深度学习在原发性肝癌相关诊断模型中的应用与前景

张清华; 李海涛; 方国旭; 郭鹏飞; 刘景丰

doi:10.3969/j.issn.1001-5256.2022.01.003

深度学习在原发性肝癌相关诊断模型中的应用与前景

DOI: 10.3969/j.issn.1001-5256.2022.01.003

张清华¹,
李海涛^{2a, 2b, ,},
方国旭^{2a, 2b},
郭鹏飞^2b,
刘景丰^{2b, 3, ,}

1.
福建医科大学研究生院, 福州350108
2a.
福建医科大学孟超肝胆医院肝胆胰外科, 福州 350025
2b.
福建医科大学孟超肝胆医院东南肝胆健康大数据研究所, 福州 350025
3.
福建省肿瘤医院肝胆胰肿瘤外科, 福州 350014

基金项目:

福州市科技局科技创新平台项目 (2021-P-055);

福建省发展和改革委员会专项基金 (31010308)

利益冲突声明：所有作者均声明不存在利益冲突。

作者贡献声明：张清华起草并撰写文章主要内容；李海涛负责主要内容修改并完善文章内容；方国旭负责协助查阅文献并提出修改意见；郭鹏飞提供了文章思路；刘景丰参与修改及审校。

详细信息

通信作者:
李海涛，lht45182@163.com

刘景丰，drjingfeng@126.com

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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-14
- 录用日期: 2021-10-17
- 出版日期: 2022-01-20

Application and prospect of deep learning in primary liver cancer-related diagnostic model

Qinghua ZHANG¹,
Haitao LI^{2a, 2b
, ,},
Guoxu FANG^{2a, 2b},
Pengfei GUO^2b,
Jingfeng LIU^{2b, 3
, ,}

1.
Graduate School of Fujian Medical University, Fuzhou 350108, China
2a.
Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
2b.
Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
3.
Department of Hepatobiliary and Pancreatic Tumor Surgery, Fujian Provincial Tumor Hospital, Fuzhou 350014, China

Research funding:

Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau (2021-P-055);

Specific Foundation of Development and Reform Commission in Fujian Province (31010308)

摘要

摘要: 深度学习是机器学习通过大量数据训练及分析来模拟人脑的学习行为而获得新的知识和技能。随着医学技术的进步, 医学领域积累了大量的数据, 对数据的研究有助于深入了解数据内的联系与规律, 从而有助于预测人类疾病的发生与预后。深度学习通过找出数据中隐藏的信息, 在医学领域中的应用日益突出。原发性肝癌是发病率和死亡率很高的恶性肿瘤, 预后差, 复发率高, 如何早期诊断、及时治疗、预测复发等一直是研究重点之一。本文从肝癌发生风险预测、术后复发与生存风险预测等方面闸述深度学习在肝癌诊断及复发方面的应用进展。
- 肝肿瘤 /
- 深度学习 /
- 机器学习
Abstract: Deep learning is a process in which machine learning obtains new knowledge and skills by simulating the learning behavior of human brain through massive data training and analysis. With the development of medical technology, a large amount of data has been accumulated in the medical field, and the research on data may help to understand the relationships and rules within data and predict the onset and prognosis of human diseases. Deep learning can find the hidden information in data and has been increasingly used in the medical field. Primary liver cancer is a malignant tumor with high incidence and mortality rates, poor prognosis, and a high recurrence rate, and early diagnosis, timely treatment, and prediction of recurrence have always been the research hotspots in recent years. This article reviews the advances in the application of deep learning in the diagnosis and recurrence of liver cancer from the aspects of risk prediction, postoperative recurrence, and survival risk prediction.
- Liver Neoplasm /
- Deep Learning /
- Machine Learning

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参考文献(52)

