Advertisement

Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy

      Abstract

      Background

      Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Current radiological staging of nodal metastasis has poor accuracy. This study aimed to investigate the machine learning convolutional neural networks (CNNs) on multiparametric MRI to detect nodal metastasis with 18FDG-PET as ground truths.

      Materials and Methods

      Data were obtained via a retrospective search. Inclusion criteria were patients with bilateral breast MRI and 18FDG-PETand/or CT scans obtained before neoadjuvant chemotherapy. In total, 238 aLNs were obtained from 56 breast cancer patients with 18FDG-PET and/or CT and breast MRI data. Radiologists scored each node based on all MRI as diseased and non–diseased nodes. Five models were built using T1-W MRI, T2-W MRI, DCE MRI, T1-W + T2-W MRI, and DCE + T2-W MRI model. Performance was evaluated using receiver operating curve (ROC) analysis, including area under the curve (AUC).

      Results

      All CNN models yielded similar performance with an accuracy ranging from 86.08% to 88.50% and AUC ranging from 0.804 to 0.882. The CNN model using T1-W MRI performed better than that using T2-W MRI in detecting nodal metastasis. CNN model using combined T1- and T2-W MRI performed the best compared to all other models (accuracy = 88.50%, AUC = 0.882), but similar in AUC to the DCE + T2-W MRI model (accuracy = 88.02%, AUC = 0.880). All CNN models performed better than radiologists in detecting nodal metastasis (accuracy = 65.8%).

      Conclusion

      xxxxxx

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Breast Cancer
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      REFERENCES

