Literature DB >> 27393617

Characteristics and prognosis of interval cancers after biennial screen-film or full-field digital screening mammography.

Roy J P Weber1, Rob M G van Bommel2, Marieke W Louwman3, Joost Nederend2, Adri C Voogd3,4, Frits H Jansen2, Vivianne C G Tjan-Heijnen5, Lucien E M Duijm6,7.   

Abstract

We determined the characteristics and prognosis of interval breast cancers (IC) at screen-film (SFM) and full-field digital (FFDM) screening mammography. The study population consisted of 417,746 consecutive screening mammograms (302,699 SFM screens and 115,047 FFDM screens), obtained between 2000 and 2011. During 2-year follow-up, we collected breast imaging reports, surgical reports, and pathology results. A total of 800 ICs had been diagnosed in the screened population, of which 288 detected in the first year (early ICs) and 512 in the second year (late ICs) after a negative screen. 31.3 % of early IC's and 19.1 % of late IC's, respectively, were visible in retrospect on the latest previous screens, but had been missed during screening (P < 0.001). Missed invasive ICs were larger (28.5 mm vs. 23.9 mm, P = 0.003) and showed a higher fraction of T3+cancers (16.9 vs. 8.5 %, P = 0.02) than true ICs (i.e., not visible at the latest screen). A higher portion of missed than true ICs underwent mastectomy (44.7 vs. 30.8 %, P = 0.002). We found no differences in mammographic and tumor characteristics for early ICs, detected either after SFM or FFDM. Late ICs following FFDM were more often true ICs than missed ICs (69.0 vs. 57.6 %, P = 0.03) and more often receptor triple negative (P = 0.02), compared to late ICs at SFM. Interval cancer subgroups showed comparable overall survival. Interval cancer subgroups show distinctive mammographic and tumor characteristics but a comparable overall survival.

Entities:  

Keywords:  Breast cancer; Digital mammography and survival rates; Interval cancer; Screening mammography

Mesh:

Year:  2016        PMID: 27393617     DOI: 10.1007/s10549-016-3882-0

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  17 in total

1.  Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Authors:  Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Tao Tan; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ritse M Mann; Ioannis Sechopoulos
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

2.  Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.

Authors:  Frederik Abel; Anna Landsmann; Patryk Hejduk; Carlotta Ruppert; Karol Borkowski; Alexander Ciritsis; Cristina Rossi; Andreas Boss
Journal:  Diagnostics (Basel)       Date:  2022-05-29

3.  Organized screening detects breast cancer at earlier stage regardless of molecular phenotype.

Authors:  Claire M B Holloway; Li Jiang; Marlo Whitehead; Jennifer M Racz; Patti A Groome
Journal:  J Cancer Res Clin Oncol       Date:  2018-06-16       Impact factor: 4.553

Review 4.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

5.  The importance of early detection of calcifications associated with breast cancer in screening.

Authors:  J J Mordang; A Gubern-Mérida; A Bria; F Tortorella; R M Mann; M J M Broeders; G J den Heeten; N Karssemeijer
Journal:  Breast Cancer Res Treat       Date:  2017-10-17       Impact factor: 4.872

6.  The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening.

Authors:  Nehmat Houssami; Kylie Hunter
Journal:  NPJ Breast Cancer       Date:  2017-04-13

7.  Quantification of masking risk in screening mammography with volumetric breast density maps.

Authors:  Katharina Holland; Carla H van Gils; Ritse M Mann; Nico Karssemeijer
Journal:  Breast Cancer Res Treat       Date:  2017-02-04       Impact factor: 4.872

8.  Comparison of Mortality Among Participants of Women's Health Initiative Trials With Screening-Detected Breast Cancers vs Interval Breast Cancers.

Authors:  Veronica L Irvin; Zhenzhen Zhang; Michael S Simon; Rowan T Chlebowski; Shiuh-Wen Luoh; Aladdin H Shadyab; Jessica L Krok-Schoen; Fred K Tabung; Lihong Qi; Marcia L Stefanick; Pepper Schedin; Sonali Jindal
Journal:  JAMA Netw Open       Date:  2020-06-01

9.  The frequency of missed breast cancers in women participating in a high-risk MRI screening program.

Authors:  S Vreemann; A Gubern-Merida; S Lardenoije; P Bult; N Karssemeijer; K Pinker; R M Mann
Journal:  Breast Cancer Res Treat       Date:  2018-01-31       Impact factor: 4.872

10.  A deep learning-based automated diagnostic system for classifying mammographic lesions.

Authors:  Takeshi Yamaguchi; Kenichi Inoue; Hiroko Tsunoda; Takayoshi Uematsu; Norimitsu Shinohara; Hirofumi Mukai
Journal:  Medicine (Baltimore)       Date:  2020-07-02       Impact factor: 1.817

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