scholarly journals Risk-adjusted colorectal cancer screening using the FIT and routine screening data: development of a risk prediction model

2017 ◽  
Vol 118 (2) ◽  
pp. 285-293 ◽  
Author(s):  
Jennifer Anne Cooper ◽  
Nick Parsons ◽  
Chris Stinton ◽  
Christopher Mathews ◽  
Steve Smith ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Colorectal Cancer Screening ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Routine Screening

scholarly journals Risk Prediction Model for Lung Cancer Screening

2019 ◽  
Vol 80 (5) ◽  
pp. 860
Author(s):  
Tae Jung Kim ◽  
Hyae Young Kim ◽  
Jin Mo Goo ◽  
Joo Sung Sun
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Screening ◽  
Risk Prediction Model

Selection of individuals for lung cancer screening based on risk prediction model performance and economic factors – The Ontario experience

Lung Cancer ◽  
2021 ◽  
Vol 156 ◽  
pp. 31-40
Author(s):  
Martin C. Tammemägi ◽  
Gail E. Darling ◽  
Heidi Schmidt ◽  
Diego Llovet ◽  
Daniel N. Buchanan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Risk Prediction ◽  
Model Performance ◽  
Lung Cancer Screening ◽  
Risk Prediction Model ◽  
Economic Factors ◽  
Selection Of

scholarly journals The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Sibel Saya ◽  
Jon D Emery ◽  
James G Dowty ◽  
Jennifer G McIntosh ◽  
Ingrid M Winship ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Family History ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Screening Colonoscopy ◽  
Crc Screening ◽  
Target Screening ◽  
The Impact ◽  
Risk Categories

Abstract Background In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. Methods We estimated the model’s predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. Results Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. Conclusions A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.

scholarly journals Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88079 ◽  
Author(s):  
Aesun Shin ◽  
Jungnam Joo ◽  
Hye-Ryung Yang ◽  
Jeongin Bak ◽  
Yunjin Park ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Health Insurance ◽  
Prediction Model ◽  
Risk Prediction ◽  
National Health Insurance ◽  
National Health ◽  
Risk Prediction Model

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

Cancers ◽  
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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