国际肿瘤学杂志››2023,Vol. 50››Issue (5): 310-314.doi:10.3760/cma.j.cn371439-20230227-00062
收稿日期:
2023-02-27修回日期:
2023-04-01出版日期:
2023-05-08发布日期:
2023-06-27通讯作者:
张巍 E-mail:zhang_wei_1980@163.com基金资助:
Received:
2023-02-27Revised:
2023-04-01Online:
2023-05-08Published:
2023-06-27Contact:
Zhang Wei E-mail:zhang_wei_1980@163.comSupported by:
摘要:
人工智能机器学习与传统统计学建立的预测模型相比具有一定的优势。目前,已有大量研究探讨机器学习在肝脏疾病中的应用,但机器学习算法的选择以及训练不同机器学习算法的步骤尚未达成统一。随着研究的不断深入,基于机器学习联合各组学建立的预测模型对提升肝脏疾病诊断、治疗以及疗效评估可发挥巨大作用。
陈丰洋, 张巍. 机器学习在肝脏疾病中的应用:提升诊断、治疗和疗效评估[J]. 国际肿瘤学杂志, 2023, 50(5): 310-314.
Chen Fengyang, Zhang Wei. Application of machine learning in liver disease: improving diagnosis, treatment, and efficacy evaluation[J]. Journal of International Oncology, 2023, 50(5): 310-314.
[1] | Mitchell TM. Machine learning[M]. New York: McGraw-Hill, 1997. |
[2] | Ding H, Fawad M, Xu X, et al. A framework for identification and classification of liver diseases based on machine learning algorithms[J]. Front Oncol, 2022, 12: 1048348. DOI: 10.3389/fonc.2022.1048348. doi:10.3389/fonc.2022.1048348 |
[3] | Wu Y, Yang X, Morris HL, et al. Noninvasive diagnosis of nonalcoholic steatohepatitis and advanced liver fibrosis using machine lear-ning methods: comparative study with existing quantitative risk scores[J]. JMIR Med Inform, 2022, 10(6): e36997. DOI: 10.2196/36997. doi:10.2196/36997 |
[4] | Pei X, Deng Q, Liu Z, et al. Machine learning algorithms for predicting fatty liver disease[J]. Ann Nutr Metab, 2021, 77(1): 38-45. DOI: 10.1159/000513654. doi:10.1159/000513654 |
[5] | Li J, Tao Y, Cong H, et al. Predicting liver cancers using skewed epidemiological data[J]. Artif Intell Med, 2022, 124: 102234. DOI: 10.1016/j.artmed.2021.102234. doi:10.1016/j.artmed.2021.102234 |
[6] | Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics[J]. Nat Rev Genet, 2015, 16(6): 321-332. DOI: 10.1038/nrg3920. doi:10.1038/nrg3920pmid:25948244 |
[7] | Han N, He J, Shi L, et al. Identification of biomarkers in nonalcoholic fatty liver disease: a machine learning method and experimental study[J]. Front Genet, 2022, 13: 1020899. DOI: 10.3389/fgene.2022.1020899. doi:10.3389/fgene.2022.1020899 |
[8] | Mann M, Kumar C, Zeng WF, et al. Artificial intelligence for proteomics and biomarker discovery[J]. Cell Syst, 2021, 12(8): 759-770. DOI: 10.1016/j.cels.2021.06.006. doi:10.1016/j.cels.2021.06.006pmid:34411543 |
[9] | Zhang S, Liu Y, Chen J, et al. Autoantibody signature in hepatocellular carcinoma using seromics[J]. J Hematol Oncol, 2020, 13(1): 85. DOI: 10.1186/s13045-020-00918-x. doi:10.1186/s13045-020-00918-x |
[10] | Streba CT, Ionescu M, Gheonea DI, et al. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors[J]. World J Gastroenterol, 2012, 18(32): 4427-4434. DOI: 10.3748/wjg.v18.i32.4427. doi:10.3748/wjg.v18.i32.4427 |
[11] | Turco S, Tiyarattanachai T, Ebrahimkheil K, et al. Interpretable machine learning for characterization of focal liver lesions by contrast-enhanced ultrasound[J]. IEEE Trans Ultrason Ferroelectr Freq Control, 2022, 69(5): 1670-1681. DOI: 10.1109/TUFFC.2022.3161719. doi:10.1109/TUFFC.2022.3161719pmid:35320099 |
[12] | Căleanu CD, Sîrbu CL, Simion G. Deep neural architectures for contrast enhanced ultrasound (CEUS) focal liver lesions automated diagnosis[J]. Sensors (Basel), 2021, 21(12): 4126. DOI: 10.3390/s21124126. doi:10.3390/s21124126 |
[13] | Nebbia G, Zhang Q, Arefan D, et al. Pre-operative microvascular invasion prediction using multi-parametric liver MRI radiomics[J]. J Digit Imaging, 2020, 33(6): 1376-1386. DOI: 10.1007/s10278-020-00353-x. doi:10.1007/s10278-020-00353-x |
[14] | Nakaura T, Higaki T, Awai K, et al. A primer for understanding radiology articles about machine learning and deep learning[J]. Diagn Interv Imaging, 2020, 101(12): 765-770. DOI: 10.1016/j.diii.2020.10.001. doi:10.1016/j.diii.2020.10.001 |