[1]	FORNER A, REIG M, BRUIX J. Hepatocellular carcinoma[J]. Lancet, 2018, 391(10127): 1301-1314. DOI: 10.1016/S0140-6736(18)30010-2.
[2]	FERLAY J, COLOMBET M, SOERJOMATARAM I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods[J]. Int J Cancer, 2019, 144(8): 1941-1953. DOI: 10.1002/ijc.31937.
[3]	CHEN W, ZHENG R, BAADE PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132. DOI: 10.3322/caac.21338.
[4]	LIBBRECHT MW, NOBLE WS. Machine learning applications in genetics and genomics[J]. Nat Rev Genet, 2015, 16(6): 321-332. DOI: 10.1038/nrg3920.
[5]	OBERMEYER Z, EMANUEL EJ. Predicting the future - big data, machine learning, and clinical medicine[J]. N Engl J Med, 2016, 375(13): 1216-1219. DOI: 10.1056/NEJMp1606181.
[6]	CAO C, LIU F, TAN H, et al. Deep learning and its applications in biomedicine[J]. Genomics Proteomics Bioinformatics, 2018, 16(1): 17-32. DOI: 10.1016/j.gpb.2017.07.003.
[7]	CAMACHO DM, COLLINS KM, POWERS RK, et al. Next-generation machine learning for biological networks[J]. Cell, 2018, 173(7): 1581-1592. DOI: 10.1016/j.cell.2018.05.015.
[8]	DEO RC. Machine learning in medicine[J]. Circulation, 2015, 132(20): 1920-1930. DOI: 10.1161/CIRCULATIONAHA.115.001593.
[9]	SINGAL AG, MUKHERJEE A, ELMUNZER BJ, et al. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma[J]. Am J Gastroenterol, 2013, 108(11): 1723-1730. DOI: 10.1038/ajg.2013.332.
[10]	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. DOI: 10.1001/jama.2017.14585.
[11]	BAȘTANLAR Y, OZUYSAL M. Introduction to machine learning[J]. Methods Mol Biol, 2014, 1107: 105-128. DOI: 10.1007/978-1-62703-748-8_7.
[12]	ESTEVA A, ROBICQUET A, RAMSUNDAR B, et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25(1): 24-29. DOI: 10.1038/s41591-018-0316-z.
[13]	TERENTIEV AA, MOLDOGAZIEVA NT. Alpha-fetoprotein: A renaissance[J]. Tumour Biol, 2013, 34(4): 2075-2091. DOI: 10.1007/s13277-013-0904-y.
[14]	POON TC, CHAN AT, ZEE B, et al. Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma[J]. Oncology, 2001, 61(4): 275-283. DOI: 10.1159/000055334.
[15]	CAMAGGI CM, ZAVATTO E, GRAMANTIERI L, et al. Serum albumin-bound proteomic signature for early detection and staging of hepatocarcinoma: Sample variability and data classification[J]. Clin Chem Lab Med, 2010, 48(9): 1319-1326. DOI: 10.1515/CCLM.2010.248.
[16]	PATTERSON AD, MAURHOFER O, BEYOGLU D, et al. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling[J]. Cancer Res, 2011, 71(21): 6590-6600. DOI: 10.1158/0008-5472.CAN-11-0885.
[17]	WANG N, CAO Y, SONG W, et al. Serum peptide pattern that differentially diagnoses hepatitis B virus-related hepatocellular carcinoma from liver cirrhosis[J]. J Gastroenterol Hepatol, 2014, 29(7): 1544-1550. DOI: 10.1111/jgh.12545.
[18]	ESTEVEZ J, CHEN VL, PODLAHA O, et al. Differential serum cytokine profiles in patients with chronic hepatitis B, C, and hepatocellular carcinoma[J]. Sci Rep, 2017, 7(1): 11867. DOI: 10.1038/s41598-017-11975-7.
[19]	OSHO A, RICH NE, SINGAL AG. Role of imaging in management of hepatocellular carcinoma: Surveillance, diagnosis, and treatment response[J]. Hepatoma Res, 2020, 6: 55. DOI: 10.20517/2394-5079.2020.42.
[20]	MUNIR K, ELAHI H, AYUB A, et al. Cancer diagnosis using deep learning: A bibliographic review[J]. Cancers (Basel), 2019, 11(9): 1235. DOI: 10.3390/cancers11091235.
[21]	SINGH SP, WANG L, GUPTA S, et al. 3D deep learning on medical images: A review[J]. Sensors (Basel), 2020, 20(18): 5097. DOI: 10.3390/s20185097.
[22]	AZER SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review[J]. World J Gastrointest Oncol, 2019, 11(12): 1218-1230. DOI: 10.4251/wjgo.v11.i12.1218.
[23]	PANG W, JIANG H, LI S. Sparse contribution feature selection and classifiers optimized by concave-convex variation for HCC image recognition[J]. Biomed Res Int, 2017, 2017: 9718386. DOI: 10.1155/2017/9718386.
[24]	WANG CJ, HAMM CA, SAVIC LJ, et al. Deep learning for liver tumor diagnosis part II: Convolutional neural network interpretation using radiologic imaging features[J]. Eur Radiol, 2019, 29(7): 3348-3357. DOI: 10.1007/s00330-019-06214-8.
[25]	HAMM CA, WANG CJ, SAVIC LJ, et al. Deep learning for liver tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI[J]. Eur Radiol, 2019, 29(7): 3338-3347. DOI: 10.1007/s00330-019-06205-9.
[26]	SHI W, KUANG S, CAO S, et al. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: Choice of four-phase and three-phase CT imaging protocol[J]. Abdom Radiol (NY), 2020, 45(9): 2688-2697. DOI: 10.1007/s00261-020-02485-8.
[27]	CUCCHETTI A, PISCAGLIA F, GRIGIONI AD, et al. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: A pilot study[J]. J Hepatol, 2010, 52(6): 880-888. DOI: 10.1016/j.jhep.2009.12.037.
