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Graham NJ, Souter LH, Salami SS. A Systematic Review of Family History, Race/Ethnicity, and Genetic Risk on Prostate Cancer Detection and Outcomes: Considerations in PSA-based Screening. Urol Oncol 2024:S1078-1439(24)00504-0. [PMID: 39013715 DOI: 10.1016/j.urolonc.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/25/2024] [Accepted: 06/02/2024] [Indexed: 07/18/2024]
Abstract
AIM To investigate the role of family history, race/ethnicity, and genetics in prostate cancer (PCa) screening. METHODS We conducted a systematic review of articles from January 2013 through September 2023 that focused on the association of race/ethnicity and genetic factors on PCa detection. Of 10,815 studies, we identified 43 that fulfilled our pre-determined PICO (Patient, Intervention, Comparison and Outcome) criteria. RESULTS Men with ≥1 first-degree relative(s) with PCa are at increased risk of PCa, even with negative imaging and/or benign prostate biopsy. Black men have higher PCa risk, while Asian men have lower risk. Most of the differences in risks are attributable to environmental and socioeconomic factors; however, genetic differences may play a role. Among numerous pathogenic variants that increase PCa risk, BRCA2, MSH2, and HOXB13 mutations confer the highest risk of PCa. Polygenic risk score (PRS) models identify men at higher PCa risk for a given age and PSA; these models improve when considering other clinical factors and when the model population matches the study population's ancestry. CONCLUSIONS Family history of PCa, race/ethnicity, pathogenic variants (particularly BRCA2, MSH2, and HOXB13), and PRS are associated with increased PCa risk and should be considered in shared decision-making to determine PCa screening regimens.
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Affiliation(s)
| | | | - Simpa S Salami
- Department of Urology, University of Michigan, Ann Arbor, MI.
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Haj-Mirzaian A, Burk KS, Lacson R, Glazer DI, Saini S, Kibel AS, Khorasani R. Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis. JAMA Netw Open 2024; 7:e244258. [PMID: 38551559 PMCID: PMC10980971 DOI: 10.1001/jamanetworkopen.2024.4258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/02/2024] [Indexed: 04/01/2024] Open
Abstract
Importance Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion. Objective To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data. Data Sources PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022. Study Selection English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data. Data Extraction and Synthesis Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Main Outcomes and Measures Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis. Results The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15). Conclusions and Relevance These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.
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Affiliation(s)
- Arya Haj-Mirzaian
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kristine S. Burk
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel I. Glazer
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Sanjay Saini
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Adam S. Kibel
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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Neumair M, Kattan MW, Freedland SJ, Haese A, Guerrios-Rivera L, De Hoedt AM, Liss MA, Leach RJ, Boorjian SA, Cooperberg MR, Poyet C, Saba K, Herkommer K, Meissner VH, Vickers AJ, Ankerst DP. Accommodating heterogeneous missing data patterns for prostate cancer risk prediction. BMC Med Res Methodol 2022; 22:200. [PMID: 35864460 PMCID: PMC9306143 DOI: 10.1186/s12874-022-01674-x] [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: 12/03/2021] [Accepted: 07/04/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.
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Affiliation(s)
- Matthias Neumair
- grid.6936.a0000000123222966Department of Life Sciences, Technical University of Munich, Freising, Germany
| | - Michael W. Kattan
- grid.239578.20000 0001 0675 4725Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH USA
| | - Stephen J. Freedland
- Section of Urology, Durham Veterans Administration Health Care System, Durham, NC USA ,grid.50956.3f0000 0001 2152 9905Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Alexander Haese
- grid.13648.380000 0001 2180 3484Martini-Clinic Prostate Cancer Center, University Clinic Eppendorf, Hamburg, Germany
| | - Lourdes Guerrios-Rivera
- grid.509403.b0000 0004 0420 4000Department of Surgery, Urology Section, Veterans Affairs Caribbean Healthcare System, San Juan, Puerto Rico
| | - Amanda M. De Hoedt
- Section of Urology, Durham Veterans Administration Health Care System, Durham, NC USA
| | - Michael A. Liss
- grid.267309.90000 0001 0629 5880Department of Urology, University of Texas Health at San Antonio, San Antonio, TX USA
| | - Robin J. Leach
- grid.267309.90000 0001 0629 5880Department of Cell Systems and Anatomy, University of Texas Health at San Antonio, San Antonio, TX USA
| | - Stephen A. Boorjian
- grid.66875.3a0000 0004 0459 167XDepartment of Urology, Mayo Clinic, Rochester, MN USA
| | - Matthew R. Cooperberg
- grid.266102.10000 0001 2297 6811Departments of Urology and Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA USA
| | - Cedric Poyet
- grid.7400.30000 0004 1937 0650Department of Urology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Karim Saba
- grid.7400.30000 0004 1937 0650Department of Urology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland ,grid.483344.c0000000406274213Urology Centre, Hirslanden Klinik Aarau, Aarau, Switzerland
| | - Kathleen Herkommer
- Department of Urology, University Hospital, Technical University of Munich, Munich, Germany
| | - Valentin H. Meissner
- Department of Urology, University Hospital, Technical University of Munich, Munich, Germany
| | - Andrew J. Vickers
- grid.51462.340000 0001 2171 9952Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Donna P. Ankerst
- grid.6936.a0000000123222966Department of Life Sciences, Technical University of Munich, Freising, Germany ,grid.6936.a0000000123222966Department of Mathematics, Technical University of Munich, Boltzmannstrasse 3, Garching, Germany
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