated Hospital of USTC of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Bin Zhang
- Department of Urology, Affiliated Anhui Provincial Hospital of Anhui Medical University, Hefei, China
| | - Baorui Wu
- Department of Urology, The First Affiliated Hospital of USTC of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Panrui Zhang
- Hefei National Laboratory for Physical Sciences at Microscale, The CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Jun Xiao
- Department of Urology, The First Affiliated Hospital of USTC of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Urology, Affiliated Anhui Provincial Hospital of Anhui Medical University, Hefei, China
| | - Tao Tao
- Department of Urology, The First Affiliated Hospital of USTC of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| |
Collapse
|
7
|
Bandala-Jacques A, Castellanos Esquivel KD, Pérez-Hurtado F, Hernández-Silva C, Reynoso-Noverón N. Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review. JMIR Cancer 2021; 7:e30430. [PMID: 34477564 PMCID: PMC8449298 DOI: 10.2196/30430] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/12/2021] [Accepted: 07/28/2021] [Indexed: 11/15/2022] Open
Abstract
Background Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources Objective The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models’ overall performance. Methods We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model’s overall accuracy. Results We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history. Conclusions Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented. Trial Registration PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110
Collapse
Affiliation(s)
- Antonio Bandala-Jacques
- Centro de Investigación en Prevención, Instituto Nacional de Cancerología, Mexico City, Mexico.,Centro de Investigación en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | | | - Fernanda Pérez-Hurtado
- Centro de Investigación en Prevención, Instituto Nacional de Cancerología, Mexico City, Mexico
| | | | - Nancy Reynoso-Noverón
- Centro de Investigación en Prevención, Instituto Nacional de Cancerología, Mexico City, Mexico
| |
Collapse
|
8
|
Guo H, Jia X, Liu H. Based on biomedical index data: Risk prediction model for prostate cancer. Medicine (Baltimore) 2021; 100:e25602. [PMID: 33907111 PMCID: PMC8084031 DOI: 10.1097/md.0000000000025602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 02/12/2021] [Accepted: 04/02/2021] [Indexed: 11/30/2022] Open
Abstract
ABSTRACT To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.
Collapse
Affiliation(s)
- Hanxu Guo
- School of Clinical Medicine, Bengbu Medical College
| | - Xianjie Jia
- Department of Epidemiology and Health Statistics, School of Public Health, Bengbu Medical College
| | - Hao Liu
- Department of Pharmacy, Bengbu Medical College, Anhui Biochemical Drug Engineering Technology Research Center, Bengbu, China
| |
Collapse
|
9
|
Chen Y, Zhou Z, Zhou Y, Wu X, Xiao Y, Ji Z, Li H, Yan W. Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy. BMC Urol 2021; 21:68. [PMID: 33892696 PMCID: PMC8063345 DOI: 10.1186/s12894-021-00840-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/12/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Due to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Most existing models are based on transrectal ultrasonography (TRUS)-guided biopsy. On the other hand, transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB. RESULT A total of 1352 out of 3794 (35.6%) patients were diagnosed with prostate cancer, 848 of whom had tumour with Grade Group 2-5. Age, PSA, PV, DRE and f/t PSA are independent predictors of HGPC with p < 0.001. The model showed good discrimination ability (c-index 0.886) and calibration during internal validation and good clinical performance was observed through decision curve analysis. The external validation of CPCC-RC, an existing model, demonstrated that models based on TRUS-guided biopsy may underestimate the risk of HGPC in patients who underwent TTPB. CONCLUSION We established a prediction model which showed good discrimination ability and calibration in predicting the detection of HGPC by initial TTPB. This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability.
Collapse
Affiliation(s)
- Yuliang Chen
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Zhien Zhou
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yi Zhou
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Xingcheng Wu
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Yu Xiao
- The Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Zhigang Ji
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Hanzhong Li
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Weigang Yan
- The Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
| |
Collapse
|
10
|
He BM, Chen R, Sun TQ, Yang Y, Zhang CL, Ren SC, Gao X, Sun YH. Prostate cancer risk prediction models in Eastern Asian populations: current status, racial difference, and future directions. Asian J Androl 2021; 22:158-161. [PMID: 31187780 PMCID: PMC7155801 DOI: 10.4103/aja.aja_55_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Prostate cancer (PCa) risk calculators (RCs) with prostate-specific antigen (PSA) and other risk factors can greatly improve the accurate prediction of potential risk of PCa compared to PSA. The European Randomized Study of Screening for PCa Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) are developed on the Western population. However, the Western RCs showed limited diagnostic efficacy in the Eastern Asian population, mainly due to racial differences between the two populations. We aimed to review the application of Western RCs and Eastern Asian RCs in Eastern Asian cohorts and to identify the characteristics and efficacy of these RCs.
