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Chung JH, Kim JH, Lee SW, Park H, Song G, Song W, Kang M, Sung HH, Jeon HG, Jeong BC, Seo SI, Lee HM, Jeon SS. Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study. World J Mens Health 2024; 42:168-177. [PMID: 37118959 PMCID: PMC10782127 DOI: 10.5534/wjmh.220223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 04/30/2023] Open
Abstract
PURPOSE To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed. MATERIALS AND METHODS A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed. RESULTS Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively. CONCLUSIONS Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.
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Affiliation(s)
- Jae Hoon Chung
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Hyun Kim
- Department of Urology, Kangwon National University School of Medicine, Chuncheon, Korea.
| | - Sang Wook Lee
- Department of Urology, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Hongzoo Park
- Department of Urology, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Geehyun Song
- Department of Urology, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Wan Song
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Minyong Kang
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyun Hwan Sung
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hwang Gyun Jeon
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Byong Chang Jeong
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seong Il Seo
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyun Moo Lee
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seong Soo Jeon
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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2
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Arafa MA, Farhat KH, Khan FK, Rabah DM, Elmorshedy H, Mokhtar A, Al-Taweel W. Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study. Prostate 2024; 84:56-63. [PMID: 37759243 DOI: 10.1002/pros.24625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Accurately identifying aggressive prostate tumors and studying them as a separate outcome are urgently needed. Nomogram is a predictive tool using an algorithm, it has been widely applied in clinical practice to predict prognosis. We aimed to develop and internally validate a nomogram predicting clinically significant prostate cancer (csPCa). METHODS Data were retrieved from the records of the two main hospitals in Riyadh, during the period 2019-2022. Significant variables associated with csPCa cases were used to develop and internally validate a novel nomogram, utilizing the C index, and calibration curves. Decision curve analysis (DCA) was used to assess its clinical utility. RESULTS Prostate imaging reporting and data system (PI-RADS), smaller prostate volume, and prostate-specific antigen (PSA) > 10 ng/mL were significantly associated with the risk csPCa, respectively. The model developed by the nomogram showed an excellent accuracy for csPCa discrimination, as indicated by area under the curve (0.83), and calibration curves. DCA showed that our model was superior and surpassed all other models with a larger net benefit for various threshold probabilities. Based on our model, at a probability threshold of 30%, biopsying patients is the equivalent of a strategy that led to an absolute 5% reduction in the number of biopsies without missing any csPCa. CONCLUSION The developed nomogram consisting of PI-RAD, total PSA, and prostate volume showed a robust predictive capacity for csPCa before prostate biopsy that may be valuable for clinical judgment to prevent needless biopsy. Yet, the small percentage (5%) of yielded unnecessary biopsies that could be saved by using such a model, cast an important question on its merit and clinical applicability.
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Affiliation(s)
- Mostafa A Arafa
- The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Epidemiology Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt
| | - Karim H Farhat
- The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Farrukh K Khan
- Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Danny M Rabah
- The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Urology Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Hala Elmorshedy
- Epidemiology Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt
| | - Alaa Mokhtar
- Urology Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Waleed Al-Taweel
- Urology Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
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3
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Nan L, Guo K, Li M, Wu Q, Huo S. Development and validation of a multi-parameter nomogram for predicting prostate cancer: a retrospective analysis from Handan Central Hospital in China. PeerJ 2022; 10:e12912. [PMID: 35256916 PMCID: PMC8898009 DOI: 10.7717/peerj.12912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/19/2022] [Indexed: 01/14/2023] Open
Abstract
Background To explore the possible predicting factors related to prostate cancer and develop a validated nomogram for predicting the probability of patients with prostate cancer. Method Clinical data of 697 patients who underwent prostate biopsy in Handan Central Hospital from January 2014 to January 2020 were retrospectively collected. Cases were randomized into two groups: 80% (548 cases) as the development group, and 20% (149 cases) as the validation group. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for prostate cancer. The nomogram prediction model was generated using the finalized independent risk factors. Decision curve analysis (DCA) and the area under receiver operating characteristics curve (ROC) of both development group and validation group were calculated and compared to validate the accuracy and efficiency of the nomogram prediction model. Clinical utility curve (CUC) helped to decide the desired cut-off value for the prediction model. The established nomogram with Prostate Cancer Prevention Trial Derived Cancer Risk Calculator (PCPT-CRC) and other domestic prediction models using the entire study population were compared. Results The independent risk factors determined through univariate and multivariate logistic regression analyses were: age, tPSA, fPSA, PV, DRE, TRUS and BMI. Nomogram prediction model was developed with the cut-off value of 0.31. The AUC of development group and validation group were 0.856 and 0.797 respectively. DCA exhibits consistent observations with the findings. Through validating our prediction model as well as other three domestic prediction models based on the entire study population of 697 cases, our prediction model demonstrated significantly higher predictive value than all the other models. Conclusion The nomogram for predicting prostate cancer can facilitate more accurate evaluation of the probability of having prostate cancer, and provide better ground for prostate biopsy.
