Literature DB >> 36051671

When to initiate cancer screening exam?

Dongfeng Wu1.   

Abstract

A probability method is developed to decide when to initiate cancer screening for asymptomatic individuals. The probability of incidence is a function of screening sensitivity, time duration in the disease-free state and sojourn time in the preclinical state; and it is monotonically increasing as time increases, given a person's current age. So a unique solution of the first screening time can be found by limiting this probability to a small value, such as 10% or 20%. That is, with 90% or 80% probability, one will not be a clinical incident case before the first exam. After this age is found, we can further estimate the lead time distribution and probability of over-diagnosis if one would be diagnosed with cancer at the first exam. Simulations were carried out under different scenarios; and the method was applied to two heavy smoker cohorts in the National Lung Screening Trial using low-dose computerized tomography. The method is applicable to other kinds of cancer screening. The predictive information can be used by physicians or individuals at risk to make informed decisions on when to initiate screening.

Entities:  

Keywords:  Lead time; Overdiagnosis; Scheduling; Sensitivity; Sojourn time; Transition density

Year:  2022        PMID: 36051671      PMCID: PMC9432459          DOI: 10.4310/21-sii716

Source DB:  PubMed          Journal:  Stat Interface        ISSN: 1938-7989            Impact factor:   0.716


  14 in total

1.  The National Lung Screening Trial: overview and study design.

Authors:  Denise R Aberle; Christine D Berg; William C Black; Timothy R Church; Richard M Fagerstrom; Barbara Galen; Ilana F Gareen; Constantine Gatsonis; Jonathan Goldin; John K Gohagan; Bruce Hillman; Carl Jaffe; Barnett S Kramer; David Lynch; Pamela M Marcus; Mitchell Schnall; Daniel C Sullivan; Dorothy Sullivan; Carl J Zylak
Journal:  Radiology       Date:  2010-11-02       Impact factor: 11.105

2.  Sojourn time and lead time projection in lung cancer screening.

Authors:  Dongfeng Wu; Diane Erwin; Gary L Rosner
Journal:  Lung Cancer       Date:  2010-11-13       Impact factor: 5.705

3.  MLE and Bayesian inference of age-dependent sensitivity and transition probability in periodic screening.

Authors:  Dongfeng Wu; Gary L Rosner; Lyle Broemeling
Journal:  Biometrics       Date:  2005-12       Impact factor: 2.571

Review 4.  Cancer screening in the United States, 2018: A review of current American Cancer Society guidelines and current issues in cancer screening.

Authors:  Robert A Smith; Kimberly S Andrews; Durado Brooks; Stacey A Fedewa; Deana Manassaram-Baptiste; Debbie Saslow; Otis W Brawley; Richard C Wender
Journal:  CA Cancer J Clin       Date:  2018-05-30       Impact factor: 508.702

Review 5.  Low-Dose CT Scan for Lung Cancer Screening: Clinical and Coding Considerations.

Authors:  Yiwey Shieh; Martin Bohnenkamp
Journal:  Chest       Date:  2017-03-21       Impact factor: 9.410

6.  Reduced lung-cancer mortality with low-dose computed tomographic screening.

Authors:  Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks
Journal:  N Engl J Med       Date:  2011-06-29       Impact factor: 91.245

7.  Estimation of Lead Time via Low-Dose CT in the National Lung Screening Trial.

Authors:  Ruiqi Liu; Adriana Pérez; Dongfeng Wu
Journal:  J Healthc Inform Res       Date:  2018-06-12

8.  Bayesian inference for the lead time in periodic cancer screening.

Authors:  Dongfeng Wu; Gary L Rosner; Lyle D Broemeling
Journal:  Biometrics       Date:  2007-09       Impact factor: 2.571

9.  Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study.

Authors:  J S Mandel; J H Bond; T R Church; D C Snover; G M Bradley; L M Schuman; F Ederer
Journal:  N Engl J Med       Date:  1993-05-13       Impact factor: 91.245

10.  Bayesian lead time estimation for the Johns Hopkins Lung Project data.

Authors:  Hyejeong Jang; Seongho Kim; Dongfeng Wu
Journal:  J Epidemiol Glob Health       Date:  2013-06-14
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