Literature DB >> 33724062

Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial.

Stefanie G A Veenhuizen1, Stéphanie V de Lange1, Marije F Bakker1, Ruud M Pijnappel1, Ritse M Mann1, Evelyn M Monninkhof1, Marleen J Emaus1, Petra K de Koekkoek-Doll1, Robertus H C Bisschops1, Marc B I Lobbes1, Mathijn D F de Jong1, Katya M Duvivier1, Jeroen Veltman1, Nico Karssemeijer1, Harry J de Koning1, Paul J van Diest1, Willem P T M Mali1, Maurice A A J van den Bosch1, Carla H van Gils1, Wouter B Veldhuis1.   

Abstract

Background In the first (prevalent) supplemental MRI screening round of the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a considerable number of breast cancers were found at the cost of an increased false-positive rate (FPR). In incident screening rounds, a lower cancer detection rate (CDR) is expected due to a smaller pool of prevalent cancers, and a reduced FPR, due to the availability of prior MRI examinations. Purpose To investigate screening performance indicators of the second round (incidence round) of the DENSE trial. Materials and Methods The DENSE trial (ClinicalTrials.gov: NCT01315015) is embedded within the Dutch population-based biennial mammography screening program for women aged 50-75 years. MRI examinations were performed between December 2011 and January 2016. Women were eligible for the second round when they again had a negative screening mammogram 2 years after their first MRI. The recall rate, biopsy rate, CDR, FPR, positive predictive values, and distributions of tumor characteristics were calculated and compared with results of the first round using 95% CIs and χ2 tests. Results A total of 3436 women (median age, 56 years; interquartile range, 48-64 years) underwent a second MRI screening. The CDR was 5.8 per 1000 screening examinations (95% CI: 3.8, 9.0) compared with 16.5 per 1000 screening examinations (95% CI: 13.3, 20.5) in the first round. The FPR was 26.3 per 1000 screening examinations (95% CI: 21.5, 32.3) in the second round versus 79.8 per 1000 screening examinations (95% CI: 72.4, 87.9) in the first round. The positive predictive value for recall was 18% (20 of 110 participants recalled; 95% CI: 12.1, 26.4), and the positive predictive value for biopsy was 24% (20 of 84 participants who underwent biopsy; 95% CI: 16.0, 33.9), both comparable to that of the first round. All tumors in the second round were stage 0-I and node negative. Conclusion The incremental cancer detection rate in the second round was 5.8 per 1000 screening examinations-compared with 16.5 per 1000 screening examinations in the first round. This was accompanied by a strong reduction in the number of false-positive results. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moy and Gao in this issue.

Entities:  

Year:  2021        PMID: 33724062     DOI: 10.1148/radiol.2021203633

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


  8 in total

Review 1.  Screening MRI in Women at Intermediate Breast Cancer Risk: An Update of the Recent Literature.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2022-05-08

Review 2.  AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2022.

Authors:  Nina Ditsch; Achim Wöcke; Michael Untch; Christian Jackisch; Ute-Susann Albert; Maggie Banys-Paluchowski; Ingo Bauerfeind; Jens-Uwe Blohmer; Wilfried Budach; Peter Dall; Eva Maria Fallenberg; Peter A Fasching; Tanja N Fehm; Michael Friedrich; Bernd Gerber; Oleg Gluz; Nadia Harbeck; Jörg Heil; Jens Huober; Hans H Kreipe; David Krug; Thorsten Kühn; Sherko Kümmel; Cornelia Kolberg-Liedtke; Sibylle Loibl; Diana Lüftner; Michael Patrick Lux; Nicolai Maass; Christoph Mundhenke; Ulrike Nitz; Tjoung-Won Park-Simon; Toralf Reimer; Kerstin Rhiem; Achim Rody; Marcus Schmidt; Andreas Schneeweiss; Florian Schütz; Hans-Peter Sinn; Christine Solbach; Erich-Franz Solomayer; Elmar Stickeler; Christoph Thomssen; Isabell Witzel; Volkmar Müller; Wolfgang Janni; Marc Thill
Journal:  Breast Care (Basel)       Date:  2022-05-05       Impact factor: 2.268

3.  Cost-Effectiveness of MR-Mammography in Breast Cancer Screening of Women With Extremely Dense Breasts After Two Rounds of Screening.

Authors:  Fabian Tollens; Pascal A T Baltzer; Matthias Dietzel; Moritz L Schnitzer; Wolfgang G Kunz; Johann Rink; Johannes Rübenthaler; Matthias F Froelich; Clemens G Kaiser
Journal:  Front Oncol       Date:  2021-09-09       Impact factor: 6.244

4.  Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance.

Authors:  Isaac Daimiel Naranjo; Peter Gibbs; Jeffrey S Reiner; Roberto Lo Gullo; Sunitha B Thakur; Maxine S Jochelson; Nikita Thakur; Pascal A T Baltzer; Thomas H Helbich; Katja Pinker
Journal:  Cancers (Basel)       Date:  2022-03-29       Impact factor: 6.575

5.  Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI).

Authors:  Ritse M Mann; Alexandra Athanasiou; Pascal A T Baltzer; Julia Camps-Herrero; Paola Clauser; Eva M Fallenberg; Gabor Forrai; Michael H Fuchsjäger; Thomas H Helbich; Fleur Killburn-Toppin; Mihai Lesaru; Pietro Panizza; Federica Pediconi; Ruud M Pijnappel; Katja Pinker; Francesco Sardanelli; Tamar Sella; Isabelle Thomassin-Naggara; Sophia Zackrisson; Fiona J Gilbert; Christiane K Kuhl
Journal:  Eur Radiol       Date:  2022-03-08       Impact factor: 7.034

6.  Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.

Authors:  Anna Landsmann; Carlotta Ruppert; Jann Wieler; Patryk Hejduk; Alexander Ciritsis; Karol Borkowski; Moritz C Wurnig; Cristina Rossi; Andreas Boss
Journal:  Eur Radiol Exp       Date:  2022-07-20

Review 7.  Breast surgery: a narrative review.

Authors:  Christobel M Saunders
Journal:  Med J Aust       Date:  2022-08-21       Impact factor: 12.776

8.  Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis.

Authors:  Isaac Daimiel Naranjo; Peter Gibbs; Jeffrey S Reiner; Roberto Lo Gullo; Caleb Sooknanan; Sunitha B Thakur; Maxine S Jochelson; Varadan Sevilimedu; Elizabeth A Morris; Pascal A T Baltzer; Thomas H Helbich; Katja Pinker
Journal:  Diagnostics (Basel)       Date:  2021-05-21
  8 in total

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