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Affiliation(s)
- Libin Nan
- Department of Urology, Handan Central Hospital, Handan, Hebei, China
| | - Kai Guo
- Cardiac Department, Turku City Hospital, Turku, Varsinais-suomi, Finland
| | - Mingmin Li
- Out-patient Department, Handan Central Hospital, Handan, Hebei, China
| | - Qi Wu
- Department of Urology, Handan Central Hospital, Handan, Hebei, China
| | - Shaojun Huo
- Department of Urology, Handan Central Hospital, Handan, Hebei, China
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4
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Zhang Z, Zhou Q, Ang X, Hu C, Ouyang J, Zhang J. The research of the application of a new urinary biomarker PCA-M of prostate cancer (PSA from 4 to 20 ng/ml). ALL LIFE 2022. [DOI: 10.1080/26895293.2022.2038689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Affiliation(s)
- Zhiyu Zhang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
| | - Qi Zhou
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
| | - Xiaojie Ang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
| | - Can Hu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
| | - Jun Ouyang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
| | - Jianglei Zhang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China
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Wu Q, Li F, Yin X, Gao J, Zhang X. Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy. Medicine (Baltimore) 2021; 100:e28196. [PMID: 34918677 PMCID: PMC8677903 DOI: 10.1097/md.0000000000028196] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 11/17/2021] [Indexed: 01/05/2023] Open
Abstract
The aim of this study was to construct a nomogram for predicting prostate cancer (PCa) in patients with PSA ≤ 20 ng/mL at initial biopsy.The patients with PSA ≤ 20 ng/mL who underwent prostate biopsy were retrospectively included in this study. The nomogram was developed based on predictors for PCa, which were assessed by multivariable logistic regression analysis. The receiver operating characteristic curve, calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.This retrospective study included 691 patients, who were divided into training set (505 patients) and validation set (186 patients). The nomogram was developed based on the multivariable logistic regression model, including age, total PSA, free PSA, and prostate volume. It had a high area under the curve of 0.857, and was well verified in validation set. Calibration plots and DCA further validated its discrimination and potential clinical benefits. Applying the cut-off value of 15%, our nomogram would avoid 42.5% of unnecessary biopsies while miss only 4.4% of PCa patients.The nomogram provided high predictive accuracy for PCa in patients with PSA ≤ 20 ng/mL at initial biopsy, which could be used to avoid the unnecessary biopsies in clinical practice.
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Affiliation(s)
- Qiang Wu
- Department of Graduate Administration, Chinese PLA General Hospital, Beijing, China
- Department of Urology, Huhhot First Hospital, Huhhot, China
| | - Fanglong Li
- Department of Urology, Chinese PLA 980th Hospital, Shijiazhuang, China
| | - Xiaotao Yin
- Senior Department of Urology, the Third Medical Center of PLA General Hospital, Beijing, China
| | - Jiangping Gao
- Senior Department of Urology, the Third Medical Center of PLA General Hospital, Beijing, China
| | - Xu Zhang
- Senior Department of Urology, the Third Medical Center of PLA General Hospital, Beijing, China
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6
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Choi J, Kang M, Sung HH, Jeon HG, Jeong BC, Seo SI, Jeon SS, Lee HM. Correlation between Gleason score distribution and Prostate Health Index in patients with prostate-specific antigen values of 2.5-10 ng/mL. Investig Clin Urol 2021; 61:582-587. [PMID: 33135403 PMCID: PMC7606122 DOI: 10.4111/icu.20200084] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/18/2020] [Accepted: 06/23/2020] [Indexed: 11/26/2022] Open
Abstract
Purpose To determine the clinical significance and correlation between the Prostate Health Index (PHI) and Gleason score in patients with a prostate-specific antigen (PSA) value of 2.5–10 ng/mL. Materials and Methods This retrospective analysis included 114 patients who underwent biopsy after completion of the PHI from November 2018 to July 2019. Various parameters such as PSA, PHI, PSA density, free PSA, p2PSA, and %free PSA were collected, and correlations with biopsy Gleason score and cancer detection rates were investigated. Results Baseline characteristics were comparable between PHI groups (0–26.9 [n=11], 27.0–35.9 [n=17], 36.0–54.9 [n=50], and ≥55.0 [n=36]). A total of 37 patients (32.5%) were diagnosed with prostate cancer, and 28 (24.6%) were diagnosed with clinically significant prostate cancer (CSPC, Gleason score ≥7) after prostate biopsy. The cancer detection rate gradually increased with a corresponding increase in the PHI (18%, 24%, 30%, and 44%, respectively). The same pattern was observed with detecting CSPC (0%, 18%, 26%, and 33%, respectively). There was no CSPC in the groups with PHI <27.0, and Gleason score 7 began to appear in groups with PHI ≥27.0. In particular, patients with Gleason score 8 and 9 were distributed only in the groups with PHI ≥36.0. Conclusions The diagnostic accuracy of detection of CSPC could be increased when prostate biopsy is performed in patients with a PHI ≥36.0. In this study, there was a clear Gleason score difference when the PHI cutoff value was set to 27.0 or 36.0.