[15] | Sandfort V, Yan K, Pickhardt PJ, et al. Data augmentation using generative adversarial networks (CycleGAN) to improve generali-zability in CT segmentation tasks[J]. Sci Rep, 2019, 9(1): 16884. DOI: 10.1038/s41598-019-52737-x. doi:10.1038/s41598-019-52737-xpmid:31729403 |
[16] | Mulé S, Galletto Pregliasco A, Tenenhaus A, et al. Multiphase liver MRI for identifying the macrotrabecular-massive subtype of hepatocellular carcinoma[J]. Radiology, 2020, 295(3): 562-571. DOI: 10.1148/radiol.2020192230. doi:10.1148/radiol.2020192230pmid:32228294 |
[17] | Mulé S, Lawrance L, Belkouchi Y, et al. Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: the SFR 2021 artificial intelligence data challenge[J]. Diagn Interv Imaging, 2023, 104(1): 43-48. DOI: 10.1016/j.diii.2022.09.005. doi:10.1016/j.diii.2022.09.005 |
[18] | Heusel M, Ramsauer H, Unterthiner T, et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA: Curran Associates Inc., 2017: 6629-6640. DOI: 10.5555/3295222.3295408. doi:10.5555/3295222.3295408 |
[19] | Xu Q, Huang G, Yuan Y, et al. An empirical study on evaluation metrics of generative adversarial networks[DB/OL]. [2018-08-17][2023-02-19]. https://arxiv.org/abs/1806.07755v1. |
[20] | Teramoto T, Shinohara T, Takiyama A. Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology[J]. Comput Methods Programs Biomed, 2020, 195: 105614. DOI: 10.1016/j.cmpb.2020.105614. doi:10.1016/j.cmpb.2020.105614 |
[21] | Matteoni CA, Younossi ZM, Gramlich T, et al. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity[J]. Gastroenterology, 1999, 116(6): 1413-1419. DOI: 10.1016/S0016-5085(99)70506-8. doi:10.1016/s0016-5085(99)70506-8pmid:10348825 |
[22] | 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. DOI: 10.1002/hep.31207. doi:10.1002/hep.31207 |
[23] | Minerali E, Foil DH, Zorn KM, et al. Comparing machine learning algorithms for predicting drug-induced liver injury (DILI)[J]. Mol Pharm, 2020, 17(7): 2628-2637. DOI: 10.1021/acs.molpharmaceut.0c00326. doi:10.1021/acs.molpharmaceut.0c00326 |
[24] | Chen M, Suzuki A, Thakkar S, et al. DILIrank: the largest refe-rence drug list ranked by the risk for developing drug-induced liver injury in humans[J]. Drug Discov Today, 2016, 21(4): 648-653. DOI: 10.1016/j.drudis.2016.02.015. doi:10.1016/j.drudis.2016.02.015 |
[25] | Williams DP, Lazic SE, Foster AJ, et al. Predicting drug-induced liver injury with Bayesian machine learning[J]. Chem Res Toxicol, 2020, 33(1): 239-248. DOI: 10.1021/acs.chemrestox.9b00264. doi:10.1021/acs.chemrestox.9b00264pmid:31535850 |
[26] | Aleo MD, Shah F, Allen S, et al. Moving beyond binary predictions of human drug-induced liver injury (DILI) toward contrasting relative risk potential[J]. Chem Res Toxicol, 2020, 33(1): 223-238. DOI: 10.1021/acs.chemrestox.9b00262. doi:10.1021/acs.chemrestox.9b00262pmid:31532188 |
[27] | Wu CY, Benet LZ. Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system[J]. Pharm Res, 2005, 22(1): 11-23. DOI: 10.1007/s11095-004-9004-4. doi:10.1007/s11095-004-9004-4 |
[28] | Mazzaferro V, Llovet JM, Miceli R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis[J]. Lancet Oncol, 2009, 10(1): 35-43. DOI: 10.1016/S1470-2045(08)70284-5. doi:10.1016/S1470-2045(08)70284-5pmid:19058754 |
[29] | Kim WR, Lake JR, Smith JM, et al. OPTN/SRTR 2017 annual data report: liver[J]. Am J Transplant, 2019, 19 Suppl 2: 184-283. DOI: 10.1111/ajt.15276. doi:10.1111/ajt.15276pmid:30811890 |
[30] | He T, Fong JN, Moore LW, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer[J]. Comput Med Imaging Graph, 2021, 89: 101894. DOI: 10.1016/j.compmedimag.2021.101894. doi:10.1016/j.compmedimag.2021.101894 |
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