Literature DB >> 24975995

Sensitivity, effect and overdiagnosis in screening for cancers with detectable pre-invasive phase.

Matti Hakama1, Arun Pokhrel, Nea Malila, Timo Hakulinen.   

Abstract

Studies on cancer screening often evaluate the performance by indirect indicators. In case the screening detects pre-invasive lesions, they may be a mixture of benefit of sensitivity and effect as well as of harm of overdiagnosis. Here, we develop the formulae for the sensitivity, the effect and overdiagnosis in screening for pre-invasive lesions of cancer. Sensitivity is the ability of screening to identify a progressive lesion at the level of test (relevant for the laboratory), episode (relevant in the clinic) and programme (relevant at the population level). Effect is reduction of cancer incidence in those screened (efficacy) and in the target population (effectiveness). The sensitivity is estimated by interval cancers between two consecutive screens (incidence method) and the effect by interval cancers and cancers detected at the subsequent screen. Overdiagnosis is estimated as the detection rate of pre-invasive lesions minus the rate of invasive cancer prevented by screening in one screening round. All the indicators are corrected for nonattendance and selective attendance by disease risk. The population to be followed and the period of follow-up are defined for each indicator separately. Data on cervix cancer screening with Papnet® automation device are given as an example. Estimation of sensitivity and effect are consistent with the purpose of the screening to prevent invasive disease. We further define the purpose at the level of laboratory, clinical medicine and public health and derive six estimators corresponding to the specific purposes considered in our article.
© 2014 UICC.

Entities:  

Keywords:  effectiveness; efficacy; episode sensitivity; overdiagnosis; programme sensitivity; test sensitivity

Mesh:

Year:  2014        PMID: 24975995     DOI: 10.1002/ijc.29053

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  5 in total

Review 1.  Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

Authors:  Abraham Pouliakis; Efrossyni Karakitsou; Niki Margari; Panagiotis Bountris; Maria Haritou; John Panayiotides; Dimitrios Koutsouris; Petros Karakitsos
Journal:  Biomed Eng Comput Biol       Date:  2016-02-18

2.  Are CIN3 risk or CIN3+ risk measures reliable surrogates for invasive cervical cancer risk?

Authors:  R Marshall Austin; Agnieszka Onisko; Chengquan Zhao
Journal:  J Am Soc Cytopathol       Date:  2020-07-29

3.  Association of symptoms and interval breast cancers in the mammography-screening programme: population-based matched cohort study.

Authors:  Deependra Singh; Joonas Miettinen; Stephen Duffy; Nea Malila; Janne Pitkäniemi; Ahti Anttila
Journal:  Br J Cancer       Date:  2018-11-07       Impact factor: 7.640

4.  Contributions of Liquid-Based (Papanicolaou) Cytology and Human Papillomavirus Testing in Cotesting for Detection of Cervical Cancer and Precancer in the United States.

Authors:  Harvey W Kaufman; Damian P Alagia; Zhen Chen; Agnieszka Onisko; R Marshall Austin
Journal:  Am J Clin Pathol       Date:  2020-09-08       Impact factor: 2.493

5.  Individualized Bayesian Risk Assessment for Cervical Squamous Neoplasia.

Authors:  Lama F Farchoukh; Agnieszka Onisko; R Marshall Austin
Journal:  J Pathol Inform       Date:  2020-03-30
  5 in total

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