Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer

doi:10.3877/cma.j.issn.1674-0807.2023.06.001

Abstract

Abstract:

In recent years, artificial intelligence, especially deep learning, has developed rapidly. It has become an effective method to capture the shape and texture of interested tissues from multi-modal images including X-ray, CT, MRI, positron emission tomography and ultrasound. This article comprehensively reviewed the application of deep learning in the early detection, accurate diagnosis, individualized treatment and prognosis prediction of breast cancer, based on the above-mentioned medical imaging methods, aiming to improve the clinical management of breast cancer patients and their prognosis. Finally, this paper summarized the current dilemma of this challenging research field and discussed its future prospect.

Key words: Breast neoplasms, Deep learning, Artificial intelligence, Medical imaging, Imaging omics

Wei Tang, Rongquan He, Suning Huang. Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 323-328.

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URL: https://zhrxbzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-0807.2023.06.001

https://zhrxbzz.cma-cmc.com.cn/EN/Y2023/V17/I06/323

References 43

[1]	Din NMU, Dar RA, Rasool M, Assad A, et al. Breast cancer detection using deep learning: datasets, methods, and challenges ahead[J]. Comput Biol Med, 2022, 149: 106073.
[2]	王浩滢. 深度学习及其发展趋势研究综述[J]. 电子制作，2021, 29(10): 92-95.
[3]	商亮，郭宇峰，叶伟，等. 人工智能在乳腺癌诊断中应用的研究进展[J]. 现代肿瘤医学，2021, 29(1): 155-158.
[4]	Nyante SJ, Marsh MW, Benefield T, et al. Supplemental breast imaging utilization after breast density legislation in North Carolina[J]. J Am Coll Radiol, 2020, 17(1 Pt A): 6-14.
[5]	Johnson K, Lång K, Ikeda DM, et al. Interval breast cancer rates and tumor characteristics in the prospective population-based malmö breast tomosynthesis screening trial[J]. Radiology, 2021, 299(3): 559-567.
[6]	Van Winkel SL, Rodríguez-ruiz A, Appelman L, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study[J]. Eur Radiol, 2021, 31(11): 8682-8691.
[7]	Lotter W, Diab AR, Haslam B, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach[J]. Nat Med, 2021, 27(2): 244-249.
[8]	Zhang X, Lin X, Zhang Z, et al. Artificial intelligence medical ultrasound equipment: application of breast lesions detection[J]. Ultrason Imaging, 2020, 42(4-5): 191-202.
[9]	Moon WK, Huang YS, Hsu CH, et al. Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network[J]. Comput Methods Programs Biomed, 2020, 190: 105360.
[10]	Wang K, Liang S, Zhong S, et al. Breast ultrasound image segmentation: a coarse-to-fine fusion convolutional neural network[J]. Med Phys, 2021, 48(8): 4262-4278.
[11]	Balkenende L, Teuwen J, Mann RM. Application of deep learning in breast cancer imaging[J]. Semin Nucl Med, 2022, 52(5): 584-596.
[12]	Hejduk P, Marcon M, Unkelbach J, et al. Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network[J]. Eur Radiol, 2022, 32(7): 4868-4878.
[13]	瞿微花，唐震. 深度学习在联合超声和钼靶检查乳腺癌中的应用[J]. 现代肿瘤医学，2019, 27(1): 144-149.
[14]	Li Y, Yang H, Zhang H, et al. Decode-seq: a practical approach to improve differential gene expression analysis[J]. Genome Biol, 2020, 21(1): 66.
[15]	Liu G, Mitra D, Jones EF, et al. Mask-guided convolutional neural network for breast tumor prognostic outcome prediction on 3D DCE-MR images[J]. J Digit Imaging, 2021, 34(3): 630-636.
[16]	Witowski J, Heacock L, Reig B, et al. Improving breast cancer diagnostics with deep learning for MRI[J]. Sci Transl Med, 2022, 14(664): eabo4802.