Collapse
Affiliation(s)
- Bi-Ming He
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Rui Chen
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Tian-Qi Sun
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yue Yang
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Chun-Lei Zhang
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Shan-Cheng Ren
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Xu Gao
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Ying-Hao Sun
- Department of Urology, Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| |
Collapse
|
11
|
Yu S, Hong G, Tao J, Shen Y, Liu J, Dong B, Fan Y, Li Z, Zhu A, Zhang X. Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy. Front Oncol 2020; 10:575261. [PMID: 33262944 PMCID: PMC7688051 DOI: 10.3389/fonc.2020.575261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/14/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose We sought to develop diagnostic models incorporating mpMRI examination to identify PCa (Gleason score≥3+3) and CSPCa (Gleason score≥3+4) to reduce overdiagnosis and overtreatment. Methods We retrospectively identified 784 patients according to inclusion criteria between 2016 and 2020. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Age, PSA derivatives, prostate volume, and mpMRI parameters were assessed as predictors for PCa and CSPCa. The multivariable models based on clinical parameters were evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Results Univariate analysis showed that age, tPSA, PSAD, prostate volume, MRI-PCa, MRI-seminal vesicle invasion, and MRI-lymph node invasion were significant predictors for both PCa and CSPCa (each p≤0.001). PSAD has the highest diagnostic accuracy in predicting PCa (AUC=0.79) and CSPCa (AUC=0.79). The multivariable models for PCa (AUC=0.92, 95% CI: 0.88–0.96) and CSPCa (AUC=0.95, 95% CI: 0.92–0.97) were significantly higher than the combination of derivatives for PSA (p=0.041 and 0.009 for PCa and CSPCa, respectively) or mpMRI (each p<0.001) in diagnostic accuracy. And the multivariable models for PCa and CSPCa illustrated better calibration and substantial improvement in DCA at threshold above 10%, compared with PSA or mpMRI derivatives. The PCa model with a 30% cutoff or CSPCa model with a 20% cutoff could spare the number of biopsies by 53%, and avoid the number of benign biopsies over 80%, while keeping a 95% sensitivity for detecting CSPCa. Conclusion Our multivariable models could reduce unnecessary biopsy without comprising the ability to diagnose CSPCa. Further prospective validation is required.
Collapse
Affiliation(s)
- Shuanbao Yu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guodong Hong
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jin Tao
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Shen
- Department of Nosocomial Infection Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junxiao Liu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Biao Dong
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yafeng Fan
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ziyao Li
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ali Zhu
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuepei Zhang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| |
Collapse
|
12
|
Wu YS, Fu XJ, Na R, Ye DW, Qi J, Lin XL, Liu F, Gong J, Zhang N, Jiang GL, Jiang HW, Ding Q, Xu J, Sun YH. Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population. Asian J Androl 2020; 21:592-597. [PMID: 30924451 PMCID: PMC6859657 DOI: 10.4103/aja.aja_125_18] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.
Collapse
Affiliation(s)
- Yi-Shuo Wu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China.,Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Xiao-Jian Fu
- Department of Surgery, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Rong Na
- Department of Urology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Ding-Wei Ye
- Department of Urology, Shanghai Cancer Center, Fudan University, Shanghai 200032, China
| | - Jun Qi
- Department of Urology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200092, China
| | - Xiao-Ling Lin
- Urology Research Center, Fudan University, Shanghai 200040, China
| | - Fang Liu
- Urology Research Center, Fudan University, Shanghai 200040, China
| | - Jian Gong
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China
| | - Ning Zhang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China
| | - Guang-Liang Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China
| | - Hao-Wen Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China
| | - Qiang Ding
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.,Urology Research Center, Fudan University, Shanghai 200040, China
| | - Jianfeng Xu
- Urology Research Center, Fudan University, Shanghai 200040, China.,Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Ying-Hao Sun
- Department of Urology, Changhai Hospital, The Second Military Medical University, Shanghai 200433, China
| |
Collapse
|
13
|
Chen R, Verbeek JFM, Yang Y, Song Z, Sun Y, Roobol MJ. Comparing the prediction of prostate biopsy outcome using the Chinese Prostate Cancer Consortium (CPCC) Risk Calculator and the Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer (ERSPC) Risk Calculator in Chinese and European men. World J Urol 2020; 39:73-80. [PMID: 32279141 DOI: 10.1007/s00345-020-03177-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 03/20/2020] [Indexed: 10/24/2022] Open
Abstract
PURPOSE To externally validate the clinical utility of Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) and Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (A-ERSPC-RC3) for prediction prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 3 + 4) in both Chinese and European populations. MATERIALS AND METHODS The Chinese clinical cohort, the European population-based screening cohort, and the European clinical cohort included 2,508, 3,616 and 617 prostate biopsy-naive men, respectively. The area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analyses were applied in the analysis. RESULTS The CPCC-RC's predictive ability for any PCa (AUC 0.77, 95% CI 0.75-0.79) was lower than the A-ERSPC-RC3 (AUC 0.79, 95% CI 0.77-0.81) in the European screening cohort (p < 0.001), but similar for HGPCa (p = 0.24). The CPCC-RC showed lower predictive accuracy for any PCa (AUC 0.65, 95% CI 0.61-0.70), but acceptable predictive accuracy for HGPCa (AUC 0.73, 95% CI 0.69-0.77) in the European clinical cohort. The A-ERSPC-RC3 showed an AUC of 0.74 (95% CI 0.72-0.76) in predicting any PCa, and a similar AUC of 0.74 (95% CI 0.72-0.76) in predicting HGPCa in Chinese cohort. In the Chinese population, decision curve analysis revealed a higher net benefit for CPCC-RC than A-ERSPC-RC3, while in the European screening and clinical cohorts, the net benefit was higher for A-ERSPC-RC3. CONCLUSIONS The A-ERSPC-RC3 accurately predict the prostate biopsy in a contemporary Chinese multi-center clinical cohort. The CPCC-RC can predict accurately in a population-based screening cohort, but not in the European clinical cohort.
