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中华肝脏外科手术学电子杂志 ›› 2023, Vol. 12 ›› Issue (05) : 480 -485. doi: 10.3877/cma.j.issn.2095-3232.2023.05.002

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深度学习神经网络在肝癌诊疗中的研究及应用前景
韩冰, 顾劲扬()   
  1. 210008 南京大学医学院附属鼓楼医院肝胆与肝移植外科
    430022 武汉,华中科技大学同济医学院附属协和医院肝脏移植中心
  • 收稿日期:2023-07-11 出版日期:2023-10-10
  • 通信作者: 顾劲扬
  • 基金资助:
    国家自然科学基金重点项目(82130020); 国家自然科学基金面上项目(82072645); 上海市科学技术委员会"科技创新行动计划"国内科技合作项目(21015801600); 促进市级医院临床技能与临床创新能力三年行动计划重大临床研究项目(SHDC2020CR3005A); 上海市教育委员会高峰高原人才计划(20191910)

Research and application prospect of deep learning neural network in diagnosis and treatments for liver cancer

Bing Han, Jinyang Gu()   

  • Received:2023-07-11 Published:2023-10-10
  • Corresponding author: Jinyang Gu
引用本文:

韩冰, 顾劲扬. 深度学习神经网络在肝癌诊疗中的研究及应用前景[J]. 中华肝脏外科手术学电子杂志, 2023, 12(05): 480-485.

Bing Han, Jinyang Gu. Research and application prospect of deep learning neural network in diagnosis and treatments for liver cancer[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 480-485.

原发性肝癌(肝癌)是2020年全球第五大最常见的癌症和第二大癌症相关死亡原因[1]。2003~2015年肝癌是我国第三常见的恶性肿瘤,5年生存率12.1%,在所有侵袭性癌症中排名第二[2]。可以说肝癌一直是困扰我国国民健康的重大健康问题,随着医疗设备、药品开发和治疗技术的飞跃,数十年内肝癌的在诊断和治疗上经历了一个又一个突破性进展。然而,数千年来,以一位医师或几位医师组成的多学科团队依据其理论基础和既往经验综合分析患者的临床数据进行疾病诊断和治疗为核心的医疗模式从未改变。近年来,人工智能(artificial intelligence,AI)的快速发展逐渐显示出对这一核心医疗模式的重大挑战。特别是深度学习(deep learning,DL)的出现,尤其凭借其在图像处理方面显示的独特优势,可能会在医学领域带来划时代的变化。

