Literature DB >> 31012817

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization.

Gabrielle C Baxter1, Martin J Graves1, Fiona J Gilbert1, Andrew J Patterson1.   

Abstract

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Entities:  

Year:  2019        PMID: 31012817     DOI: 10.1148/radiol.2019182510

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


  21 in total

Review 1.  Six DWI questions you always wanted to know but were afraid to ask: clinical relevance for breast diffusion MRI.

Authors:  Mami Iima; Savannah C Partridge; Denis Le Bihan
Journal:  Eur Radiol       Date:  2020-01-21       Impact factor: 5.315

Review 2.  Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis.

Authors:  LuShun Zhang; LongLin Yin; MeiLin Zhu; ChuanDe Zhang; JingXin Yan; Ju Sun; XinYi Zhao
Journal:  Eur Radiol       Date:  2021-04-13       Impact factor: 5.315

Review 3.  Challenges in ensuring the generalizability of image quantitation methods for MRI.

Authors:  Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza
Journal:  Med Phys       Date:  2021-09-29       Impact factor: 4.506

4.  Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma.

Authors:  Qiyi Hu; Guojie Wang; Xiaoyi Song; Jingjing Wan; Man Li; Fan Zhang; Qingling Chen; Xiaoling Cao; Shaolin Li; Ying Wang
Journal:  Cancers (Basel)       Date:  2022-06-30       Impact factor: 6.575

5.  Can DWI provide additional value to Kaiser score in evaluation of breast lesions.

Authors:  Yongyu An; Guoqun Mao; Weiqun Ao; Fan Mao; Hongxia Zhang; Yougen Cheng; Guangzhao Yang
Journal:  Eur Radiol       Date:  2022-03-31       Impact factor: 7.034

6.  Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.

Authors:  Alexey Surov; Hans Jonas Meyer; Andreas Wienke
Journal:  BMC Cancer       Date:  2019-10-15       Impact factor: 4.430

7.  Breast MRI in the era of diffusion weighted imaging: do we still need signal-intensity time curves?

Authors:  Matthias Dietzel; Stephan Ellmann; Rüdiger Schulz-Wendtland; Paola Clauser; Evelyn Wenkel; Michael Uder; Pascal A T Baltzer
Journal:  Eur Radiol       Date:  2019-07-29       Impact factor: 5.315

8.  Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis.

Authors:  Kai Wang; Zhipeng Li; Zhifeng Wu; Yucong Zheng; Sihui Zeng; Linning E; Jianye Liang
Journal:  Front Oncol       Date:  2019-11-18       Impact factor: 6.244

9.  Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers.

Authors:  Mengtian Sun; Jingliang Cheng; Cuiping Ren; Yong Zhang; Yinhua Li; Ying Li; Suping Zhang
Journal:  Quant Imaging Med Surg       Date:  2021-08

10.  Mean Apparent Diffusion Coefficient Is a Sufficient Conventional Diffusion-weighted MRI Metric to Improve Breast MRI Diagnostic Performance: Results from the ECOG-ACRIN Cancer Research Group A6702 Diffusion Imaging Trial.

Authors:  Elizabeth S McDonald; Justin Romanoff; Habib Rahbar; Averi E Kitsch; Sara M Harvey; Jennifer G Whisenant; Thomas E Yankeelov; Linda Moy; Wendy B DeMartini; Basak E Dogan; Wei T Yang; Lilian C Wang; Bonnie N Joe; Lisa J Wilmes; Nola M Hylton; Karen Y Oh; Luminita A Tudorica; Colleen H Neal; Dariya I Malyarenko; Christopher E Comstock; Mitchell D Schnall; Thomas L Chenevert; Savannah C Partridge
Journal:  Radiology       Date:  2020-11-17       Impact factor: 11.105

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