Collapse
Affiliation(s)
- Rui Chen
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Changhai Road 168, Yangpu District, Shanghai, 200433, China
| | - Jan F M Verbeek
- Department of Urology, Erasmus University Medical Center, Room Na1706, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Yue Yang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Changhai Road 168, Yangpu District, Shanghai, 200433, China
| | - Zijian Song
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Changhai Road 168, Yangpu District, Shanghai, 200433, China
| | - Yinghao Sun
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Changhai Road 168, Yangpu District, Shanghai, 200433, China.
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, Room Na1706, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
| |
Collapse
|
14
|
Yang Y, Jia B, Zhao X, Wang Y, Ye W. miR-93-5p may be an important oncogene in prostate cancer by bioinformatics analysis. J Cell Biochem 2018; 120:10463-10483. [PMID: 30582208 DOI: 10.1002/jcb.28332] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 11/29/2018] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Prostate adenocarcinoma is one of the most prevalent causes of cancer-related deaths in males worldwide. However, the underlying mechanisms remain poorly understood. Hence, it is important to identify specific and effective therapeutic targets, to be able to determine appropriate therapy and management. So, this study aimed to predict that miR-93-5p is an important oncogene in prostate cancer by bioinformatics analysis. METHODS In this study, initially we identified differentially expressed genes (DEGs) and differently expressed miRNAs (DEMs) in the The Cancer Genome Atlas (TCGA) database, performed Gene Ontology (GO) and pathway enrichment analysis, then investigated the relationship between DEGs and DEMs, and finally through consulting the literature and retrieving the database, we found that miR-93-5p may play a major role in prostate cancer, so we predicted the expression and survival of miR-93-5p and its isomers by bioinformatics analysis, meanwhile, evaluated the function of miR-93-5p in vitro. RESULTS In total, 104 DEMs were differently expressed between prostate cancer and normal samples, including 56 downregulated ones and 48 upregulated ones; miR-93-5p (upregulated) was identified as a good biomarker. And 1904 DEGs were retrieved, including 794 downregulated ones and 1110 upregulated ones. We also obtained 1254 DEGs of the DEMs. In GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the significantly enriched pathways involved pathway in focal adhesion, vascular smooth muscle contraction, and regulation of actin cytoskeleton. By the KEGG pathway, we got 16 target genes and 92 pathways of the miR-93-5p in prostate cancer. We also found that the miR-93-5p and its isomers can express in prostate cancer, and which with a high expression had a poor overall survival and a significant difference recurrence rate within 5 years. Further in vitro verification results demonstrated that the low expression of miR-93-5p can inhibit cell proliferation, migration, invasion, change cell cycle, and promote early apoptosis of PC-3 cells. CONCLUSION The miR-93-5p and its target genes were used to define important molecular targets that could serve as a prognostic and predictive marker in the treatment of prostate cancer. Further research on the function of the miR-93-5p and its target genes in the KEGG pathway could provide references for the treatment of prostate cancer.
