Literature DB >> 25513854

Diagnostic Performance of Dedicated Axillary T2- and Diffusion-weighted MR Imaging for Nodal Staging in Breast Cancer.

Robert-Jan Schipper1, Marie-Louise Paiman, Regina G H Beets-Tan, Patricia J Nelemans, Bart de Vries, Esther M Heuts, Koen K van de Vijver, Kristien B Keymeulen, Boudewijn Brans, Marjolein L Smidt, Marc B I Lobbes.   

Abstract

PURPOSE: To evaluate the diagnostic performance of unenhanced axillary T2-weighted and diffusion-weighted (DW) magnetic resonance (MR) imaging for axillary nodal staging in patients with newly diagnosed breast cancer, with node-by-node and patient-by-patient validation.
MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained. Fifty women (mean age, 60 years; range, 22-80 years) underwent high-spatial-resolution axillary 3.0-T T2-weighted imaging without fat suppression and DW imaging (b = 0, 500, and 800 sec/mm(2)), followed by either sentinel lymph node biopsy (SLNB) or axillary lymph node dissection. Two radiologists independently scored each lymph node on a confidence level scale from 0 (benign) to 4 (malignant), first on T2-weighted MR images, then on DW MR images. Two researchers independently measured the mean apparent diffusion coefficient (ADC) of each lymph node. Diagnostic performance parameters were calculated on the basis of node-by-node and patient-by-patient validation.
RESULTS: With respective node-by-node and patient-by-patient validation, T2-weighted MR imaging had a specificity of 93%-97% and 87%-95%, sensitivity of 32%-55% and 50%-67%, negative predictive value (NPV) of 88%-91% and 86%-89%, positive predictive value (PPV) of 60%-70% and 62%-75%, and area under the receiver operating characteristic curve (AUC) of 0.78 and 0.80-0.88, with good interobserver agreement (κ = 0.70). The addition of DW MR imaging resulted in lower specificity (59%-88% and 50%-84%), higher sensitivity (45%-64% and 75%-83%), comparable NPV (89% and 90%-91%), lower PPV (23%-42% and 34%-60%), and lower AUC (0.68-0.73 and 0.70-0.86). ADC measurement resulted in a specificity of 63%-64% and 61%-63%, sensitivity of 41% and 67%, NPV of 85% and 85%-86%, PPV of 18% and 35%-36%, and AUC of 0.54-0.58 and 0.69-0.74, respectively, with excellent interobserver agreement (intraclass correlation coefficient, 0.83).
CONCLUSION: Dedicated high-spatial-resolution axillary T2-weighted MR imaging showed good specificity on the basis of node-by-node and patient-by-patient validation, with good interobserver agreement. However, its NPV is still insufficient to substitute it for SLNB for exclusion of axillary lymph node metastasis. DW MR imaging and ADC measurement were of no added value.

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Year:  2014        PMID: 25513854     DOI: 10.1148/radiol.14141167

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


  13 in total

1.  Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.

Authors:  Yuhao Dong; Qianjin Feng; Wei Yang; Zixiao Lu; Chunyan Deng; Lu Zhang; Zhouyang Lian; Jing Liu; Xiaoning Luo; Shufang Pei; Xiaokai Mo; Wenhui Huang; Changhong Liang; Bin Zhang; Shuixing Zhang
Journal:  Eur Radiol       Date:  2017-08-21       Impact factor: 5.315

2.  Can integrated 18F-FDG PET/MR replace sentinel lymph node resection in malignant melanoma?

Authors:  Benedikt Michael Schaarschmidt; Johannes Grueneisen; Vanessa Stebner; Joachim Klode; Ingo Stoffels; Lale Umutlu; Dirk Schadendorf; Philipp Heusch; Gerald Antoch; Thorsten Dirk Pöppel
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-06-06       Impact factor: 9.236

3.  Assessment of preoperative axillary nodal disease burden: breast MRI in locally advanced breast cancer before, during and after neoadjuvant endocrine therapy.

Authors:  Jürgen Geisler; Jonn Terje Geitung; Joana Reis; Joao Boavida; Hang T Tran; Marianne Lyngra; Laurens Cornelus Reitsma; Hossein Schandiz; Woldegabriel A Melles; Kjell-Inge Gjesdal
Journal:  BMC Cancer       Date:  2022-06-25       Impact factor: 4.638

4.  Fat fractions from high-resolution 3D radial Dixon MRI for predicting metastatic axillary lymph nodes in breast cancer patients.

Authors:  Thomas Winther Buus; Kim Sivesgaard; Tanja Linde Fris; Peer Michael Christiansen; Anders Bonde Jensen; Erik Morre Pedersen
Journal:  Eur J Radiol Open       Date:  2020-11-07

5.  Diagnostic Value of Diffusion Weighted Magnetic Resonance Imaging in Evaluation of Metastatic Axillary Lymph Nodes in a Sample of Iranian Women with Breast Cancer

Authors:  Fereshteh Hasanzadeh; Fariborz Faeghi; Abdollah Valizadeh; Leyla Bayani
Journal:  Asian Pac J Cancer Prev       Date:  2017-05-01

6.  Best MRI sequences for identifying axillary lymph node markers in patients with metastatic breast cancer: an inter-reader observational study.

Authors:  Naziya Samreen; Asha A Bhatt; Kalie Adler; Shannon Zingula; Katrina N Glazebrook
Journal:  Eur Radiol Exp       Date:  2020-06-12

7.  Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer.

Authors:  Jiao Li; Weimei Ma; Xinhua Jiang; Chunyan Cui; Hongli Wang; Jiewen Chen; Runcong Nie; Yaopan Wu; Li Li
Journal:  J Cancer       Date:  2019-01-29       Impact factor: 4.207

8.  MRI evaluation of axillary and intramammary lymph nodes in the postoperative period.

Authors:  Joao V Horvat; Elizabeth A Morris; Blanca Bernard-Davila; Danny F Martinez; Doris Leithner; Rosa Elena Ochoa-Albiztegui; Sunitha B Thakur; Katja Pinker
Journal:  Breast J       Date:  2019-06-07       Impact factor: 2.431

Review 9.  The Diagnosis of Metastatic Axillary Lymph Nodes of Breast Cancer By Diffusion Weighted Imaging: a meta-analysis and systematic review.

Authors:  Wei Fan Sui; Xiang Chen; Zhen Kun Peng; Jing Ye; Jing Tao Wu
Journal:  World J Surg Oncol       Date:  2016-06-02       Impact factor: 2.754

10.  Prediction of high nodal burden with ultrasound and magnetic resonance imaging in clinically node-negative breast cancer patients.

Authors:  Won Hwa Kim; Hye Jung Kim; So Mi Lee; Seung Hyun Cho; Kyung Min Shin; Sang Yub Lee; Jae Kwang Lim
Journal:  Cancer Imaging       Date:  2019-02-01       Impact factor: 3.909

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