Rescreening of Persons With a Negative Colonoscopy Result: Results From a Microsimulation Model

Knudsen, Amy B.; Hur, Chin; Gazelle, G. Scott; Schrag, Deborah; McFarland, Elizabeth G.; Kuntz, Karen M.

doi:10.7326/0003-4819-157-9-201211060-00005

Cited by 28 publications

(35 citation statements)

References 58 publications

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“…This study contributes new information to the field of PDAC screening by introducing contemporary simulation methods that are being used to develop and analyze cancer policy models in other organ systems (33, 36, 49, 55–56). Our model is calibrated to National Cancer Institute SEER data, thereby leveraging high-quality data that are representative of the U.S. population to model pancreatic cancer.…”

Section: Discussionmentioning

confidence: 99%

Targeted Screening of Individuals at High Risk for Pancreatic Cancer: Results of a Simulation Model

Pandharipande¹,

Heberle

Dowling³

et al. 2015

Radiology

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Purpose To identify when, from the standpoint of relative risk, MRI-based pancreatic cancer screening may be effective in patients with a known or suspected genetic predisposition. Methods We developed a Markov model of pancreatic ductal adenocarcinoma (PDAC). The model was calibrated to National Cancer Institute Surveillance, Epidemiology and End Results registry data and informed by the published literature. A hypothetical screening strategy was evaluated in which all population individuals underwent one-time MRI screening at age 50. Screening outcomes for individuals with average PDAC risk (base case) were compared to those with increased risk, to assess for differential benefits in populations with a known or suspected genetic predisposition. Effects of varying key inputs, including MRI performance, surgical mortality, and screening age, were evaluated in sensitivity analysis. Results In the base case, screening resulted in a small number of cancer deaths averted (39/100,000 (men), 38/100,000 (women)) and a net decrease in life expectancy, driven by surgical deaths from false-positive results (-3 days (men), −4 days (women)). Life expectancy gains were achieved if individuals’ risk for PDAC exceeded 2.4× (men) or 2.7× (women) that of the general population. When relative risk increased further, for example to 30× that of the general population, averted cancer deaths and life expectancy gains increased substantially (1,219/100,000, 65 days (men) and 1,204/100,000, 71 days (women)). Results were additionally sensitive to MRI specificity and the surgical mortality rate. Conclusions Although PDAC screening with MRI for the entire population is not effective, individuals with even modestly increased risk may benefit.

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Section: Discussionmentioning

confidence: 99%

Targeted Screening of Individuals at High Risk for Pancreatic Cancer: Results of a Simulation Model

Pandharipande¹,

Heberle

Dowling³

et al. 2015

Radiology

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“…A current question in colorectal cancer screening is whether individuals with one negative colonoscopy require another 10 years later; proposed alternatives include longer screening intervals 15 or screening with fecal immunochemical stool testing. 16 Intensification of screening regimens because of positive findings on a screening exam, such as cervical dysplasia, actually represents surveillance rather than screening and is not discussed here.…”

Section: Classes Of Factors Used To Determine Riskmentioning

confidence: 99%

Targeted Cancer Screening in Average-Risk Individuals

Freedman

Khoury

2015

American Journal of Preventive Medicine

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Targeted cancer screening refers to use of disease risk information to identify those most likely to benefit from screening. Researchers have begun to explore the possibility of refining screening regimens for average-risk individuals using genetic and non-genetic risk factors and previous screening experience. Average-risk individuals are those not known to be at substantially elevated risk, including those without known inherited predisposition, without comorbidities known to increase cancer risk, and without previous diagnosis of cancer or pre-cancer. In this paper, we describe the goals of targeted cancer screening in average-risk individuals, present factors on which cancer screening has been targeted, discuss inclusion of targeting in screening guidelines issued by major U.S. professional organizations, and present evidence to support or question such inclusion. Screening guidelines for average-risk individuals currently target age; smoking (lung cancer only); and, in some instances, race; family history of cancer; and previous negative screening history (cervical cancer only). No guidelines include common genomic polymorphisms. RCTs suggest that targeting certain ages and smoking histories reduces disease-specific cancer mortality, although some guidelines extend ages and smoking histories based on statistical modeling. Guidelines that are based on modestly elevated disease risk typically have either no or little evidence of an ability to affect a mortality benefit. In time, targeted cancer screening is likely to include genetic factors and past screening experience as well as non-genetic factors other than age, smoking, and race, but it is of utmost importance that clinical implementation be evidence-based.

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“…Many of these models include a component that describes the natural history of the disease and simulates an individual’s course of health to assess the overall effect of the disease in the population [4, 5, 6, 7]. For example, several models for breast, prostate, colon, and lung cancer have been used to evaluate various cancer screening policies while representing both unobservable and observable portions of the natural history of cancer at an individual level [8, 9, 10, 11, 12, 13, 14]. In particular, natural history components of these simulation models require the use of unobservable parameters such as disease onset and tumor growth rate, which often cannot be estimated from available data sources [13, 15].…”

Section: Introductionmentioning

confidence: 99%

Using Active Learning for Speeding up Calibration in Simulation Models

et al. 2015

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Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration.

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Rescreening of Persons With a Negative Colonoscopy Result: Results From a Microsimulation Model

Cited by 28 publications

References 58 publications

Targeted Screening of Individuals at High Risk for Pancreatic Cancer: Results of a Simulation Model

Targeted Screening of Individuals at High Risk for Pancreatic Cancer: Results of a Simulation Model

Targeted Cancer Screening in Average-Risk Individuals

Using Active Learning for Speeding up Calibration in Simulation Models

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