Collapse
Affiliation(s)
- Yuemei Yang
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Binghan Jia
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Xiaoling Zhao
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Yao Wang
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Weiliang Ye
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| |
Collapse
|
15
|
De Nunzio C, Lombardo R, Tema G, Alkhatatbeh H, Gandaglia G, Briganti A, Tubaro A. External validation of Chun, PCPT, ERSPC, Kawakami, and Karakiewicz nomograms in the prediction of prostate cancer: A single center cohort-study. Urol Oncol 2018; 36:364.e1-364.e7. [DOI: 10.1016/j.urolonc.2018.05.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 04/01/2018] [Accepted: 05/08/2018] [Indexed: 12/27/2022]
|
16
|
Yeboah F, Acheampong E, Gyasi-Sarpong C, Aboah K, Laing E, Obirikorang C, Frimpong B, Amoah G, Batu E, Anto E, Amankwaah B. Nomogram for predicting the probability of the positive outcome of prostate biopsies among Ghanaian men. AFRICAN JOURNAL OF UROLOGY 2018. [DOI: 10.1016/j.afju.2017.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
17
|
Zhao J, Liu S, Gao D, Ding S, Niu Z, Zhang H, Huang Z, Qiu J, Li Q, Li N, Xie F, Cui J, Lu J. Risk assessment models to evaluate the necessity of prostate biopsies in North Chinese patients with 4-50 ng/mL PSA. Oncotarget 2018; 8:9935-9946. [PMID: 28039477 PMCID: PMC5354782 DOI: 10.18632/oncotarget.14214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 11/23/2016] [Indexed: 11/25/2022] Open
Abstract
Background Prostate-specific antigen (PSA) is widely used for prostate cancer screening, but low specificity results in high false positive rates of prostate biopsies. Objective To develop new risk assessment models to overcome the diagnostic limitation of PSA and reduce unnecessary prostate biopsies in North Chinese patients with 4–50 ng/mL PSA. Methods A total of 702 patients in seven hospitals with 4–10 and 10–50 ng/mL PSA, respectively, who had undergone transrectal ultrasound-guided prostate biopsies, were assessed. Analysis-modeling stage for several clinical indexes related to prostate cancer and renal function was carried out. Multiple logistic regression analyses were used to develop new risk assessment models of prostate cancer for both PSA level ranges 4-10 and 10-50 ng/mL. External validation stage of the new models was performed to assess the necessity of biopsy. Results The new models for both PSA ranges performed significantly better than PSA for detecting prostate cancers. Both models showed higher areas under the curves (0.937 and 0.873, respectively) compared with PSA alone (0.624 and 0.595), at pre-determined cut-off values of 0.1067 and 0.6183, respectively. Patients above the cut-off values were recommended for immediate biopsy, while the others were actively observed. External validation of the models showed significantly increased detection rates for prostate cancer (4-10 ng/mL group, 39.29% vs 17.79%, p=0.006; 10-50 ng/mL group, 71.83% vs 50.0%, p=0.015). Conclusions We developed risk assessment models for North Chinese patients with 4–50 ng/mL PSA to reduce unnecessary prostate biopsies and increase the detection rate of prostate cancer.
Collapse
Affiliation(s)
- Jing Zhao
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Shuai Liu
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Dexuan Gao
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Sentai Ding
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Zhihong Niu
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Hui Zhang
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University (East Branch), Jinan, People's Republic of China
| | - Zhilong Huang
- Department of Urology, Lanling People's Hospital, Lanling, People's Republic of China
| | - Juhui Qiu
- Department of Urology, Dongying People's Hospital, Dongying, People's Republic of China
| | - Qing Li
- Department of Urology, Yucheng People's Hospital, Yucheng, People's Republic of China
| | - Ning Li
- Department of Urology, Guangrao County Hospital of traditional Chinese Medicine, Guangrao, People's Republic of China
| | - Fang Xie
- Department of Urology, Weihai Municipal Hospital, Weihai, People's Republic of China
| | - Jilei Cui
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| | - Jiaju Lu
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, People's Republic of China
| |
Collapse
|
18
|
Morlacco A, Pan J, Karnes RJ. Risk-prediction tools in prostate cancer: the challenge of tailoring. Asian J Androl 2017; 18:952. [PMID: 27212124 PMCID: PMC5109897 DOI: 10.4103/1008-682x.179526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Affiliation(s)
| | - Jiahua Pan
- Department of Urology, Renji Hospital, Shanghai, China
| | | |
Collapse
|
19
|
Wu YS, Zhang N, Liu SH, Xu JF, Tong SJ, Cai YH, Zhang LM, Bai PD, Hu MB, Jiang HW, Na R, Ding Q, Sun YH. The Huashan risk calculators performed better in prediction of prostate cancer in Chinese population: a training study followed by a validation study. Asian J Androl 2017; 18:925-929. [PMID: 27212127 PMCID: PMC5109890 DOI: 10.4103/1008-682x.181192] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The performances of the Prostate Cancer Prevention Trial (PCPT) risk calculator and other risk calculators for prostate cancer (PCa) prediction in Chinese populations were poorly understood. We performed this study to build risk calculators (Huashan risk calculators) based on Chinese population and validated the performance of prostate-specific antigen (PSA), PCPT risk calculator, and Huashan risk calculators in a validation cohort. We built Huashan risk calculators based on data from 1059 men who underwent initial prostate biopsy from January 2006 to December 2010 in a training cohort. Then, we validated the performance of PSA, PCPT risk calculator, and Huashan risk calculators in an observational validation study from January 2011 to December 2014. All necessary clinical information were collected before the biopsy. The results showed that Huashan risk calculators 1 and 2 outperformed the PCPT risk calculator for predicting PCa in both entire training cohort and stratified population (with PSA from 2.0 ng ml−1 to 20.0 ng m). In the validation study, Huashan risk calculator 1 still outperformed the PCPT risk calculator in the entire validation cohort (0.849 vs 0.779 in area under the receiver operating characteristic curve [AUC] and stratified population. A considerable reduction of unnecessary biopsies (approximately 30%) was also observed when the Huashan risk calculators were used. Thus, we believe that the Huashan risk calculators (especially Huashan risk calculator 1) may have added value for predicting PCa in Chinese population. However, these results still needed further evaluation in larger populations.