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Affiliation(s)
- Joongwon Choi
- Department of Urology, VHS Medical Center, Seoul, Korea
| | - Minyong Kang
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyun Hwan Sung
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hwang Gyun Jeon
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Byong Chang Jeong
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seong Il Seo
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seong Soo Jeon
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyun Moo Lee
- Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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7
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A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer. Sci Rep 2020; 10:17675. [PMID: 33077762 PMCID: PMC7572499 DOI: 10.1038/s41598-020-74703-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 10/05/2020] [Indexed: 12/24/2022] Open
Abstract
The roles played by several inflammatory factors in screening for prostate cancer (PCa) among gray area patients, namely those with serum prostate-specific antigen (PSA) levels between 4 and 10 ng/ml, have not been completely identified, and few effective diagnostic nomograms have been developed exclusively for these patients. We aimed to investigate new independent predictors of positive biopsy (PB) results and develop a novel diagnostic nomogram for this group of patients. The independent predictors of PB results were identified, and a nomogram was constructed using multivariate logistic regression analysis based on a cohort comprising 401 Gy area patients diagnosed at Xijing Hospital (Xi’an, China) between January 2016 and December 2019. The predictive accuracy of the nomogram was assessed using the receiver operating characteristic curve, and the nomogram was calibrated by comparing the prediction with the observation. The performance of the nomogram was further validated using an independent cohort. Finally, lymphocyte-to-monocyte ratio (LMR) > 4.11 and red blood cell distribution width (RDW)-standard deviation (SD) > 42.9 fl were identified as independent protective predictors of PB results, whereas PSA density (PSAD) > 0.141 was identified as an independent risk predictor. The nomogram established using PSAD, LMR, and RDW-SD was perfectly calibrated, and its predictive accuracy was superior to that of PSAD in both internal and external validations (0.827 vs 0.769 and 0.765 vs 0.713, respectively). This study is the first to report the importance of LMR and RDW-SD in screening for PCa among gray area patients and to construct an exclusive nomogram to predict the individual risk of positive 13-core biopsy results in this group of patients. With superior performance over PSAD, our nomogram will help increase the accuracy of PCa screening, thereby avoiding unnecessary biopsy.
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8
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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: 0.8] [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.
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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
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Remmers S, Roobol MJ. Personalized strategies in population screening for prostate cancer. Int J Cancer 2020; 147:2977-2987. [PMID: 32394421 PMCID: PMC7586980 DOI: 10.1002/ijc.33045] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 12/29/2022]
Abstract
This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.
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Affiliation(s)
- Sebastiaan Remmers
- Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, Rotterdam, The Netherlands
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10
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Adaptation of the prostate biopsy collaborative group risk calculator in patients with PSA less than 10 ng/ml improves its performance. Int Urol Nephrol 2020; 52:1811-1819. [PMID: 32468165 DOI: 10.1007/s11255-020-02517-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 05/22/2020] [Indexed: 10/24/2022]
Abstract
PURPOSES The prostate biopsy collaborative group risk calculator (PBCGRC) is a newly developed risk estimator for predicting prostate biopsy outcomes. However, its clinical usefulness is still unknown within the so-called gray area of PSA values. This study aimed to determine whether updating the PBCGRC improves its predictive performance for predicting any-grade and high-grade (HG), defined as biopsy Gleason score ≥ 7, prostate cancer (PCa) in patients with prostate-specific antigen (PSA) less than 10 ng/ml. METHODS The risk of any-grade and HGPCa was calculated using the PBCG risk calculation formulas updated by recalibration in the large, logistic recalibration and model revision. Predictive performances of the PBCGRC and the updated models were compared using discrimination, calibration, and clinical utility. RESULTS Within the study sample of 526 patients, PCa was detected in 193 (36.7%), and 78 (14.8%) of them had HGPCa. According to the calibration curves, the PBCGRC overestimated the risk of PCa. Predictive accuracy of the revised model was higher [the area under the receiver-operating characteristic curve (AUCs), 65.4% and 70.2%] than that of the PBCGRC (AUCs, 60.4% and 64.3%) for any-grade and HGPCa. The net benefit was greater for model revision in comparison with the original model. CONCLUSION The performance accuracy of PBCGRC for the prediction of any and HGPC in men undergoing prostate biopsy with PSA levels below 10 ng/ml is suboptimal. The model revision resulted with significant improvement in model performance. However, external validation of the revised model is necessary before its routine use in clinical practice.