[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3):209-249.
[2]
Zeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries[J]. Lancet Glob Health, 2018, 6(5):e555-567.
[3]
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943[J]. Bull Math Biol, 1990, 52(1/2):99-115.
[4]
LeCun Y, Bengio Y. Convolutional networks for images, speech, and time-series[M]. Cambridge: MIT Press, 1995.
[5]
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[6]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks[J]. Commun Acm, 2017, 60(6): 84-90.
[7]
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22):2402-2410.
[8]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639):115-118.
[9]
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318(22):2199-2210.
[10]
Chu LC, Park S, Kawamoto S, et al. Current status of radiomics and deep learning in liver imaging[J]. J Comput Assist Tomogr, 2021, 45(3):343-351.
[11]
Feng B, Ma XH, Wang S, et al. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: current status and future perspectives[J]. World J Gastroenterol, 2021, 27(32): 5341-5350.
[12]
Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study[J]. Radiology, 2018, 286(3):887-896.
[13]
Gao R, Zhao S, Aishanjiang K, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data[J]. J Hematol Oncol, 2021, 14(1):154.
[14]
Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis partⅠ: development of a convolutional neural network classifier for multi-phasic MRI[J]. Eur Radiol, 2019, 29(7):3338-3347.
[15]
Trivizakis E, Manikis GC, Nikiforaki K, et al. Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to mri liver tumor differentiation[J]. IEEE J Biomed Health Inform, 2019, 23(3):923-930.
[16]
Yang Q, Wei J, Hao X, et al. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning:a multicentre study[J]. EBioMedicine, 2020(56):102777.
[17]
Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters[J]. J Cancer Res Clin Oncol, 2021, 147(12):3757-3767.
[18]
Tian Y, Komolafe TE, Zheng J, et al. Assessing PD-L1 expression level via preoperative MRI in HCC based on integrating deep learning and radiomics features[J]. Diagnostics, 2021, 11(10):1875.
[19]
Liu F, Liu D, Wang K, et al. Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients[J]. Liver Cancer, 2020, 9(4):397-413.
[20]
Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology[J]. Nat Rev Clin Oncol, 2019, 16(11):703-715.
[21]
Falk T, Mai D, Bensch R, et al. U-Net: deep learning for cell counting, detection, and morphometry[J]. Nat Methods, 2019, 16(1): 67-70.
[22]
Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18(2):203-211.
[23]
Schau GF, Burlingame EA, Thibault G, et al. Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin[J]. J Med Imaging, 2020, 7(1):012706.
[24]
Sun L, Zhou M, Li Q, et al. Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks[J]. Methods, 2021(202):22-30.
[25]
Roy M, Kong J, Kashyap S, et al. Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images[J]. Sci Rep, 2021, 11(1):139.
[26]
Kiani A, Uyumazturk B, Rajpurkar P, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer[J]. NPJ Digit Med, 2020(3):23.
[27]
Shi JY, Wang X, Ding GY, et al. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning[J]. Gut, 2021, 70(5):951-961.
[28]
Cheng N, Ren Y, Zhou J, et al. Deep learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images[J]. Gastroenterology, 2022, 162(7):1948-1961, e7.
[29]
Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies:a diagnostic study[J]. Lancet Oncol, 2020, 21(2):233-241.
[30]
Kong J, Sertel O, Shimada H, et al. Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation[J]. Pattern Recognit, 2009, 42(6):1080-1092.
[31]
Lin H, Wei C, Wang G, et al. Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning[J]. J Biophotonics, 2019, 12(7): e201800435.
[32]
Chen M, Zhang B, Topatana W, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning[J]. NPJ Precis Oncol, 2020(4):14.
[33]
Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J]. Nat Med, 2018, 24(10):1559-1567.
[34]
Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study[J]. Lancet Oncol, 2021, 22(1):132-141.
[35]
Jackson CR, Sriharan A, Vaickus LJ. A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms[J]. Mod Pathol, 2020, 33(9):1638-1648.
[36]
Zhang H, Kalirai H, Acha-Sagredo A, et al. Piloting a deep learning model for predicting nuclear bap1 immunohistochemical expression of uveal melanoma from hematoxylin-and-eosin sections[J]. Transl Vis Sci Technol, 2020, 9(2):50.
[37]
Gonzalez Y, Shen C, Jung H, et al. Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach[J]. Med Image Anal, 2021(68):101896.
[38]
Kim N, Chun J, Chang JS, et al. Feasibility of continual deep learning-based segmentation for personalized adaptive radiation therapy in head and neck area[J]. Cancers, 2021, 13(4):702.
[39]
Hayakawa S, Suzuki T. On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces[J]. Neural Netw, 2020(123):343-361.
[40]
Audureau E, Carrat F, Layese R, et al. Personalized surveillance for hepatocellular carcinoma in cirrhosis-using machine learning adapted to HCV status[J]. J Hepatol, 2020, 73(6):1434-1445.
[41]
Kim HY, Lampertico P, Nam JY, et al. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B[J]. J Hepatol, 2022, 76(2):311-318.
[42]
Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer[J]. Clin Cancer Res, 2018, 24(6):1248-1259.
[43]
Liao H, Xiong T, Peng J, et al. Classification and prognosis prediction from histopathological images of hepatocellular carcinoma by a fully automated pipeline based on machine learning[J]. Ann Surg Oncol, 2020, 27(7):2359-2369.
[44]
Wang H, Jiang Y, Li B, et al. Single-cell spatial analysis of tumor and immune microenvironment on whole-slide image reveals hepatocellular carcinoma subtypes[J]. Cancers, 2020, 12(12):3562.
[45]
Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides[J]. Hepatology, 2020, 72(6):2000-2013.
[46]
Yamashita R, Long J, Saleem A, et al. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images[J]. Sci Rep, 2021, 11(1):2047.
[47]
Nam JY, Lee JH, Bae J, et al. Novel model to predict hcc recurrence after liver transplantation obtained using deep learning: a multicenter study[J]. Cancers, 12(10):2791.
[48]
Ahn JC, Qureshi TA, Singal AG, et al. Deep learning in hepatocellular carcinoma: current status and future perspectives[J]. World J Hepatol, 2021, 13(12):2039-2051.
[49]
Hosseini SA, Jamshidnezhad A, Zilaee M, et al. Neural network-based clinical prediction system for identifying the clinical effects of saffron (crocus sativus L) supplement therapy on allergic asthma: model evaluation study[J]. JMIR Med Inform, 2020, 8(7):e17580.
[50]
Sakellaropoulos T, Vougas K, Narang S, et al. A deep learning framework for predicting response to therapy in cancer[J]. Cell Rep, 2019, 29(11):3367-3373, e4.
[51]
Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy[J]. Ann Surg, 2020, 276(2):363-369.
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