Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment

doi:10.37015/AUDT.2025.240009

Abstract

Abstract:

Since 2020, breast cancer has held the highest incidence rate among cancers worldwide. Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. This review focuses on the application of convolutional neural networks (CNNs) within DL technology in the field of breast US. It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. The review also identifies the data limitations of using CNN models in breast US and describes the development history and current applications of DL in breast cancer screening, diagnostic guidance, and prognostic prediction. Furthermore, it discusses the future research directions and potential challenges. Advancing the development of CNN technology in breast US, and improving the generalizability and reproducibility of these models, will significantly promote their translational application in clinical settings.

Key words: Breast cancer; Ultrasonography; Computer neural networks

An Zichen, Li Fan. Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment. Advanced Ultrasound in Diagnosis and Therapy, 2025, 9(1): 21-31.

Figures/Tables 5

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

References 65

[1]	Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71:209-249.
[2]	Nothacker M, Duda V, Hahn M, Warm M, Degenhardt F, Madjar H, et al. Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review. BMC Cancer 2009;9:335.
[3]	M. Farkhadov, A. Eliseev, and N. Petukhova, Explained Artificial Intelligence Helps to Integrate Artificial and Human Intelligence Into Medical Diagnostic Systems: Analytical Review of Publications. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), Tashkent, Uzbekistan 2020:1-4.
[4]	He Q, Yang Q, Xie M. HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation. Comput Biol Med 2023;55:106629.
[5]	Cai L, Wang X, Wang Y, Guo Y, Yu J, Wang Y. Robust phase-based texture descriptor for classification of breast ultrasound images. Biomed Eng Online 2015;14:26.
[6]	You J, Huang Y, Ouyang L, Zhang X, Chen P, Wu X, et al. Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study. EClinicalMedicine 2024;69:102499.
[7]	Tagnamas J, Ramadan H, Yahyaouy A, Tairi H. Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images. Vis Comput Ind Biomed Art 2024;7:2.
[8]	Mo Y, Han C, Liu Y, Liu M, Shi Z, Lin J, et al. HoVer-Trans: Anatomy-aware HoVer-transformer for ROI-free breast cancer diagnosis in ultrasound images. IEEE Trans Med Imaging 2023;42:1696-1706.
[9]	Lin T, Wang Y, Liu X, Qiu X. A survey of transformers. AI open 2022;3:111-132.
[10]	Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]//Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13. Springer International Publishing, 2014: 818-833.
[11]	Jiang M, Zhang D, Tang SC, Luo XM, Chuan ZR, Lv WZ, et al. Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. Eur Radiol 2021;31:3673-3682.
[12]	Mendelson E B, Böhm-Vélez M, Berg W A, et al. Acr bi-rads® ultrasound. ACR BI-RADS® atlas, breast imaging reporting and data system 2013;149.
[13]	Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 2018;37:217-225.
[14]	Jamieson A R, Drukker K, Giger M L. Breast image feature learning with adaptive deconvolutional networks. Medical Imaging 2012;8315:64-76.
[15]	Wang G. A perspective on deep imaging. IEEE Access 2016;4:8914-8924.
[16]	Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 2016;6:24454.
[17]	Zhang Q, Xiao Y, Dai W, Suo J, Wang C, Shi J, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016;72:150-157.
[18]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[19]	Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition 2016:2818-2826.
[20]	He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016:770-778.
[21]	Chollet F. Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition 2017:1251-1258.