Collapse
Affiliation(s)
- Yi-Shuo Wu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Ning Zhang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Sheng-Hua Liu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Jian-Feng Xu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China.,NorthShore University HealthSystem, Evanston, IL, USA
| | - Shi-Jun Tong
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Ye-Hua Cai
- Department of Ultrasonic, Huashan Hospital, Fudan University, Shanghai, China
| | - Li-Min Zhang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Pei-De Bai
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Meng-Bo Hu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Hao-Wen Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Rong Na
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Qiang Ding
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.,Urology Research Center, Fudan University, Shanghai, China
| | - Ying-Hao Sun
- Department of Urology, Changhai Hospital, The Second Military Medical University, Shanghai, China
| |
Collapse
|
20
|
Lee A, Lim J, Gao X, Liu L, Chia SJ. A nomogram for prediction of prostate cancer on multi-core biopsy using age, serum prostate-specific antigen, prostate volume and digital rectal examination in Singapore. Asia Pac J Clin Oncol 2016; 13:e348-e355. [PMID: 27641069 DOI: 10.1111/ajco.12596] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Revised: 07/10/2016] [Accepted: 07/28/2016] [Indexed: 01/01/2023]
Abstract
AIM To develop and internally validate two nomograms for predicting the probability of overall and clinically-significant prostate cancer on initial biopsy in a Singaporean population. METHODS Data were collected from men undergoing initial prostate biopsy at a single center. The indications for biopsy were serum prostate-specific antigen (PSA) ≥4.0 ng/mL or suspicious digital rectal examination (DRE) findings. Men with PSA >30 ng/mL were excluded. Age, PSA, prostate volume (PV) and DRE were predictors included in our logistic regression model and used to construct two nomograms for overall prostate cancer and clinically-significant (Gleason sum ≥7) cancer detection. Predictive accuracies of our nomograms were assessed using area under curve (AUC) of their receiver-operator characteristic curves. Internal validation was performed using the bootstrap method. Our nomograms were compared to a model based on PSA alone using AUC and decision curve analysis (DCA). RESULTS Out of 672 men analyzed, our positive biopsy rate was 26.2% (n = 176), of which 63.6% (n = 112) had clinically significant disease. Age, PSA, PV and DRE status were all independent risk factors for both overall prostate cancer detection as well as clinically-significant cancer detection (all P < 0.05). Our nomogram outperformed serum PSA for both overall and clinically-significant cancer detection (0.736 vs 0.642, P < 0.001 and 0.793 vs 0.696, P < 0.001, respectively). Using DCA, our nomograms had superior net benefit and net reduction in biopsy rate compared to PSA alone. CONCLUSIONS Our nomograms have been shown to be superior to PSA alone, on both AUC and DCA. However, it warrants external validation.
Collapse
Affiliation(s)
- Alvin Lee
- Department of Urology, Tan Tock Seng Hospital, Singapore
| | - Joel Lim
- Department of Urology, Tan Tock Seng Hospital, Singapore
| | - Xiao Gao
- Clinical Research and Innovation Office, Tan Tock Seng Hospital, Singapore
| | - Lizhen Liu
- Clinical Research and Innovation Office, Tan Tock Seng Hospital, Singapore
| | - Sing Joo Chia
- Department of Urology, Tan Tock Seng Hospital, Singapore
| |
Collapse
|
21
|
Chen R, Xie L, Xue W, Ye Z, Ma L, Gao X, Ren S, Wang F, Zhao L, Xu C, Sun Y. Development and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy. Urol Oncol 2016; 34:416.e1-7. [PMID: 27185342 DOI: 10.1016/j.urolonc.2016.04.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 03/21/2016] [Accepted: 04/05/2016] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men. MATERIALS AND METHODS Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals. RESULTS Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa. CONCLUSIONS CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds.
