Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821–0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

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Individual 5-Year Lung Cancer Risk Prediction Model in Korea Using a Nationwide Representative Database

Cancers ◽

10.3390/cancers13143496 ◽

2021 ◽

Vol 13 (14) ◽

pp. 3496

Author(s):

Yohwan Yeo ◽

Dong Wook Shin ◽

Kyungdo Han ◽

Sang Hyun Park ◽

Keun-Hye Jeon ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Lung Cancer Screening ◽

Risk Prediction Model ◽

C Statistic ◽

The Mean

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801–0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.

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Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

World Journal of Urology ◽

10.1007/s00345-020-03256-2 ◽

2020 ◽

Author(s):

I- Hsuan Alan Chen ◽

Chi-Hsiang Chu ◽

Jen-Tai Lin ◽

Jeng -Yu Tsai ◽

Chia-Cheng Yu ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Prostate Cancer Risk ◽

Risk Prediction Model ◽

Risk Calculator ◽

Taiwanese Population

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A Unified Prostate Cancer Risk Prediction Model Combining the Stockholm3 Test and Magnetic Resonance Imaging

European Urology Oncology ◽

10.1016/j.euo.2018.09.008 ◽

2019 ◽

Vol 2 (5) ◽

pp. 490-496 ◽

Cited By ~ 4

Author(s):

Thorgerdur Palsdottir ◽

Tobias Nordström ◽

Markus Aly ◽

Fredrik Jäderling ◽

Mark Clements ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Magnetic Resonance ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Prostate Cancer Risk ◽

Risk Prediction Model ◽

Resonance Imaging ◽

Model Combining

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Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists

CHEST Journal ◽

10.1016/j.chest.2019.04.002 ◽

2019 ◽

Vol 156 (1) ◽

pp. 112-119 ◽

Cited By ~ 2

Author(s):

Heber MacMahon ◽

Feng Li ◽

Yulei Jiang ◽

Samuel G. Armato

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Risk Prediction Model

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The Cytokinesis-Blocked Micronucleus Assay as a Strong Predictor of Lung Cancer: Extension of a Lung Cancer Risk Prediction Model

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-14-0462 ◽

2014 ◽

Vol 23 (11) ◽

pp. 2462-2470 ◽

Cited By ~ 21

Author(s):

Randa A. El-Zein ◽

Mirtha S. Lopez ◽

Anthony M. D'Amelio ◽

Mei Liu ◽

Reginald F. Munden ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Prediction Model ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Strong Predictor ◽

Micronucleus Assay ◽

Risk Prediction Model

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Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

BMC Medical Research Methodology ◽

10.1186/s12874-019-0839-0 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Johanna Tolksdorf ◽

Michael W. Kattan ◽

Stephen A. Boorjian ◽

Stephen J. Freedland ◽

Karim Saba ◽

...

Keyword(s):

Prostate Cancer ◽

Risk Factors ◽

Family History ◽

Cancer Risk ◽

Risk Prediction ◽

Prostate Biopsy ◽

Prostate Cancer Risk ◽

High Grade ◽

Prediction Tools ◽

History Of

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

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