Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer

Published:November 11, 2022DOI:

      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.


      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.


      This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.

      Key Words


      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)
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        • Langer I
        • Guller U
        • Berclaz G
        • et al.
        Morbidity of sentinel lymph node biopsy (SLN) alone versus SLN and completion axillary lymph node dissection after breast cancer surgery - A prospective Swiss multicenter study on 659 patients.
        Ann Surg. 2007; 245: 452-461
        • Kamath VJ
        • Giuliano R
        • Dauway EL
        • et al.
        Characteristics of the sentinel lymph node in breast cancer predict further involvement of higher-echelon nodes in the axilla - A study to evaluate the need for complete axillary lymph node dissection.
        Arch Surg. 2001; 136: 688-692
        • Giuliano AE
        • Ballman K
        • McCall L
        • et al.
        Locoregional recurrence after sentinel lymph node dissection with or without axillary dissection in patients with sentinel lymph node metastases long-term follow-up from the American College of Surgeons Oncology Group (Alliance) ACOSOG Z0011 randomized trial.
        Ann Surg. 2016; 264: 413-420
        • Galimberti V
        • Cole BF
        • Viale G
        • et al.
        Axillary dissection versus no axillary dissection in patients with breast cancer and sentinel-node micrometastases (IBCSG 23-01): 10-year follow-up of a randomized, controlled, phase 3 trial.
        Lancet Oncol. 2018; 19: 1385-1393
        • Lambin P
        • Rios-Velazquez E
        • Leijenaar R
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446
        • Samiei S
        • Granzier RWY
        • Ibrahim A
        • et al.
        Dedicated axillary MRI-based radiomics analysis for the prediction of axillary lymph node metastasis in breast cancer.
        Cancers. 2021; 13: 757
        • Yu FH
        • Wang JX
        • Ye XH
        • et al.
        Ultrasound-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer.
        Eur J Radiol. 2019; 119: 108658
        • Yu YF
        • He ZF
        • Ouyang J
        • et al.
        Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study.
        Ebiomedicine. 2021; 69: 103460
        • Tan HN
        • Gan FW
        • Wu YP
        • et al.
        Preoperative prediction of axillary lymph node metastasis in breast carcinoma using radiomics features based on the fat-suppressed T2 sequence.
        Acad Radiol. 2020; 27: 1217-1225
        • Islam MM
        • Huang SJ
        • Ajwad R
        • et al.
        An integrative deep learning framework for classifying molecular subtypes of breast cancer.
        Computat Struct Biotechnol J. 2020; 18: 2185-2199
        • Shin HC
        • Roth HR
        • Gao MC
        • et al.
        Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning.
        IEEE Trans Med Imaging. 2016; 35: 1285-1298
        • Truhn D
        • Schrading S
        • Haarburger C
        • et al.
        Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI.
        Radiology. 2019; 290: 290-297
        • Sun S
        • Mutasa S
        • Liu MZ
        • et al.
        Deep learning prediction of axillary lymph node status using ultrasound images.
        Comp Biol Med. 2022; 143105250
        • Zhou LQ
        • Wu XL
        • Huang SY
        • et al.
        Lymph node metastasis prediction from primary breast cancer US images using deep learning.
        Radiology. 2020; 294: 19-28
        • Sun Q
        • Lin X
        • Zhao Y
        • et al.
        Deep learning vs. Radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don’t forget the peritumoral region.
        Front Oncol. 2020; 10: 53
        • Chin YJ
        • Ong TS
        • Teoh ABJ
        • et al.
        Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion.
        Inf Fusion. 2014; 18: 161-174
        • Alexandre LA.
        Gender recognition: a multiscale decision fusion approach.
        Pattern Recognit Lett. 2010; 31: 1422-1427
        • Xie YT
        • Zhang JP
        • Xia Y
        • et al.
        Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT.
        Inf Fusion. 2018; 42: 102-110
        • Whitney HM
        • Li H
        • Ji Y
        • et al.
        Comparison of breast MRI tumor classification using human-engineered radiomics, transfer learning from deep convolutional neural networks, and fusion method.
        in: Proceedings of the IEEE. 108. 2020: 163-177
        • Clark K
        • Vendt B
        • Smith K
        • et al.
        The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.
        J Digit Imaging. 2013; 26: 1045-1057
        • Saha A
        • Harowicz MR
        • Grimm LJ
        • et al.
        A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.
        Br J Cancer. 2018; 119: 508-516
        • Simonyan K
        • Zisserman A.
        Very deep convolutional networks for large-scale image recognition.
        arXiv preprint arXiv. 2014; : 1409-1556
        • Wang ZJ
        • Sun H
        • Li J
        • et al.
        Preoperative prediction of axillary lymph node metastasis in breast cancer using CNN based on multiparametric MRI.
        J Magn Reson Imaging. 2022; 56: 700-709
        • Zheng XY
        • Yao Z
        • Huang YN
        • et al.
        Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.
        Nat Commun. 2020; 11: 1236
        • Liu ZY
        • Ni SJ
        • Yang CM
        • et al.
        Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning.
        Comp Biol Med. 2021; 136: 104715
        • Gao Y
        • Luo Y
        • Zhao C
        • et al.
        Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients.
        Eur Radiol. 2021; 31: 928-937
        • Lee YW
        • Huang CS
        • Shih CC
        • et al.
        Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks.
        Comp Biol Med. 2021; 130: 104206