[17]	Al-antari MA, Al-masni MA, Kim TS. Deep learning computer-aided diagnosis for breast lesion in digital mammogram[J]. Adv Exp Med Biol, 2020, 1213: 59-72.
[18]	Samala RK, Chan HP, Hadjiiski LM, et al. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms[J]. Phys Med Biol, 2017, 62(23): 8894-8908.
[19]	Li X, Qin G, He Q, et al. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification[J]. Eur Radiol, 2020, 30(2): 778-788.
[20]	周洁洁，张洋，苏敏莹，等. 基于动态增强磁共振成像的影像组学和不同CNN的深度学习对乳腺良恶性病变的诊断价值[J]. 温州医科大学学报，2020, 50(6): 475-479.
[21]	Liu H, Chen Y, Zhang Y, et al. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening[J]. Eur Radiol, 2021, 31(8): 5902-5912.
[22]	潘德润，秦耿耿，陈卫国. 基于深度学习的人工智能技术在乳腺癌筛查及影像诊断中的应用进展[J]. 国际医学放射学杂志，2019, 42(1): 12-15.
[23]	Ma J, He N, Yoon JH, et al. Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search[J]. Eur J Radiol, 2021, 142: 109878.
[24]	Koh J, Yoon Y, Kim S, et al. Deep learning for the detection of breast cancers on chest computed tomography[J]. Clin Breast Cancer, 2022, 22(1): 26-31.
[25]	Caballo M, Pangallo DR, Mann RM, et al. Deep learning-based segmentation of breast masses in dedicated breast CT imaging: radiomic feature stability between radiologists and artificial intelligence[J]. Comput Biol Med, 2020, 118: 103629.
[26]	Mao N, Yin P, Li Q, et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study[J]. Eur Radiol, 2020, 30(12): 6732-6739.
[27]	Zhou J, Luo LY, Dou Q, et al. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images[J]. J Magn Reson Imaging, 2019, 50(4): 1144-1151.
[28]	陈志庚，李响，沙琳. 机器学习基于MRI预测乳腺癌对新辅助化疗反应的研究进展[J]. 磁共振成像，2021, 12(12): 102-104.
[29]	Jiang M, Li CL, Luo XM, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer[J]. Eur J Cancer, 2021, 147: 95-105.
[30]	Gu J, Tong T, He C, 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[J]. Eur Radiol, 2022, 32(3): 2099-2109.
[31]	Ye G, He S, Pan R, et al. Research on DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction[J]. J Healthc Eng, 2022, 2022: 4477099.
[32]	Ha R, Mutasa S, Karcich J, et al. Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm[J]. J Digit Imaging, 2019, 32(2): 276-282.
[33]	Guo X, Liu Z, Sun C, et al. Deep learning radiomics of ultrasonography: identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer[J]. EBioMedicine, 2020, 60: 103 018.
[34]	覃艳金，唐彩丽，胡奇兰，等. RSNA2021乳腺影像学[J]. 放射学实践，2022, 37(7): 802-805.
[35]	Yang X, Wu L, Ye W, et al. Deep learning signature based on staging CT for preoperative prediction of sentinel lymph node metastasis in breast cancer[J]. Acad Radiol, 2020, 27(9): 1226-1233.
[36]	杜雅楠，张光. 人工智能技术在乳腺影像诊断中的应用进展[J]. 医学影像学杂志，2021, 31(9): 1597-1599.
[37]	Ha R, Chin C, Karcich J, et al. Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset[J]. J Digit Imaging, 2019, 32(5): 693-701.
[38]	任晓丽. 基于深度学习的医学影像数据的瑕疵及对策[J]. 医疗装备，2022，35(3): 191-194.
[39]	Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection[J]. Genome Med, 2021, 13(1): 152.
[40]	Zhang W, Karagiannidis I, Van Vliet ES, et al. Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis[J]. Analyst, 2021, 146(20): 6124-6131.
[41]	Gutierrez-cardenas J, Wang Z. Classification of breast cancer and breast neoplasm scenarios based on machine learning and sequence features from lncRNAs-miRNAs-diseases associations[J]. Interdiscip Sci, 2021, 13(4): 572-581.
[42]	Pfob A, Mehrara BJ, Nelson JA, et al. Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)[J]. Breast, 2021, 60: 111-122.
[43]	钟雅婷，林艳梅，陈定甲，等. 多组学数据整合分析和应用研究综述[J]. 计算机工程与应用，2021, 57(30): 1-17.

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