Literature DB >> 29029766

Diagnostic performance of gadofosveset-enhanced axillary MRI for nodal (re)staging in breast cancer patients: results of a validation study.

T J A van Nijnatten1, R J Schipper2, M B I Lobbes3, L M van Roozendaal4, S Vöö3, M Moossdorff5, M-L Paiman3, B de Vries6, K B M I Keymeulen7, J E Wildberger3, M L Smidt5, R G H Beets-Tan8.   

Abstract

AIM: To evaluate diagnostic performance of gadofosveset (GDF)-enhanced magnetic resonance imaging (MRI) in addition to T2-weighted (T2W) MRI for nodal (re)staging in newly diagnosed breast cancer patients.
MATERIALS AND METHODS: Ninety patients underwent axillary T2W- and GDF-MRI. Two radiologists independently scored each lymph node; first on T2W-MRI, subsequently adjusting their score on GDF-MRI. Diagnostic performance parameters were calculated on node-by-node and patient-by-patient validation with histopathology as the reference standard. Furthermore, learning curve analysis for reading GDF-MRI was performed.
RESULTS: In patient-by-patient validation, overall reader performances for T2W- and GDF-MRI were similar with area under the receiver operating characteristic curves (AUC) of 0.75 and 0.77 (p=0.731) for reader 1 and 0.79 and 0.72 (p=0.156) for reader 2. For node-by-node validation, AUC values of T2W- and GDF-MRI were 0.76 and 0.82 (p=0.018) and 0.77 and 0.77 (p=0.998) for reader 1 and 2. The AUC for reader 1 was 0.71 for first one-third of nodes evaluated, improving to 0.80 and 0.95 for the next and last one-third, respectively. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) improved from 38%, 89%, 56%, and 79% to 60%, 93%, 64%, and 92%. The AUC of reader 2 improved from 0.69 to 0.79.
CONCLUSION: The present study confirmed that GDF-MRI, in addition to T2W-MRI, has potential as a non-invasive method for nodal (re)staging in breast cancer.
Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2017        PMID: 29029766     DOI: 10.1016/j.crad.2017.09.005

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  1 in total

1.  Preoperative prediction of lymph node metastasis using deep learning-based features.

Authors:  Renee Cattell; Jia Ying; Lan Lei; Jie Ding; Shenglan Chen; Mario Serrano Sosa; Chuan Huang
Journal:  Vis Comput Ind Biomed Art       Date:  2022-03-07
  1 in total

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