        • Beenken S.W.
        • Urist M.M.
        • Zhang Y.
        • et al.
        Axillary lymph node status, but not tumor size, predicts locoregional recurrence and overall survival after mastectomy for breast cancer.
        Ann Surg. 2003; 237 (discussion 738-9): 732-738
        • Chang J.M.
        • Leung J.W.T.
        • Moy L.
        • et al.
        Axillary Nodal Evaluation in Breast Cancer: State of the Art.
        Radiology. 2020; 295: 500-515
        • Carter C.L.
        • Allen C.
        • Henson D.E.
        Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases.
        Cancer. 1989; 63: 181-187
        • Hwang S.O.
        • Lee S.W.
        • Kim H.J.
        • et al.
        The Comparative Study of Ultrasonography, Contrast-Enhanced MRI, and (18)F-FDG PET/CT for Detecting Axillary Lymph Node Metastasis in T1 Breast Cancer.
        J Breast Cancer. 2013; 16: 315-321
        • Rahman M.
        • Mohammed S.
        Breast cancer metastasis and the lymphatic system.
        Oncol Lett. 2015; 10: 1233-1239
        • Ahmed M.
        • Usiskin S.I.
        • Hall-Craggs M.A.
        • et al.
        Is imaging the future of axillary staging in breast cancer?.
        Eur Radiol. 2014; 24: 288-293
        • Anzai Y.
        • Piccoli C.W.
        • Outwater E.K.
        • et al.
        Evaluation of neck and body metastases to nodes with ferumoxtran 10-enhanced MR imaging: phase III safety and efficacy study.
        Radiology. 2003; 228: 777-788
        • Chen M.Y.
        • Gillanders W.E.
        Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound.
        Breast Cancer (Dove Med Press). 2021; 13: 311-323
        • Choi S.H.
        • Moon W.K.
        Contrast-enhanced MR imaging of lymph nodes in cancer patients.
        Korean J Radiol. 2010; 11: 383-394
        • Zhou P.
        • Wei Y.
        • Chen G.
        • et al.
        Axillary lymph node metastasis detection by magnetic resonance imaging in patients with breast cancer: A meta-analysis.
        Thorac Cancer. 2018; 9: 989-996
        • Murray A.D.
        • Staff R.T.
        • Redpath T.W.
        • et al.
        Dynamic contrast enhanced MRI of the axilla in women with breast cancer: comparison with pathology of excised nodes.
        Br J Radiol. 2002; 75: 220-228
        • Kvistad K.A.
        • Rydland J.
        • Smethurst H.B.
        • et al.
        Axillary lymph node metastases in breast cancer: preoperative detection with dynamic contrast-enhanced MRI.
        Eur Radiol. 2000; 10: 1464-1471
        • Kim S.H.
        • Shin H.J.
        • Shin K.C.
        • et al.
        Diagnostic Performance of Fused Diffusion-Weighted Imaging Using T1-Weighted Imaging for Axillary Nodal Staging in Patients With Early Breast Cancer.
        Clin Breast Cancer. 2017; 17: 154-163
        • Samiei S.
        • van Nijnatten T.J.A.
        • van Beek H.C.
        • et al.
        Diagnostic performance of axillary ultrasound and standard breast MRI for differentiation between limited and advanced axillary nodal disease in clinically node-positive breast cancer patients.
        Sci Rep. 2019; 9: 17476
        • Deo R.C.
        Machine Learning in Medicine.
        Circulation. 2015; 132: 1920-1930
        • Santos M.K.
        • Ferreria Junior J.R.
        • Wada D.T.
        • et al.
        Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine.
        Radiol Bras. 2019; 52: 387-396
        • Tschandl P.
        • Codella N.
        • Akay B.N.
        • et al.
        Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.
        Lancet Oncol. 2019; 20: 938-947
        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444
        • Harris M.
        • Qi A.
        • Jeagal L.
        • et al.
        A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis.
        PLoS One. 2019; 14e0221339
        • Killock D.
        AI outperforms radiologists in mammographic screening.
        Nat Rev Clin Oncol. 2020; 17: 134
        • Ren T.
        • Cattell R.
        • Duanmu H.
        • et al.
        Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.
        Clin Breast Cancer. 2020; 20: e301-e308
        • Ha R.
        • Chang P.
        • Karcich J.
        Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.
        J Digit Imaging. 2018; 31: 851-856
        • He N.
        • Xie C.
        • Wei W.
        • et al.
        A new, preoperative, MRI-based scoring system for diagnosing malignant axillary lymph nodes in women evaluated for breast cancer.
        European Journal of Radiology. 2012; 81: 2602-2612
        • Schipper R.J.
        • Paiman M.L.
        • Beets-Tan R.G.H.
        • et al.
        Diagnostic Performance of Dedicated Axillary T2- and Diffusion-weighted MR Imaging for Nodal Staging in Breast Cancer.
        Radiology. 2015; 275: 345-355
        • Choi E.J.
        • Youk J.H.
        • Choi H.
        • et al.
        Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status.
        J Magn Reson Imaging. 2020; 51: 615-626
        • Dong X.
        • Chungrong Y.
        • Hongjun H.
        • et al.
        Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging.
        BJR Open. 2019; 120180023
        • Wahl R.L.
        • Siegal B.A.
        • Coleman R.E.
        • et al.
        Prospective multicenter study of axillary nodal staging by positron emission tomography in breast cancer: a report of the staging breast cancer with PET Study Group.
