Literature DB >> 35355697

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis.

Hans-Jonas Meyer1, Andreas Wienke2, Alexey Surov3.   

Abstract

Background: Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.
Objectives: This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients. Method: The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.
Results: The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07). Conclusions: ADC values cannot be used to discriminate between molecular subtypes of BC.
Copyright © 2021 by S. Karger AG, Basel.

Entities:  

Keywords:  Apparent diffusion coefficient; Breast cancer; Diffusion-weighted imaging; Meta-analysis; Systematic review

Year:  2021        PMID: 35355697      PMCID: PMC8914237          DOI: 10.1159/000514407

Source DB:  PubMed          Journal:  Breast Care (Basel)        ISSN: 1661-3791            Impact factor:   2.860


  63 in total

Review 1.  Clinical implications of the intrinsic molecular subtypes of breast cancer.

Authors:  Aleix Prat; Estela Pineda; Barbara Adamo; Patricia Galván; Aranzazu Fernández; Lydia Gaba; Marc Díez; Margarita Viladot; Ana Arance; Montserrat Muñoz
Journal:  Breast       Date:  2015-08-05       Impact factor: 4.380

2.  Meta-analysis in clinical trials.

Authors:  R DerSimonian; N Laird
Journal:  Control Clin Trials       Date:  1986-09

3.  Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition.

Authors:  Mieke Kriege; Cecile T M Brekelmans; Carla Boetes; Peter E Besnard; Harmine M Zonderland; Inge Marie Obdeijn; Radu A Manoliu; Theo Kok; Hans Peterse; Madeleine M A Tilanus-Linthorst; Sara H Muller; Sybren Meijer; Jan C Oosterwijk; Louk V A M Beex; Rob A E M Tollenaar; Harry J de Koning; Emiel J T Rutgers; Jan G M Klijn
Journal:  N Engl J Med       Date:  2004-07-29       Impact factor: 91.245

4.  QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

Authors:  Penny F Whiting; Anne W S Rutjes; Marie E Westwood; Susan Mallett; Jonathan J Deeks; Johannes B Reitsma; Mariska M G Leeflang; Jonathan A C Sterne; Patrick M M Bossuyt
Journal:  Ann Intern Med       Date:  2011-10-18       Impact factor: 25.391

5.  Meta-DiSc: a software for meta-analysis of test accuracy data.

Authors:  Javier Zamora; Victor Abraira; Alfonso Muriel; Khalid Khan; Arri Coomarasamy
Journal:  BMC Med Res Methodol       Date:  2006-07-12       Impact factor: 4.615

6.  Prognosis of breast cancer molecular subtypes in routine clinical care: A large prospective cohort study.

Authors:  André Hennigs; Fabian Riedel; Adam Gondos; Peter Sinn; Peter Schirmacher; Frederik Marmé; Dirk Jäger; Hans-Ulrich Kauczor; Anne Stieber; Katja Lindel; Jürgen Debus; Michael Golatta; Florian Schütz; Christof Sohn; Jörg Heil; Andreas Schneeweiss
Journal:  BMC Cancer       Date:  2016-09-15       Impact factor: 4.430

7.  Diffusion-weighted MRI-derived ADC values reflect collagen I content in PDX models of uterine cervical cancer.

Authors:  Anette Hauge; Catherine S Wegner; Jon-Vidar Gaustad; Trude G Simonsen; Lise Mari K Andersen; Einar K Rofstad
Journal:  Oncotarget       Date:  2017-11-11

8.  Pretreatment apparent diffusion coefficient does not predict therapy response to neoadjuvant chemotherapy in breast cancer.

Authors:  Alexey Surov; Andreas Wienke; Hans Jonas Meyer
Journal:  Breast       Date:  2020-06-26       Impact factor: 4.380

9.  Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification.

Authors:  Ming Zhao; Kuang Fu; Lei Zhang; Wenhui Guo; Qiong Wu; Xue Bai; Ziyao Li; Qiang Guo; Jiawei Tian
Journal:  Oncol Lett       Date:  2018-08-16       Impact factor: 2.967

10.  Association Between VEGF Expression and Diffusion Weighted Imaging in Several Tumors-A Systematic Review and Meta-Analysis.

Authors:  Hans-Jonas Meyer; Andreas Wienke; Alexey Surov
Journal:  Diagnostics (Basel)       Date:  2019-09-23
View more
  3 in total

Review 1.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

Review 2.  Diffusion Breast MRI: Current Standard and Emerging Techniques.

Authors:  Ashley M Mendez; Lauren K Fang; Claire H Meriwether; Summer J Batasin; Stéphane Loubrie; Ana E Rodríguez-Soto; Rebecca A Rakow-Penner
Journal:  Front Oncol       Date:  2022-07-08       Impact factor: 5.738

3.  A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer.

Authors:  Valeria Romeo; Panagiotis Kapetas; Paola Clauser; Pascal A T Baltzer; Sazan Rasul; Peter Gibbs; Marcus Hacker; Ramona Woitek; Katja Pinker; Thomas H Helbich
Journal:  Cancers (Basel)       Date:  2022-08-16       Impact factor: 6.575

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.