[22]	Nielsen Moody A, Bull J, Culpan AM, Munyombwe T, Sharma N, Whitaker M, et al. Preoperative sentinel lymph node identification, biopsy and localisation using contrast enhanced ultrasound (ceus) in patients with breast cancer: a systematic review and metaanalysis. Clin Radiol 2017;72:959-971.
[23]	Xiao T, Liu L, Li K, Qin W, Yu S, Li Z. Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed Res Int 2018;2018:4605191.
[24]	Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Arxiv, 2014:27.
[25]	Byra M, Galperin M, Ojeda-Fournier H, Olson L, O'Boyle M, Comstock C, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 2019;46:746-755.
[26]	Chen J, Huang Z, Jiang Y, Wu H, Tian H, Cui C, et al. Diagnostic performance of deep learning in video-based ultrasonography for breast cancer: A retrospective multicentre study. Ultrasound Med Biol 2024;50:722-728.
[27]	Cai Y, Du L, Li F, Gu J, Bai M. Quantification of enhancement of renal parenchymal masses with contrast-enhanced ultrasound. Ultrasound Med Biol 2014;40:1387-1393.
[28]	Xiachuan Q, Xiang Z, Xuebing L, Yan L. Predictive value of contrast-enhanced ultrasound for early recurrence of single lesion hepatocellular carcinoma after curative resection. Ultrason Imaging 2019;41:49-58.
[29]	Qian X, Zhang B, Liu S, Wang Y, Chen X, Liu J, et al. A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network. Eur Radiol 2020;30:3023-3033.
[30]	Liao WX, He P, Hao J, Wang XY, Yang RL, An D, et al. Automatic identification of breast ultrasound image based on supervised block-based region segmentation algorithm and features combination migration deep learning model. IEEE J Biomed Health Inform 2020;24:984-993.
[31]	Hoffmann R, Reich C, Skerl K. Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging. Int J Comput Assist Radiol Surg 2022;17:2231-2237.
[32]	Chen C, Wang Y, Niu J, Liu X, Li Q, Gong X. Domain knowledge powered deep Learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Trans Med Imaging 2021;40:2439-2451.
[33]	Wang C, Liu Z, Chen X, Qiao J, Lu Z, Li L, et al. Neoadjuvant camrelizumab plus nab-paclitaxel and epirubicin in early triple-negative breast cancer: a single-arm phase II trial. Nat Commun 2023;14:6654.
[34]	Burstein HJ, Curigliano G, Thürlimann B, Weber WP, Poortmans P, Regan MM, et al. Panelists of the St Gallen Consensus Conference. Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021;32:1216-1235.
[35]	Schaeffer EM, Srinivas S, Adra N, An Y, Barocas D, Bitting R, et al. NProstate cancer, version 4.2023, CCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2023;21:1067-1096.
[36]	Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical Oncology/College of American pathologists clinical practice guideline focused update. Arch Pathol Lab Med 2018;142:1364-1382.
[37]	Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, et al. Tailoring therapies-improving the management of early breast cancer: St gallen international expert consensus on the primary therapy of early breast cancer 2015. Ann Oncol 2015;26:1533-1546.
[38]	Chen J, Wang Z, Lv Q, Du Z, Tan Q, Zhang D, et al. Comparison of core needle biopsy and excision specimens for the accurate evaluation of breast cancer molecular markers:a report of 1003 cases. Pathol Oncol Res 2017;23:769-775.
[39]	Huang Y, Yao Z, Li L, Mao R, Huang W, Hu Z, et al. Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers. EBioMedicine 2023;94:104706.
[40]	Zhao B, Zhao H. Impact of clinicopathological characteristics on the efficacy of neoadjuvant therapy in patients with human epidermal growth factor receptor-2-positive breast cancer. Int J Cancer 2018;142:844-853.
[41]	Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68:394-424.
[42]	Presta M, Dell'Era P, Mitola S, Moroni E, Ronca R, Rusnati M. Fibroblast growth factor/fibroblast growth factor receptor system in angiogenesis. Cytokine Growth Factor Rev 2005;16:159-178.
