Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population

Abstract

PurposeThe use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model.Materials and MethodsA total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model’s performance was evaluated using the C-statistic and Hosmer‒Lemeshow type chi-square statistics.ResultsThe risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001).ConclusionThis model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

“…Additionally, we found calculators that were developed de novo and that include different risk factors from the canonical ERPSC and PCPT RCs. For example, the Lifestyle Risk Prediction Model for Prostate Cancer by Kim et al [ 15 ] includes height, weight, glucose levels, meat and alcohol consumption, smoking status, and physical activity. The risk calculator by Albright et al [ 16 ] incorporates a detailed extended family history to calculate the risk of prostate cancer, and the risk calculator by Jalali et al [ 17 ] combines traditional measurements of PSA and DRE with family history.…”

“…For example, for the PCPT RC, AUCs for prostate cancer peaked at 0.783, while those for high-grade prostate cancer could be as high as 0.813. Furthermore, risk calculators created from scratch also showed high predictive capabilities on their target population, such as the Korean Prostate Cancer Risk Calculator (AUC 0.887) by Kim et al [ 15 ], which uses socioenvironmental aspects of their population to create the predictive models, and the risk calculator by Albright et al [ 16 ], which stratifies risk depending on the number of extended family members with prostate cancer.…”

“…For example, the calculator by Yuri et al [ 45 ] designed for an Indonesian population resulted in an AUC of 0.938 when using a simple list of 5 items. Similarly, the calculator by Kim et al [ 15 ] designed for a South Korean population reached an AUC of 0.887 and focused on epidemiologic factors over serum markers.…”

Determinants Associated with Participation in Physical Activity Among Patients with Prostate Cancer

“…In Asian populations, prostate cancer has a different epidemiology compared to Western populations [25]. Therefore, the factors affecting PA after prostate cancer treatment may also be different.…”

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer

“…Previous work and prediction models were mostly based on regression models or support vector machine. 38–58 In contrast, our work introduced the prediction model, which has first utilized the deep neural network with two layers for the prediction model development for PCa.…”