Collapse
Affiliation(s)
- Rui Chen
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Liping Xie
- Department of Urology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Wei Xue
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangqun Ye
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, Haidian District, Beijing, China
| | - Xu Gao
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Shancheng Ren
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Fubo Wang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Lin Zhao
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Chuanliang Xu
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Yinghao Sun
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China.
| |
Collapse
|
22
|
Lee A, Chia SJ. Overweight or not-Prostate-specific antigen levels reflect a continuum of risk influenced by other factors. Urol Oncol 2016; 34:117-8. [PMID: 26822078 DOI: 10.1016/j.urolonc.2015.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 09/19/2015] [Indexed: 10/22/2022]
Affiliation(s)
- Alvin Lee
- Department of Urology, Tan Tock Seng Hospital, Singapore
| | - Sing Joo Chia
- Department of Urology, Tan Tock Seng Hospital, Singapore
| |
Collapse
|
23
|
Sorokin I, Mian BM. Risk calculators and updated tools to select and plan a repeat biopsy for prostate cancer detection. Asian J Androl 2015; 17:864-9. [PMID: 26112489 PMCID: PMC4814963 DOI: 10.4103/1008-682x.156859] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Millions of men each year are faced with a clinical suspicion of prostate cancer (PCa) but the prostate biopsy fails to detect the disease. For the urologists, how to select the appropriate candidate for repeat biopsy is a significant clinical dilemma. Traditional risk-stratification tools in this setting such as prostate-specific antigen (PSA) related markers and histopathology findings have met with limited correlation with cancer diagnosis or with significant disease. Thus, an individualized approach using predictive models such as an online risk calculator (RC) or updated biomarkers is more suitable in counseling men about their risk of harboring clinically significant prostate cancer. This review will focus on the available risk-stratification tools in the population of men with prior negative biopsies and persistent suspicion of PCa. The underlying methodology and platforms of the available tools are reviewed to better understand the development and validation of these models. The index patient is then assessed with different RCs to determine the range of heterogeneity among various RCs. This should allow the urologists to better incorporate these various risk-stratification tools into their clinical practice and improve patient counseling.
Collapse
Affiliation(s)
| | - Badar M Mian
- Department of Urology, Albany Medical College, Albany, NY, USA
| |
Collapse
|
24
|
Strobl AN, Vickers AJ, Van Calster B, Steyerberg E, Leach RJ, Thompson IM, Ankerst DP. Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators. J Biomed Inform 2015; 56:87-93. [PMID: 25989018 DOI: 10.1016/j.jbi.2015.05.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 03/14/2015] [Accepted: 05/04/2015] [Indexed: 10/23/2022]
Abstract
Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.
Collapse
Affiliation(s)
- Andreas N Strobl
- TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany.
| | - Andrew J Vickers
- Memorial Sloan-Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York City, NY, USA
| | - Ben Van Calster
- KU Leuven, Department of Development and Regeneration, Leuven, Belgium
| | - Ewout Steyerberg
- Erasmus MC, Department of Public Health, Rotterdam, The Netherlands
| | - Robin J Leach
- University of Texas Health Science Center at San Antonio, Department of Cellular and Structural Biology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA
| | - Ian M Thompson
- University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA
| | - Donna P Ankerst
- TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Epidemiology and Biostatistics, San Antonio, TX, USA
| |
Collapse
|
25
|
Predicting prostate cancer: analysing the clinical efficacy of prostate cancer risk calculators in a referral population. Ir J Med Sci 2015; 184:701-6. [DOI: 10.1007/s11845-015-1291-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 03/29/2015] [Indexed: 10/23/2022]
|
26
|
Grill S, Fallah M, Leach RJ, Thompson IM, Hemminki K, Ankerst DP. A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future validation. J Clin Epidemiol 2015; 68:563-73. [PMID: 25684153 DOI: 10.1016/j.jclinepi.2015.01.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Revised: 01/07/2015] [Accepted: 01/09/2015] [Indexed: 01/23/2023]
Abstract
OBJECTIVES To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC). STUDY DESIGN AND SETTING A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds. RESULTS In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively). CONCLUSION The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.
Collapse
Affiliation(s)
- Sonja Grill
- Department of Life Sciences of the Technical University Munich, Liesel-Beckmann-Str. 2, 85354 Freising, Germany.
