Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images

被引:0
作者
Liu, Menghan [1 ,2 ]
Zhang, Shuai [3 ,4 ]
Du, Yanan [1 ,2 ]
Zhang, Xiaodong [4 ]
Wang, Dawei [5 ]
Ren, Wanqing [4 ]
Sun, Jingxiang [4 ]
Yang, Shiwei [6 ]
Zhang, Guang [1 ,2 ]
机构
[1] Shandong First Med Univ, Dept Hlth Management, Affiliated Hosp 1, Jinan, Peoples R China
[2] Shandong Prov Qianfoshan Hosp, Shandong Engn Lab Hlth Management, Shandong Med & Hlth Key Lab Lab Med, Jinan, Peoples R China
[3] Shandong First Med Univ, Shandong Prov Hosp, Dept Radiol, Jinan, Peoples R China
[4] Shandong First Med Univ, Shandong Acad Med Sci, Postgrad Dept, Jinan, Peoples R China
[5] Shandong First Med Univ, Dept Radiol, Affiliated Hosp 1, Jinan, Peoples R China
[6] Shandong First Med Univ, Dept Anorectal Surg, Affiliated Hosp 1, Jinan, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2023年 / 13卷
关键词
molecular subtype; breast cancer; multi-modality; deep learning; mammography; MRI; MOLECULAR SUBTYPES; NEURAL-NETWORK; CLASSIFICATION; DIAGNOSIS; MRI;
D O I
10.3389/fonc.2023.1243126
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
PurposeTo evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer.Materials and methodsA total of 158 breast cancer patients (170 lesions, median age, 50.8 +/- 11.0 years), including 78 Luminal A subtype and 92 non-Luminal A subtype lesions, were retrospectively analyzed and divided into a training set (n = 100), test set (n = 45), and validation set (n = 25). Mammography (MG) and magnetic resonance imaging (MRI) images were used. Five single-mode models, i.e., MG, T2-weighted imaging (T2WI), diffusion weighting imaging (DWI), axial apparent dispersion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), were selected. The deep learning network ResNet50 was used as the basic feature extraction and classification network to construct the molecular subtype identification model. The receiver operating characteristic curve were used to evaluate the prediction efficiency of each model.ResultsThe accuracy, sensitivity and specificity of a multi-modal tool for identifying Luminal A subtype were 0.711, 0.889, and 0.593, respectively, and the area under the curve (AUC) was 0.802 (95% CI, 0.657- 0.906); the accuracy, sensitivity, and AUC were higher than those of any single-modal model, but the specificity was slightly lower than that of DCE-MRI model. The AUC value of MG, T2WI, DWI, ADC, and DCE-MRI model was 0.593 (95%CI, 0.436-0.737), 0.700 (95%CI, 0.545-0.827), 0.564 (95%CI, 0.408-0.711), 0.679 (95%CI, 0.523-0.810), and 0.553 (95%CI, 0.398-0.702), respectively.ConclusionThe combination of deep learning and multi-modal imaging is of great significance for diagnosing breast cancer subtypes and selecting personalized treatment plans for doctors.
引用
收藏
页数:11
相关论文
共 31 条
[1]   Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review [J].
Ab Mumin, Nazimah ;
Hamid, Marlina Tanty Ramli ;
Wong, Jeannie Hsiu Ding ;
Rahmat, Kartini ;
Ng, Kwan Hoong .
ACADEMIC RADIOLOGY, 2022, 29 :S89-S106
[2]   A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms [J].
Al-Tam, Riyadh M. ;
Al-Hejri, Aymen M. ;
Narangale, Sachin M. ;
Samee, Nagwan Abdel ;
Mahmoud, Noha F. ;
Al-masni, Mohammed A. ;
Al-antari, Mugahed A. .
BIOMEDICINES, 2022, 10 (11)
[3]   Mammography screening: A major issue in medicine [J].
Autier, Philippe ;
Boniol, Mathieu .
EUROPEAN JOURNAL OF CANCER, 2018, 90 :34-62
[4]   Synthetic MRI with quantitative mappings for identifying receptor status, proliferation rate, and molecular subtypes of breast cancer [J].
Gao, Weibo ;
Yang, Quanxin ;
Li, Xiaohui ;
Chen, Xin ;
Wei, Xiaocheng ;
Diao, Yan ;
Zhang, Yanyan ;
Chen, Chunni ;
Guo, Baobin ;
Wang, Youren ;
Lei, Zhe ;
Zhang, Shuqun .
EUROPEAN JOURNAL OF RADIOLOGY, 2022, 148
[5]   Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013 [J].
Goldhirsch, A. ;
Winer, E. P. ;
Coates, A. S. ;
Gelber, R. D. ;
Piccart-Gebhart, M. ;
Thuerlimann, B. ;
Senn, H. -J. .
ANNALS OF ONCOLOGY, 2013, 24 (09) :2206-2223
[6]   Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm [J].
Ha, Richard ;
Mutasa, Simukayi ;
Karcich, Jenika ;
Gupta, Nishant ;
Van Sant, Eduardo Pascual ;
Nemer, John ;
Sun, Mary ;
Chang, Peter ;
Liu, Michael Z. ;
Jambawalikar, Sachin .
JOURNAL OF DIGITAL IMAGING, 2019, 32 (02) :276-282
[7]  
Hadad O, 2017, I S BIOMED IMAGING, P109, DOI 10.1109/ISBI.2017.7950480
[8]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[9]   Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer [J].
Huang, Yuhong ;
Wei, Lihong ;
Hu, Yalan ;
Shao, Nan ;
Lin, Yingyu ;
He, Shaofu ;
Shi, Huijuan ;
Zhang, Xiaoling ;
Lin, Ying .
FRONTIERS IN ONCOLOGY, 2021, 11
[10]   ImageNet Classification with Deep Convolutional Neural Networks [J].
Krizhevsky, Alex ;
Sutskever, Ilya ;
Hinton, Geoffrey E. .
COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90