Literature DB >> 16510335

Radiological surveillance of interval breast cancers in screening programmes.

Nehmat Houssami1, Les Irwig, Stefano Ciatto.   

Abstract

Interval breast cancers-those diagnosed after a negative mammographic screen and before the next scheduled screen-are an important indicator of the potential effectiveness of population screening for breast cancer. Although the incidence of interval cancers is usually monitored, radiological surveillance is not undertaken routinely in most screening programmes. Here, we describe radiological surveillance of interval breast cancers and discuss methodological difficulties in the radiological review process and in the categorisation of interval cancers as false-negative, true, or occult. Furthermore, we identify methods that affect whether an interval cancer is classified as a false-negative (missed) or a true interval cancer. For all radiological categories of interval cancers, we outline possible changes to screening programmes that might improve cancer detection. Standardised radiological surveillance of interval cancers might allow within-programme comparisons and has the potential to guide practice and improve quality.

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Year:  2006        PMID: 16510335     DOI: 10.1016/S1470-2045(06)70617-9

Source DB:  PubMed          Journal:  Lancet Oncol        ISSN: 1470-2045            Impact factor:   41.316


  18 in total

1.  Value of audits in breast cancer screening quality assurance programmes.

Authors:  Tanya D Geertse; Roland Holland; Janine M H Timmers; Ellen Paap; Ruud M Pijnappel; Mireille J M Broeders; Gerard J den Heeten
Journal:  Eur Radiol       Date:  2015-04-23       Impact factor: 5.315

2.  Proportional incidence and radiological review of large (T2+) breast cancers as surrogate indicators of screening programme performance.

Authors:  S Ciatto; D Bernardi; M Pellegrini; G Borsato; P Peterlongo; M A Gentilini; F Caumo; A Frigerio; N Houssami
Journal:  Eur Radiol       Date:  2011-12-27       Impact factor: 5.315

3.  Wavelet-based scaling indices for breast cancer diagnostics.

Authors:  T Roberts; M Newell; W Auffermann; B Vidakovic
Journal:  Stat Med       Date:  2017-02-22       Impact factor: 2.373

4.  Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus, Medicad) with two commercial systems.

Authors:  S Ciatto; D Cascio; F Fauci; R Magro; G Raso; R Ienzi; F Martinelli; M Vasile Simone
Journal:  Radiol Med       Date:  2009-05-14       Impact factor: 3.469

5.  Analysis of interval cancers observed in an Italian mammography screening programme (2000-2006).

Authors:  F Caumo; F Vecchiato; M Pellegrini; M Vettorazzi; S Ciatto; S Montemezzi
Journal:  Radiol Med       Date:  2009-06-23       Impact factor: 3.469

6.  Can artificial intelligence reduce the interval cancer rate in mammography screening?

Authors:  Kristina Lång; Solveig Hofvind; Alejandro Rodríguez-Ruiz; Ingvar Andersson
Journal:  Eur Radiol       Date:  2021-01-23       Impact factor: 5.315

7.  Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain.

Authors:  Laia Domingo; Dolores Salas; Raquel Zubizarreta; Marisa Baré; Garbiñe Sarriugarte; Teresa Barata; Josefa Ibáñez; Jordi Blanch; Montserrat Puig-Vives; Ana Fernández; Xavier Castells; Maria Sala
Journal:  Breast Cancer Res       Date:  2014-01-10       Impact factor: 6.466

8.  Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study.

Authors:  Marina Pollán; Nieves Ascunce; María Ederra; Alberto Murillo; Nieves Erdozáin; Jose Alés-Martínez; Roberto Pastor-Barriuso
Journal:  Breast Cancer Res       Date:  2013-01-29       Impact factor: 6.466

9.  Methodological issues for determining intervals of subsequent cancer screening.

Authors:  Jong-Myon Bae
Journal:  Epidemiol Health       Date:  2014-07-30

10.  Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.

Authors:  Kavitha Krishnan; Laura Baglietto; Carmel Apicella; Jennifer Stone; Melissa C Southey; Dallas R English; Graham G Giles; John L Hopper
Journal:  Breast Cancer Res       Date:  2016-06-18       Impact factor: 6.466

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