| | - Mahdi Fallah
- Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Im Neuenheimer Feld 580, Im Technologiepark, 69120 Heidelberg, Germany
| | - Robin J Leach
- Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA; Department of Cellular and Structural Biology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Ian M Thompson
- Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Im Neuenheimer Feld 580, Im Technologiepark, 69120 Heidelberg, Germany; Center for Primary Health Care Research, Lund University, Box 117, 221 00 LUND, Sweden
| | - Donna P Ankerst
- Department of Life Sciences of the Technical University Munich, Liesel-Beckmann-Str. 2, 85354 Freising, Germany; Department of Urology of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA; Department of Mathematics of the Technical University Munich, Boltzmannstr. 3, 85748 Garching b. München, Germany; Department of Epidemiology and Biostatistics of the University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| |
Collapse
|
27
|
Zhao R, Huang Y, Cheng G, Liu J, Shao P, Qin C, Hua L, Yin C. Developing a follow-up strategy for patients with PSA ranging from 4 to 10 ng/ml via a new model to reduce unnecessary prostate biopsies. PLoS One 2014; 9:e106933. [PMID: 25268808 PMCID: PMC4182133 DOI: 10.1371/journal.pone.0106933] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 08/05/2014] [Indexed: 11/18/2022] Open
Abstract
Objective The aim of this study was to develop a follow-up strategy based on the new model to reduce unnecessary prostate biopsies in patients with prostate specific antigen (PSA) ranging from 4 to 10 ng/ml. Methods A total of 436 patients with PSA ranging from 4 to 10 ng/ml who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were evaluated during the first stage. Age, PSA, free PSA (fPSA), digital rectal examination (DRE) findings, ultrasonic hypoechoic mass, ultrasonic microcalcifications, prostate volume (PV) and PSA density (PSAD) were considered as predictive factors. A multiple logistic regression analysis involving a backward elimination selection procedure was applied to select independent predictors. After a comprehensive analysis of all results, we developed a new model to assess the risk of prostate cancer and an effective follow-up strategy. Results Age, PSA, PV, fPSA, rate of abnormal DRE findings and rate of hypoechoic masses detected by TRUS were included in our model. A significantly greater area under the receiver-operating characteristic curve was obtained in our model when compared with using PSA alone (0.782 vs. 0.566). Patients were grouped according to the value of prostate cancer risk (PCaR). In the second stage of our study, patients with PCaR>0.52 were recommended to undergo biopsies immediately while the rest of the patients continued close follow-up observation. Compared with the first stage, the detection rate of PCa in the second stage was significantly increased (33.0% vs 21.1%, p = 0.012). There was no significant difference between the two stages in distribution of the Gleason score (p = 0.808). Conclusions We developed a follow-up strategy based on the new model, which reduced unnecessary prostate biopsies without delaying patients’ diagnoses and treatments.
Collapse
Affiliation(s)
- Ruizhe Zhao
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yuan Huang
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Gong Cheng
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinliang Liu
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Pengfei Shao
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Qin
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lixin Hua
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- * E-mail:
| | - Changjun Yin
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
28
|
Grill S, Fallah M, Leach RJ, Thompson IM, Freedland S, Hemminki K, Ankerst DP. Incorporation of detailed family history from the Swedish Family Cancer Database into the PCPT risk calculator. J Urol 2014; 193:460-5. [PMID: 25242395 DOI: 10.1016/j.juro.2014.09.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2014] [Indexed: 11/17/2022]
Abstract
PURPOSE A detailed family history provides an inexpensive alternative to genetic profiling for individual risk assessment. We updated the PCPT Risk Calculator to include detailed family histories. MATERIALS AND METHODS The study included 55,168 prostate cancer cases and 638,218 controls from the Swedish Family Cancer Database who were 55 years old or older in 1999 and had at least 1 male first-degree relative 40 years old or older and 1 female first-degree relative 30 years old or older. Likelihood ratios, calculated as the ratio of risk of observing a specific family history pattern in a prostate cancer case compared to a control, were used to update the PCPT Risk Calculator. RESULTS Having at least 1 relative with prostate cancer increased the risk of prostate cancer. The likelihood ratio was 1.63 for 1 first-degree relative 60 years old or older at diagnosis (10.1% of cancer cases vs 6.2% of controls), 2.47 if the relative was younger than 60 years (1.5% vs 0.6%), 3.46 for 2 or more relatives 60 years old or older (1.2% vs 0.3%) and 5.68 for 2 or more relatives younger than 60 years (0.05% vs 0.009%). Among men with no diagnosed first-degree relatives the likelihood ratio was 1.09 for 1 or more second-degree relatives diagnosed with prostate cancer (12.7% vs 11.7%). Additional first-degree relatives with breast cancer, or first-degree or second-degree relatives with prostate cancer compounded these risks. CONCLUSIONS A detailed family history is an independent predictor of prostate cancer compared to commonly used risk factors. It should be incorporated into decision making for biopsy. Compared with other costly biomarkers it is inexpensive and universally available.