[43]	Tran B, Bedard PL. Luminal-B breast cancer and novel therapeutic targets. Breast Cancer Res 2011;13:221.
[44]	Zhang X, Li H, Wang C, Cheng W, Zhu Y, Li D, et al. Evaluating the accuracy of breast cancer and molecular subtype diagnosis by ultrasound image deep learning model. Front Oncol 2021;11:623506.
[45]	Quan MY, Huang YX, Wang CY, Zhang Q, Chang C, Zhou SC. Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status. Front Endocrinol (Lausanne) 2023;14:1144812.
[46]	Zhou BY, Wang LF, Yin HH, Wu TF, Ren TT, Peng C, et al. Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study. EBioMedicine 2021;74:103684.
[47]	Giordano SH. Update on locally advanced breast cancer. Oncologist 2003;8:521-530.
[48]	Charfare H, Limongelli S, Purushotham AD. Neoadjuvant chemotherapy in breast cancer. Vol. Br J Surgery 2005;92:14-23.
[49]	Gu J, Tong T, He C, Xu M, Yang X, et al. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Eur Radiol 2022;32:2099-2109.
[50]	Gu YL, Pan SM, Ren J, Yang ZX, Jiang GQ. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: A meta-analys. Clin Breast Cancer 2017;17:245-255.
[51]	Di Cosimo S, Campbell C, Azim Jr. HA, et al. The use of breast imaging for predicting response to neoadjuvant lapatinib, trastuzumab and their combination in HER2-positive breast cancer: results from Neo-ALTTO. Eur J Cancer 2018;89:42-48.
[52]	Zhang J, Deng J, Huang J, Mei L, Liao N, Yao F, et al. Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model. Front Oncol 2024;14:1255618.
[53]	Liu Y, Wang Y, Wang Y, Xie Y, Cui Y, Feng S, et al. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study. EClinicalMedicine 2022;52:101562.
[54]	Huang JX, Shi J, Ding SS, Zhang HL, Wang XY, Lin SY, et al. Deep learning model based on dual-modal ultrasound and molecular data for predicting response to neoadjuvant chemotherapy in breast cancer. Acad Radiol 2023;30Suppl 2:S50-S61.
[55]	Gu J, Zhong X, Fang C, Lou W, Fu P, Woodruff HC, et al. Deep learning of multimodal ultrasound: Stratifying the response to neoadjuvant Chemotherapy in Breast Cancer Before Treatment. Oncologist 2024;29:e187-e197.
[56]	Wang H, Yang XW, Chen F, Qin YY, Li XB, Ma SM, et al. Non-invasive assessment of axillary lymph node metastasis risk in early invasive breast cancer adopting automated breast volume scanning-based radiomics nomogram: A multicenter study. Ultrasound Med Biol 2023;49:1202-1211.
[57]	Bae MS, Shin SU, Song SE, Ryu HS, Han W, Moon WK. Association between US features of primary tumor and axillary lymph node metastasis in patients with clinical T1-T2N0 breast cancer. Acta Radiol 2018;59:402-408.
[58]	Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning. Radiology 2020;294:19-28.
[59]	Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 2020;11:1236.
[60]	de Boer M, van Deurzen CH, van Dijck JA, Borm GF, van Diest PJ, Adang EM, et al. Micrometastases or isolated tumor cells and the outcome of breast cancer. N Engl J Med 2009;361:653-663.
[61]	Badve SS, Gökmen-Polar Y. Ductal carcinoma in situ of breast: update 2019. Pathology 2019;51:563-569.
[62]	Sagara Y, Mallory MA, Wong S, Aydogan F, DeSantis S, Barry WT, et al. Survival benefit of breast surgery for low-grade ductal carcinoma in situ: A population-based cohort study, JAMa Surg 2015;150:739-745.
[63]	Wu H, Jiang Y, Tian H, Ye X, Cui C, Shi S, et al. Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ. Comput Methods Programs Biomed 2024;245:108039.
[64]	Han J, Hua H, Fei J, Liu J, Guo Y, Ma W, et al. Prediction of disease-free survival in breast cancer using deep learning with ultrasound and mammography: A multicenter study. Clin Breast Cancer 2024;24:215-226.
[65]	Chowdary J, Yogarajah P, Chaurasia P, Guruviah V. A multi-task learning framework for automated segmentation and classification of breast tumors from ultrasound images. Ultrason Imaging 2022;44:3-12.