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11
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Yu W, Zhou L. Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians. J Cancer 2020; 11:3264-3273. [PMID: 32231732 PMCID: PMC7097943 DOI: 10.7150/jca.36697] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 01/06/2020] [Indexed: 12/28/2022] Open
Abstract
Prostate cancer (PCa) is the seventh most diagnosed cancer and the tenth leading cause of cancer mortality in China. Unlike the USA, both incidence and mortality continue to increase. In China, PCa is often diagnosed at a locally advanced or metastatic stage, resulting in a high mortality-to-incidence ratio. Implementing regular screening using a well-validated biomarker may result in the earlier diagnosis of localized disease. Furthermore, it is important to be able to distinguish between low-grade and high-grade disease, to avoid subjecting patients to unnecessary biopsies, undertreatment of significant disease, or overtreatment of indolent disease. While prostate-specific antigen (PSA) is commonly used in PCa screening around the world, its relationship to PCa is still unclear and results vary widely across different studies. New biomarkers, imaging techniques and risk predictive models have been developed in recent years to improve upon the accurate detection of high-grade PCa. Blood- and urine-based biomarkers, such as PSA isoforms, prostate cancer antigen 3, or mRNA transcripts, have been used to improve the detection of high-grade PCa. These markers have also been used to create risk predictive models, which can further improve PCa detection. Furthermore, multiparametric magnetic resonance imaging is becoming increasingly accessible for the detection of PCa. Because of ethnic variations, biomarkers and risk predictive models validated in Western populations cannot be directly applied to Chinese men. Validation of new biomarkers and risk predictive models in the Chinese population may improve PCa screening and reduce mortality of this disease in China.
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Affiliation(s)
- Wei Yu
- Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing, China
| | - Liqun Zhou
- Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center of China, Beijing, China
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12
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Chen S, Yang Y, Peng T, Yu X, Deng H, Guo Z. The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study. Int J Med Sci 2020; 17:1366-1374. [PMID: 32624693 PMCID: PMC7330665 DOI: 10.7150/ijms.45730] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/23/2020] [Indexed: 01/07/2023] Open
Abstract
Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. 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 points. 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 and the nomogram prediction model may help early diagnose the high risk prostate cancer.
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Affiliation(s)
- Song Chen
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yun Yang
- Department of Dermatology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Tianchen Peng
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Xi Yu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Haiqing Deng
- Department of Urology, Xiangyang Central Hospital, Xiangyang, 441021, China
| | - Zhongqiang Guo
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
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Su R, Xu G, Xiang L, Ding S, Wu R. A Novel Scoring System for Prediction of Prostate Cancer Based on Shear Wave Elastography and Clinical Parameters. Urology 2018; 121:112-117. [PMID: 30171925 DOI: 10.1016/j.urology.2018.08.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 07/31/2018] [Accepted: 08/21/2018] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To develop a novel scoring system for the prediction of prostate cancer (PCa). METHODS We assessed 127 patients who underwent a prostate biopsy. Prior to biopsy, we performed shear wave elastography (SWE), transrectal ultrasound, digital rectal exam, total prostatic specific antigen, PSA density (PSAD), and free PSA/total PSA ratio (F/T). We developed an 11-point scoring system based on SWE and these clinical parameters. RESULTS PCa was diagnosed in 51 (40.2%) of 127 patients and 192 (25.2%) of 762 sextants on initial biopsy. ROC curve analyses showed that the cutoff value (COV) for SWE was 40.8 kpa at the sextant level. The AUC of score system based on the SWE and clinical parameters (0.911) was significantly different from scoring systems based on SWE alone (0.842) or clinical parameters alone (0.868). For this 11-point scoring system, the optimal COV, Youden index, sensitivity, specificity, PPV, NPV, and AUC were 3 points, 0.66, 76.5% 89.5%, 82.98%, 85.00%, and 0.911, respectively. There were 68 negative biopsy results in patients with 0-3 points, and the detection rate of PCa was 100% in patients with scores exceeding 6 points. CONCLUSION This 11-point scoring system based on SWE and clinical parameters has the good diagnostic performance for predicting PCa. It may be useful in selecting patients for biopsy, substantially reducing the number of unnecessary biopsies while ensuring that few cancers are missed.
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Affiliation(s)
- Rui Su
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Department of Urology, Ningbo First Hospital, the Affiliated Hospital of Ningbo University, Ningbo, China
| | - Guang Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Lihua Xiang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Shisi Ding
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China; Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China.
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