Prostate cancer risk prediction based on clinical factors and prostate-specific antigen

BMC Urology

Research

Eo Jin Kim¹  Jung Ah Lee²  Hyungseok Oh²  Taewon Hwang³ 

[1] Division of Hematology/Oncology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, 03181, Seoul, South Korea;Workplace Health Institute, Total Health Care Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, B1, 55 Sejong-daero, Jung-gu, 06521, Seoul, South Korea;Workplace Health Institute, Total Health Care Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, B1, 55 Sejong-daero, Jung-gu, 06521, Seoul, South Korea;Department of Economics, Texas A&M University, 4228 TAMU, 77843, College Station, TX, USA;

关键词: Prostate cancer;    Prostate-specific antigen;    Prediction model;    Lifestyle risk factor;    Clinical factor;   

DOI  :  10.1186/s12894-023-01259-w

 received in 2022-10-06, accepted in 2023-04-24,  发布年份 2023

来源: Springer

【 摘 要 】

IntroductionThe incidence rate of prostate cancer (PCa) has continued to rise in Korea. This study aimed to construct and evaluate a 5-year PCa risk prediction model using a cohort with PSA < 10 ng/mL by incorporating PSA levels and individual factors.MethodsThe PCa risk prediction model including PSA levels and individual risk factors was constructed using a cohort of 69,319 participants from the Kangbuk Samsung Health Study. 201 registered PCa incidences were observed. A Cox proportional hazards regression model was used to generate the 5-year risk of PCa. The performance of the model was assessed using standards of discrimination and calibration.ResultsThe risk prediction model included age, smoking status, alcohol consumption, family history of PCa, past medical history of dyslipidemia, cholesterol levels, and PSA level. Especially, an elevated PSA level was a significant risk factor of PCa (hazard ratio [HR]: 1.77, 95% confidence interval [CI]: [1.67–1.88]). This model performed well with sufficient discrimination ability and satisfactory calibration (C-statistic: 0.911, 0.874; Nam-D’Agostino test statistic:19.76, 4.21 in the development and validation cohort, respectively).ConclusionsOur risk prediction model was effective in predicting PCa in a population according to PSA levels. When PSA levels are inconclusive, an assessment of both PSA and specific individual risk factors (e.g., age, total cholesterol, and family history of PCa) could provide further information in predicting PCa.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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