Collapse
Affiliation(s)
- Sonja Grill
- Departments of Life Sciences and Mathematics, Technical University Munich, Munich, Germany
| | - Mahdi Fallah
- Section of Surgery, Durham Veterans Affairs Hospital and Department of Surgery (Urology) and Pathology, Duke University, Durham, North Carolina
| | - Robin J Leach
- Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas; Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Ian M Thompson
- Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Stephen Freedland
- Section of Surgery, Durham Veterans Affairs Hospital and Department of Surgery (Urology) and Pathology, Duke University, Durham, North Carolina
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Centre, Heidelberg, Germany; Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Donna P Ankerst
- Departments of Life Sciences and Mathematics, Technical University Munich, Munich, Germany; Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas; Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
| |
Collapse
|
29
|
Prostate cancer risk assessment tools in an unscreened population. World J Urol 2014; 33:827-32. [PMID: 25091862 DOI: 10.1007/s00345-014-1365-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 07/08/2014] [Indexed: 10/24/2022] Open
Abstract
OBJECTIVE To compare the prostate cancer prevention trial risk calculator (PCPT-RC) and European randomized study of screening for prostate cancer risk calculator (ERSPC-RC) in a unique unscreened population from the West of Ireland. PATIENTS AND METHODS Data was prospectively recorded for all 556 consecutive men who underwent prostate biopsy at our institution as part of the Rapid Access Prostate Assessment Clinic program in Ireland. The estimated probabilities of detecting prostate cancer and high-grade disease were calculated using the PCPT and ERSPC risk calculators. For each calculator the discriminative ability, calibration and clinical utility was assessed. RESULTS Prostate cancer was detected in 49% and high-grade prostate cancer in 34% of men. Receiver operating characteristic curve analysis demonstrated that the PCPT-RCs outperformed the ERSPC-RCs for the prediction of prostate cancer areas underneath the ROC curve (AUC 0.628 vs. 0.588, p = 0.0034) and for the prediction of high-grade prostate cancer (AUC 0.792 vs. 0.690, p = 0.0029). Both risk calculators generally over-predicted the risk of prostate cancer and high-grade disease across a wide range of predicted probabilities. Decision curve analysis suggested greater net benefit using the PCPT-RCs in this population. CONCLUSIONS Multivariable nomograms can further aid patient counselling for early prostate cancer detection. In unscreened men from Western Ireland, the PCPT-RCs provided better discrimination for overall prostate cancer and high-grade disease compared to the ERSPC-RC. However, both tools overpredicted the risk of cancer detection on biopsy, and it is possible that a different set of predictive variables may be more useful in this population.
Collapse
|
30
|
Huang Y, Cheng G, Liu B, Shao P, Qin C, Li J, Hua L, Yin C. A prostate biopsy strategy based on a new clinical nomogram reduces the number of biopsy cores required in high-risk patients. BMC Urol 2014; 14:8. [PMID: 24410803 PMCID: PMC3893548 DOI: 10.1186/1471-2490-14-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Accepted: 01/09/2014] [Indexed: 12/23/2022] Open
Abstract
Background The nomograms used for prostate cancer risk assessment in Western countries are not directly applicable to Chinese males; consequently, we have developed a new model to evaluate the risk of them developing this disease. Methods A total of 1104 patients who had undergone trans-rectal ultrasound (TRUS)-guided 12 + 1-core prostate biopsy were retrospectively evaluated in the first stage of the study. Age, prostate-specific antigen (PSA), the free/total PSA ratio (f/t), digital rectal examination (DRE) findings, the presence of a hypoechoic mass revealed using ultrasound, ultrasonic detection of microcalcifications, prostate volume (PV) and PSA density were considered as predictive factors. Multiple logistic regression analysis involving a backward elimination selection procedure was used to select independent predictors. We compared positive rates regarding 6-core and 12-core biopsy schemes at different risk levels. In the second stage of the study, 238 cases were evaluated using our nomogram. In higher risk patients, we employed a 6 + 1 core biopsy. Positive rates in the first and second stages of the study were compared. Results Age, the baseline median natural logarithm of PSA (Ln[PSA]), Ln(PV), f/t, rate of abnormal DRE findings and rate of hypoechoic masses detected using TRUS were the factors that were finally submitted into our nomogram. A significantly greater area under the receiver-operating characteristic curve was obtained for the nomogram than for PSA level alone (0.853 vs. 0.761). A cancer probability cutoff value of 0.5 suggested no significant difference between the 6-core and 12-core biopsy schemes at higher risk levels. In the second stage of the study we verified that in patients with a cancer probability cutoff value >0.5, a 6 + 1-core biopsy could be used without a reduction in the positive detection rate, and significantly reducing the number of biopsy cores required. Conclusions A nomogram based on data from Chinese males was developed to predict the positive detection rate, ratio of positive cores and Gleason score at each risk level. According to this nomogram, a reasonable biopsy strategy could be constituted to reduce the number of biopsy cores required in subjects at high risk.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Lixin Hua
- State Key Laboratory of Reproductive Medicine, Department of Urology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Rd, Nanjing 210029, China.
| | | |
Collapse
|
31
|
A nomogram based on age, prostate-specific antigen level, prostate volume and digital rectal examination for predicting risk of prostate cancer. Asian J Androl 2012; 15:129-33. [PMID: 23291910 DOI: 10.1038/aja.2012.111] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE) status, % free PSA and transrectal ultrasound (TRUS) findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% (223/535). The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.
Collapse
|