        J Clin Oncol. 2004; 22: 277-285
        • Groves A.M.
        • Shastry M.
        • Ben-Haim S.
        • et al.
        Defining the role of PET-CT in staging early breast cancer.
        Oncologist. 2012; 17: 613-619
        • Sasada S.
        • Masumoto N.
        • Kimura Y.
        • et al.
        Identification of Axillary Lymph Node Metastasis in Patients With Breast Cancer Using Dual-Phase FDG PET/CT.
        AJR Am J Roentgenol. 2019; 213: 1129-1135
        • Minn H.
        • Soini I.
        [18F]fluorodeoxyglucose scintigraphy in diagnosis and follow up of treatment in advanced breast cancer.
        Eur J Nucl Med. 1989; 15: 61-66
        • Chung A.
        • Liou D.
        • Karlan S.
        • et al.
        Preoperative FDG-PET for axillary metastases in patients with breast cancer.
        Arch Surg. 2006; 141 (discussion 788-9): 783-788
        • Piva R.
        • Ticconi F.
        • Ceriani V.
        • et al.
        Comparative diagnostic accuracy of 18F-FDG PET/CT for breast cancer recurrence.
        Breast Cancer (Dove Med Press). 2017; : 461-471
        • Hong S.
        • Li J.
        • Wang S.
        18FDG PET-CT for diagnosis of distant metastases in breast cancer patients. A meta-analysis.
        Surg Oncol. 2013; 22: 139-143
        • Cooper K.L.
        • Harnan S.
        • Meng Y.
        • et al.
        Positron emission tomography (PET) for assessment of axillary lymph node status in early breast cancer: A systematic review and meta-analysis.
        Eur J Surg Oncol. 2011; 37: 187-198
        • Liang X.
        • Yu J.
        • Wen B.
        • et al.
        MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis.
        Clin Radiol. 2017; 72: 295-301
        • Maaskant-Braat A.J.
        • van de Poll-Franse L.V.
        • Voogd A.C.
        • et al.
        Sentinel node micrometastases in breast cancer do not affect prognosis: a population-based study.
        Breast Cancer Res Treat. 2011; 127: 195-203
        • Rampasek L.
        • Goldenberg A.
        TensorFlow: Biology's Gateway to Deep Learning?.
        Cell Syst. 2016; 2: 12-14
        • Medina G.
        • Buckless C.G.
        • Thomasson E.
        • et al.
        Deep learning method for segmentation of rotator cuff muscles on MR images.
        Skeletal Radiol. 2020;
        • Tang Y.
        Deep learning using linear support vector machines.
        Workshop on Representational Learning. 2013; 1306 (arXiv)
        • Ioffe S.
        • Szegedy C.
        Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
        in: Proceedings of the 32nd International Conference on Machine Learning, PMLR. 37. 2015: 448-456
        • Wu H.
        • Gu X.
        Max pooling dropout for regularization of convolutional neural networks.
        in: Proceedings of the 22nd International Conference Neural Information Processing. 2015: 46-53
        • van Heijst T.C.
        • van Asselen B.
        • Pijnappel R.M.
        • et al.
        MRI sequences for the detection of individual lymph nodes in regional breast radiotherapy planning.
        Br J Radiol. 2016; 8920160072
        • Liu C.
        • Ding D.
        • Spuhler K.
        • et al.
        Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.
        J Magn Reson Imaging. 2019; 49: 131-140
        • Chai R.
        • Ma H.
        • Xu M.
        • et al.
        Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences.
        J Magn Reson Imaging. 2019; 50: 1125-1132
        • Hawkins D.M.
        The problem of overfitting.
        J Chem Inf Comput Sci. 2004; 44: 1-12
        • Sacre R.A.
        Clinical evaluation of axillar lymph nodes compared to surgical and pathological findings.
        Eur J Surg Oncol. 1986; 12: 169-173
        • Zahoor S.
        • Haji A.
        • Battoo A.
        • et al.
        Sentinel Lymph Node Biopsy in Breast Cancer: A Clinical Review and Update.
        J Breast Cancer. 2017; 20: 217-227
        • Zhou L.Q.
        • Wu X.L.
        • Huang S.Y.
        • et al.
        Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.
        Radiology. 2020; 294: 19-28
        • Abe H.
        • Schacht D.
        • Kulkarni K.
        • et al.
        Accuracy of axillary lymph node staging in breast cancer patients: an observer-performance study comparison of MRI and ultrasound.
        Acad Radiol. 2013; 20: 1399-1404
        • He N.
        • Xie C.
        • Wei W.
        • et al.
        A new, preoperative, MRI-based scoring system for diagnosing malignant axillary lymph nodes in women evaluated for breast cancer.
        Eur J Radiol. 2012; 81: 2602-2612
        • van Nijnatten T.J.A.
        • Schipper R.J.
        • Lobbes M.B.J.
        • et al.
        Diagnostic performance of gadofosveset-enhanced axillary MRI for nodal (re)staging in breast cancer patients: results of a validation study.
        Clin Radiol. 2018; 73: 168-175
        • Cattell RF
        • Kang JJ
        • Ren T
        • et al.
        MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer.
        Clin Breast Cancer. 2020; 20 (e61): 68-79
        • Hussain L
        • Huang P
        • Nguyen T
        • et al.
        Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.
        Biomed Eng Online. 2021; 20: 63
        • Duanmu H
        • Huang P.B.
        • Brahmavar S
        Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data.
        Medical Image Computing and Computer Assisted Intervention (MICCAI). 2020; : 242-252