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

• Published in: BMC urology Vol. 23; no. 1; p. 100
• Main Authors: Hwang, Taewon, Oh, Hyungseok, Lee, Jung Ah, Kim, Eo Jin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.06.2023; BioMed Central; BMC
• Subjects: Antigens; Biomarkers; Cancer; Cholesterol; Clinical factor; Dyslipidemia; Family medical history; Laboratories; Lifestyle risk factor; Oncology, Experimental; Prediction model; Prediction models; Prostate cancer; Prostate-specific antigen; Risk factors; Urology; South Korea; United States > US; Clinical factor; Lifestyle risk factor; Prostate-specific antigen; Prostate cancer; Prediction model
• ISSN: 1471-2490; 1471-2490
• DOI: 10.1186/s12894-023-01259-w

The 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. The 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. The 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). Our 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.