Literature DB >> 31478799

Tumor Characteristics and Molecular Subtypes in Breast Cancer Screening with Digital Breast Tomosynthesis: The Malmö Breast Tomosynthesis Screening Trial.

Kristin Johnson1, Sophia Zackrisson1, Aldana Rosso1, Hanna Sartor1, Lao H Saal1, Ingvar Andersson1, Kristina Lång1.   

Abstract

Background Screening accuracy can be improved with digital breast tomosynthesis (DBT). To further evaluate DBT in screening, it is important to assess the molecular subtypes of the detected cancers. Purpose To describe tumor characteristics, including molecular subtypes, of cancers detected at DBT compared with those detected at digital mammography (DM) in breast cancer screening. Materials and Methods The Malmö Breast Tomosynthesis Screening Trial is a prospective, population-based screening trial comparing one-view DBT with two-view DM. Tumor characteristics were obtained, and invasive cancers were classified according to St Gallen as follows: luminal A-like, luminal B-like human epidermal growth factor receptor (HER)2-negative/HER2-positive, HER2-positive, and triple-negative cancers. Tumor characteristics were compared by mode of detection: DBT alone or DM (ie, DBT and DM or DM alone). χ2 test was used for data analysis. Results Between January 2010 and February 2015, 14 848 women were enrolled (mean age, 57 years ± 10; age range, 40-76 years). In total, 139 cancers were detected; 118 cancers were invasive and 21 were ductal carcinomas in situ. Thirty-seven additional invasive cancers (36 cancers with complete subtypes and stage) were detected at DBT alone, and 81 cancers (80 cancers with complete stage) were detected at DM. No differences were seen between DBT and DM in the distribution of tumor size 20 mm or smaller (86% [31 of 36] vs 85% [68 of 80], respectively; P = .88), node-negative status (75% [27 of 36] vs 74% [59 of 80], respectively; P = .89), or luminal A-like subtype (53% [19 of 36] vs 46% [37 of 81], respectively; P = .48). Conclusion The biologic profile of the additional cancers detected at digital breast tomosynthesis in a large prospective population-based screening trial was similar to those detected at digital mammography, and the majority were early-stage luminal A-like cancers. This indicates that digital breast tomosynthesis screening does not alter the predictive and prognostic profile of screening-detected cancers. © RSNA, 2019.

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Year:  2019        PMID: 31478799     DOI: 10.1148/radiol.2019190132

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  6 in total

1.  Consecutive Screening Rounds with Digital Breast Tomosynthesis Enable Detection of Breast Cancers with Poor Prognosis.

Authors:  Linda Moy; Samantha L Heller
Journal:  Radiology       Date:  2020-03-10       Impact factor: 11.105

2.  Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.

Authors:  Victor Dahlblom; Ingvar Andersson; Kristina Lång; Anders Tingberg; Sophia Zackrisson; Magnus Dustler
Journal:  Radiol Artif Intell       Date:  2021-09-01

3.  Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Authors:  Hanna Tomic; Anna Bjerkén; Gustav Hellgren; Kristin Johnson; Daniel Förnvik; Sophia Zackrisson; Anders Tingberg; Magnus Dustler; Predrag R Bakic
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-06

4.  Association of Screening With Digital Breast Tomosynthesis vs Digital Mammography With Risk of Interval Invasive and Advanced Breast Cancer.

Authors:  Karla Kerlikowske; Yu-Ru Su; Brian L Sprague; Anna N A Tosteson; Diana S M Buist; Tracy Onega; Louise M Henderson; Nila Alsheik; Michael C S Bissell; Ellen S O'Meara; Christoph I Lee; Diana L Miglioretti
Journal:  JAMA       Date:  2022-06-14       Impact factor: 157.335

5.  Integrating biology and access to care in addressing breast cancer disparities: 25 years' research experience in the Carolina Breast Cancer Study.

Authors:  Marc A Emerson; Katherine E Reeder-Hayes; Heather J Tipaldos; Mary E Bell; Marina R Sweeney; Lisa A Carey; H Shelton Earp; Andrew F Olshan; Melissa A Troester
Journal:  Curr Breast Cancer Rep       Date:  2020-05-14

6.  DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Authors:  Xiaobo Lai; Weiji Yang; Ruipeng Li
Journal:  Comput Math Methods Med       Date:  2020-01-28       Impact factor: 2.238

  6 in total

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