Literature DB >> 22863284

Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI.

M L Marinovich1, F Sardanelli, S Ciatto, E Mamounas, M Brennan, P Macaskill, L Irwig, G von Minckwitz, N Houssami.   

Abstract

Magnetic resonance imaging (MRI) has been proposed to have a role in predicting final pathologic response when undertaken early during neoadjuvant chemotherapy (NAC) in breast cancer. This paper examines the evidence for MRI's accuracy in early response prediction. A systematic literature search (to February 2011) was performed to identify studies reporting the accuracy of MRI during NAC in predicting pathologic response, including searches of MEDLINE, PREMEDLINE, EMBASE, and Cochrane databases. 13 studies were eligible (total 605 subjects, range 16-188). Dynamic contrast-enhanced (DCE) MRI was typically performed after 1-2 cycles of anthracycline-based or anthracycline/taxane-based NAC, and compared to a pre-NAC baseline scan. MRI parameters measured included changes in uni- or bidimensional tumour size, three-dimensional volume, quantitative dynamic contrast measurements (volume transfer constant [Ktrans], exchange rate constant [k(ep)], early contrast uptake [ECU]), and descriptive patterns of tumour reduction. Thresholds for identifying response varied across studies. Definitions of response included pathologic complete response (pCR), near-pCR, and residual tumour with evidence of NAC effect (range of response 0-58%). Heterogeneity across MRI parameters and the outcome definition precluded statistical meta-analysis. Based on descriptive presentation of the data, sensitivity/specificity pairs for prediction of pathologic response were highest in studies measuring reductions in Ktrans (near-pCR), ECU (pCR, but not near-pCR) and tumour volume (pCR or near-pCR), at high thresholds (typically >50%); lower sensitivity/specificity pairs were evident in studies measuring reductions in uni- or bidimensional tumour size. However, limitations in study methodology and data reporting preclude definitive conclusions. Methods proposed to address these limitations include: statistical comparison between MRI parameters, and MRI vs other tests (particularly ultrasound and clinical examination); standardising MRI thresholds and pCR definitions; and reporting changes in NAC based on test results. Further studies adopting these methods are warranted.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22863284     DOI: 10.1016/j.breast.2012.07.006

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  69 in total

1.  Quantitative DCE-MRI for prediction of pathological complete response following neoadjuvant treatment for locally advanced breast cancer: the impact of breast cancer subtypes on the diagnostic accuracy.

Authors:  Stylianos Drisis; Thierry Metens; Michael Ignatiadis; Konstantinos Stathopoulos; Shih-Li Chao; Marc Lemort
Journal:  Eur Radiol       Date:  2015-08-27       Impact factor: 5.315

2.  Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment.

Authors:  Michael A Jacobs; Antonio C Wolff; Katarzyna J Macura; Vered Stearns; Ronald Ouwerkerk; Riham El Khouli; David A Bluemke; Richard Wahl
Journal:  J Natl Cancer Inst Monogr       Date:  2015-05

3.  AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2014.

Authors:  Cornelia Liedtke; Marc Thill; Volker Hanf; Florian Schütz
Journal:  Breast Care (Basel)       Date:  2014-07       Impact factor: 2.860

4.  Histogram analysis of apparent diffusion coefficients after neoadjuvant chemotherapy in breast cancer.

Authors:  Yun Ju Kim; Sung Hun Kim; Ah Won Lee; Min-Sun Jin; Bong Joo Kang; Byung Joo Song
Journal:  Jpn J Radiol       Date:  2016-08-12       Impact factor: 2.374

5.  Preoperative MRI improves prediction of extensive occult axillary lymph node metastases in breast cancer patients with a positive sentinel lymph node biopsy.

Authors:  Christopher Loiselle; Peter R Eby; Janice N Kim; Kristine E Calhoun; Kimberly H Allison; Vijayakrishna K Gadi; Sue Peacock; Barry E Storer; David A Mankoff; Savannah C Partridge; Constance D Lehman
Journal:  Acad Radiol       Date:  2014-01       Impact factor: 3.173

Review 6.  [Oncological imaging for therapy response assessment].

Authors:  J Stattaus
Journal:  Radiologe       Date:  2014-01       Impact factor: 0.635

7.  Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

Authors:  Liming Shi; Yang Zhang; Ke Nie; Xiaonan Sun; Tianye Niu; Ning Yue; Tiffany Kwong; Peter Chang; Daniel Chow; Jeon-Hor Chen; Min-Ying Su
Journal:  Magn Reson Imaging       Date:  2019-05-03       Impact factor: 2.546

8.  Combined use of ¹⁸F-FDG PET/CT and MRI for response monitoring of breast cancer during neoadjuvant chemotherapy.

Authors:  Kenneth E Pengel; Bas B Koolen; Claudette E Loo; Wouter V Vogel; Jelle Wesseling; Esther H Lips; Emiel J Th Rutgers; Renato A Valdés Olmos; Marie Jeanne T F D Vrancken Peeters; Sjoerd Rodenhuis; Kenneth G A Gilhuijs
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-04-29       Impact factor: 9.236

9.  A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results.

Authors:  Jacob U Fluckiger; Mary E Loveless; Stephanie L Barnes; Martin Lepage; Thomas E Yankeelov
Journal:  Phys Med Biol       Date:  2013-03-04       Impact factor: 3.609

Review 10.  Multiparametric MR Imaging of Breast Cancer.

Authors:  Habib Rahbar; Savannah C Partridge
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

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