Rationale and Objectives
Accurate identification of axillary lymph node (ALN) status in breast cancer patients
is important for determining treatment options and avoiding axillary overtreatments.
Our study aims to comprehensively compare the performance of the traditional radiomics
model, deep learning radiomics model, and the fusion models in evaluating breast cancer
ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI)
images.
Materials and Methods
The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI
images. The feature selection was performed by applying mutual information and feature
recursive elimination algorithms. The traditional radiomics model and deep learning
radiomics model were built using the optimal features and machine learning classifiers,
respectively. The fusion models for distinguishing axillary lymph node status were
constructed using two fusion strategies. The performance of the models with MRI-reported
lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also
compared.
Results
The decision fusion model, with the integration of the radiomics features and deep
learning features at the decision level, achieved an area under the curve (AUC) of
0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the
traditional radiomics model and deep learning radiomics model. The results of the
decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI:
0.899-0.951), which was also superior to other models incorporating clinical characteristic.
Conclusion
This study demonstrates the effectiveness of the fusion models for predicting axillary
lymph node metastasis in breast cancer.
Key Words
Abbreviations:
ALN (axillary lymph node), ALND (axillary lymph node dissection), CI (confidence interval), CNN (convolutional neural network), RFECV (recursive feature elimination and cross-validation), SLNB (sentinel lymph node biopsy)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 11, 2022
Accepted:
October 15,
2022
Received in revised form:
October 9,
2022
Received:
August 3,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