[28]	LI S, JIANG H, PANG W. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading[J]. Comput Biol Med, 2017, 84: 156-167. DOI: 10.1016/j.compbiomed.2017.03.017.
[29]	LIAO H, LONG Y, HAN R, et al. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma[J]. Clin Transl Med, 2020, 10(2): e102. DOI: 10.1002/ctm2.102.
[30]	LIANG Q, LIU H, WANG C, et al. Phenotypic characterization analysis of human hepatocarcinoma by urine metabolomics approach[J]. Sci Rep, 2016, 6: 19763. DOI: 10.1038/srep19763.
[31]	WANG J, JAIN S, CHEN D, et al. Development and evaluation of novel statistical methods in urine biomarker-based hepatocellular carcinoma screening[J]. Sci Rep, 2018, 8(1): 3799. DOI: 10.1038/s41598-018-21922-9.
[32]	IBRAHIM R, YOUSRI NA, ISMAIL MA, et al. Multi-level gene/MiRNA feature selection using deep belief nets and active learning[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2014, 2014: 3957-3960. DOI: 10.1109/EMBC.2014.6944490.
[33]	GUI T, DONG X, LI R, et al. Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis[J]. J Comput Biol, 2015, 22(1): 63-71. DOI: 10.1089/cmb.2014.0122.
[34]	AUGELLO G, BALASUS D, FUSILLI C, et al. Association between MICA gene variants and the risk of hepatitis C virus-induced hepatocellular cancer in a sicilian population sample[J]. OMICS, 2018, 22(4): 274-282. DOI: 10.1089/omi.2017.0215.
[35]	KIM JW, YE Q, FORGUES M, et al. Cancer-associated molecular signature in the tissue samples of patients with cirrhosis[J]. Hepatology, 2004, 39(2): 518-527. DOI: 10.1002/hep.20053.
[36]	SHEN J, QI L, ZOU Z, et al. Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases[J]. Sci Rep, 2020, 10(1): 4435. DOI: 10.1038/s41598-020-61298-3.
[37]	HO WH, LEE KT, CHEN HY, et al. Disease-free survival after hepatic resection in hepatocellular carcinoma patients: A prediction approach using artificial neural network[J]. PLoS One, 2012, 7(1): e29179. DOI: 10.1371/journal.pone.0029179.
[38]	SHI HY, LEE KT, LEE HH, et al. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery[J]. PLoS One, 2012, 7(4): e35781. DOI: 10.1371/journal.pone.0035781.
[39]	QIAO G, LI J, HUANG A, et al. Artificial neural networking model for the prediction of post-hepatectomy survival of patients with early hepatocellular carcinoma[J]. J Gastroenterol Hepatol, 2014, 29(12): 2014-2020. DOI: 10.1111/jgh.12672.
[40]	HUANG Y, CHEN H, ZENG Y, et al. Development and validation of a machine learning prognostic model for hepatocellular carcinoma recurrence after surgical resection[J]. Front Oncol, 2020, 10: 593741. DOI: 10.3389/fonc.2020.593741.
[41]	TSENG YJ, PING XO, LIANG JD, et al. Multiple-time-series clinical data processing for classification with merging algorithm and statistical measures[J]. IEEE J Biomed Health Inform, 2015, 19(3): 1036-1043. DOI: 10.1109/JBHI.2014.2357719.
[42]	QIU J, PENG B, TANG Y, et al. CpG methylation signature predicts recurrence in early-stage hepatocellular carcinoma: Results from a multicenter study[J]. J Clin Oncol, 2017, 35(7): 734-742. DOI: 10.1200/JCO.2016.68.2153.
[43]	XU RH, WEI W, KRAWCZYK M, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma[J]. Nat Mater, 2017, 16(11): 1155-1161. DOI: 10.1038/nmat4997.
[44]	LIANG JD, PING XO, TSENG YJ, et al. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods[J]. Comput Methods Programs Biomed, 2014, 117(3): 425-434. DOI: 10.1016/j.cmpb.2014.09.001.
[45]	BREHAR R, MITREA DA, VANCEA F, et al. Comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images[J]. Sensors (Basel), 2020, 20(11): 3085. DOI: 10.3390/s20113085.
[46]	ABAJIAN A, MURALI N, SAVIC LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept[J]. J Vasc Interv Radiol, 2018, 29(6): 850-857. e1. DOI: 10.1016/j.jvir.2018.01.769.
[47]	PENG J, KANG S, NING Z, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging[J]. Eur Radiol, 2020, 30(1): 413-424. DOI: 10.1007/s00330-019-06318-1.
[48]	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. DOI: 10.1158/1078-0432.CCR-17-0853.
[49]	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 (Basel), 2020, 12(10): 2791. DOI: 10.3390/cancers12102791.
[50]	SINGAL AG, MUKHERJEE A, ELMUNZER BJ, et al. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma[J]. Am J Gastroenterol, 2013, 108(11): 1723-1730. DOI: 10.1038/ajg.2013.332.
[51]	DIVYA R, RADHA P. An optimized HCC recurrence prediction using APO algorithm multiple time series clinical liver cancer dataset[J]. J Med Syst, 2019, 43(7): 193. DOI: 10.1007/s10916-019-1265-x.
[52]	GIORDANO S, TAKEDA S, DONADON M, et al. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence[J]. Liver Int, 2020, 40(12): 3117-3124. DOI: 10.1111/liv.14604.