Literature DB >> 24475843

Digital mammography screening: association between detection rate and nuclear grade of ductal carcinoma in situ.

Stefanie Weigel1, Walter Heindel, Oliver Heidinger, Shoma Berkemeyer, Hans Werner Hense.   

Abstract

PURPOSE: To determine the relationship between overall detection rates of ductal carcinoma in situ (DCIS) and the specific detection rates of low-, intermediate-, and high-grade DCIS at the start of a digital mammography screening program.
MATERIALS AND METHODS: The study was approved by the local ethics board and did not require informed consent. Data were included of the first round of digital mammography examinations, performed in 17 screening units in women aged 50-69 years from 2005 to 2008. Grading was provided by the cancer registry for 1018 DCIS cases. The association between the overall cancer detection rate (cases per 100 women screened) and the separate cancer detection rate for invasive cancers and for DCIS was assessed. Likewise, the total DCIS cancer detection rate was separated into rates for low, intermediate, and high grades. Spearman rank correlations were used for analysis.
RESULTS: The overall cancer detection rate correlated with both the cancer detection rate of invasive cancers and the cancer detection rate of DCIS (r = 0.96 and r = 0.88, respectively; P < .001 for both). The cancer detection rate of total DCIS with grading varied among screening units (range, 0.05-0.25), it was borderline not significantly correlated with the cancer detection rate of low-grade DCIS (range, 0.004-0.05; r = 0.49; P = .052), and it showed significant correlations with higher cancer detection rate of intermediate-grade DCIS (range, 0.02-0.12; r = 0.89; P < .001) and of high-grade DCIS (range, 0.03-0.11; r = 0.88; P < .001).
CONCLUSION: This study demonstrates that high overall cancer detection rates in digital mammography screening are related to high detection rates of invasive cancers, as well as DCIS. Increases in the detection rates of DCIS were not driven by disproportionate increments of the slowly progressive low-grade subtype but rather by increased rates of intermediate- and high-grade subtypes that carry a higher risk of transition to invasive cancers. RSNA, 2013

Entities:  

Mesh:

Year:  2013        PMID: 24475843     DOI: 10.1148/radiol.13131498

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


  6 in total

1.  Breast cancer detection rates using four different types of mammography detectors.

Authors:  Alistair Mackenzie; Lucy M Warren; Matthew G Wallis; Julie Cooke; Rosalind M Given-Wilson; David R Dance; Dev P Chakraborty; Mark D Halling-Brown; Padraig T Looney; Kenneth C Young
Journal:  Eur Radiol       Date:  2015-06-25       Impact factor: 5.315

2.  Chronological Trends of Breast Ductal Carcinoma In Situ: Clinical, Radiologic, and Pathologic Perspectives.

Authors:  Si Eun Lee; Ha Yan Kim; Jung Hyun Yoon; Eun-Kyung Kim; Jee Ye Kim; Min Jung Kim; Ga Ram Kim; Youngjean Vivian Park; Hee Jung Moon
Journal:  Ann Surg Oncol       Date:  2021-07-01       Impact factor: 5.344

3.  Does digital mammography suppose an advance in early diagnosis? Trends in performance indicators 6 years after digitalization.

Authors:  Maria Sala; Laia Domingo; Francesc Macià; Mercè Comas; Andrea Burón; Xavier Castells
Journal:  Eur Radiol       Date:  2014-09-26       Impact factor: 5.315

4.  A ten-year, single-center experience: Concordance between breast core needle biopsy/vacuum-assisted biopsy and postoperative histopathology in B3 and B5a cases.

Authors:  Mohamed Elsharkawy; Thomas Vestring; Hans-Juergen Raatschen
Journal:  PLoS One       Date:  2020-05-21       Impact factor: 3.240

5.  Informed shared decision-making supported by decision coaches for women with ductal carcinoma in situ: study protocol for a cluster randomized controlled trial.

Authors:  Birte Berger-Höger; Katrin Liethmann; Ingrid Mühlhauser; Burkhard Haastert; Anke Steckelberg
Journal:  Trials       Date:  2015-10-12       Impact factor: 2.279

6.  Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance.

Authors:  Laura Kerschke; Stefanie Weigel; Alejandro Rodriguez-Ruiz; Nico Karssemeijer; Walter Heindel
Journal:  Eur Radiol       Date:  2021-08-12       Impact factor: 5.315

  6 in total

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