Research Progress of Deep Learning Algorithm in the Field of Imaging Diagnosis and Treatment of Hepatocellular Carcinoma

doi:10.7517/issn.1674-0475.230801

Abstract

Abstract: Deep learning algorithm is an important branch in the field of artificial intelligence, and intelligent technologies represented by deep learning algorithms have gradually applied to the medical field. Hepatocellular carcinoma is a common malignant tumor of the digestive system in China, and its incidence and mortality remain high, which seriously endangers people's life and health. Timely diagnosis, precise treatment and prognosis evaluation of patients with hepatocellular carcinoma to improve their survival quality and survival rate have always been the key and difficulties in clinical research. Deep learning provides an intelligent method for the diagnosis and treatment of hepatocellular carcinoma. This study summarizes the relevant research on the application of deep learning algorithm to the imaging diagnosis and treatment of hepatocellular carcinoma at home and abroad.

Key words: artificial intelligence, deep learning algorithm, hepatocellular carcinoma, imaging diagnosis and treatment

ZHU Yongyue, ZHOU Zhou, LI Yanruo, GUO Wei, WANG Mohan, WANG Daoqing. Research Progress of Deep Learning Algorithm in the Field of Imaging Diagnosis and Treatment of Hepatocellular Carcinoma[J]. IMAGING SCIENCE AND PHOTOCHEMISTRY, 2023, 41(6): 345-349.

References

[1] Zhou J, Sun H C, Wang Z, et al. Guidelines for diagnosis and treatment of primary liver cancer in China (2017 edition)[J]. Liver Cancer,2018, 7(3):235-260.
[2] Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA:A Cancer Journal for Clinicians,2021,71(3):209-249.
[3] 袁涛, 覃由宣, 吴玉平, 等. 肝细胞癌组织、癌旁组织、远癌组织及背景肝组织MR扩散加权成像对比研究[J]. 实用放射学杂志, 2022, 38(8):363-366.
[4] 刘旭初, 覃世运, 陈丽君, 等. KRAS基因突变对经肝动脉化疗栓塞术治疗的中晚期原发性肝癌患者预后的预测价值[J]. 临床肝胆病杂志, 2022, 38(11):2514-2519.
[5] Cronin K A, Scott S, Firth A U, et al. Annual report to the nation on the status of cancer, part 1:National cancer statistics[J]. Cancer,2022,128(24):4251-4284.
[6] 赫捷,陈万青,沈洪兵,等. 中国人群肝癌筛查指南(2022,北京)[J]. 临床肝胆病杂志, 2022, 38(8):1739-1758,1954-1967.
[7] Bai L J, Wang X P, Shi S L, et al. Evaluation of 3D-CEUS in the recurrence of liver cancer after radiofrequency ablation[J]. Journal of Healthcare Engineering,2021,2021:1-6.
[8] 金征宇. 人工智能医学影像应用:现实与挑战[J]. 放射学实践,2018,33(10):989-991.
[9] 何佳倩, 安兴伟, 任贺成, 等. 计算机辅助诊断检测颅内动脉瘤进展[J]. 中国医学影像技术, 2023, 39(2):291-294.
[10] 刘红枝, 刘景丰. 人工智能在原发性肝癌外科治疗中的应用现状与展望[J]. 临床肝胆病杂志, 2022, 38(1):10-14.
[11] Kiani A, Uyumazturk B, Rajpurkar P, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer[J]. Npj Digital Medicine,2020,3:23.
[12] Chai H, Xia L, Zhang L, et al. An adaptive transfer-learning-based deep cox neural network for hepatocellular carcinoma prognosis prediction[J]. Frontiers in Oncology,2021,11:692774.
[13] Badillo S, Banfai B, Birzele F, et al. An introduction to machine learning[J]. Clinical Pharmacology & Therapeutics,2020,107(4):871-885.
[14] Yan M, Zhang X, Zhang B, et al. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy[J]. European Radiology,2023, 33(7):4949-4961.
[15] Chan H P, Hadjiiski L M, Samala R K. Computer-aided diagnosis in the era of deep learning[J]. Medical Physics,2020, 47(5):e218-e227.
[16] Ichikawa S, Itadani H, Sugimori H. Prediction of body weight from chest radiographs using deep learning with a convolutional neural network[J]. Radiological Physics and Technology,2023, 16(1):127-134.
[17] Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Translational Vision Science & Technology,2020, 9(2):14.
[18] Li X M, Qi Z D, Du H Y, et al. Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs[J]. European Radiology, 2022, 32(2):771-782.
[19] 林斌, 周智鹏, 戴文海, 等. 2018版肝脏影像报告与数据系统肝胆期低信号对肝细胞癌的诊断效能[J]. 实用放射学杂志, 2022,38(1):68-72.
[20] 左立平, 范金蕾, 石硕,等. 术前增强MRI在预测肝细胞肝癌根治术后早期复发中的价值[J].实用放射学杂志, 2023, 39(7):1114-1118.
[21] Shukla P K, Zakariah M, Hatamleh W A, et al. AI-DRIVEN novel approach for liver cancer screening and prediction using cascaded fully convolutional neural network[J]. Journal of Healthcare Engineering,2022,2022:4277436.
[22] Zhen S H, Cheng M, Tao Y B, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data[J]. Frontiers in Oncology,2020, 10:680.
[23] Al Duhayyim M, Mengash H A, Marzouk R, et al. Hybrid rider optimization with deep learning driven biomedical liver cancer detection and classification[J]. Computational Intelligence and Neuroscience,2022,2022:6162445.
[24] 李睿, 许祥丛, 林静怡, 等. 基于卷积神经网络的超声影像肝癌自动分类[J]. 生物化学与生物物理进展, 2023, 50(3):668-675.
[25] 国家卫生健康委办公厅. 原发性肝癌诊疗指南(2022年版)[J]. 临床肝胆病杂志,2022, 38(2):288-303.
[26] Wu Y N, White G M, Cornelius T, et al. Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors[J]. Annals of Translational Medicine,2020, 8(11):701.
[27] Jiang Y Q, Cao S E, Cao S L, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. Journal of Cancer Research and Clinical Oncology,2021, 147(3):821-833.
[28] Zhuang B W, Li W, Wang W, et al. Treatment effect of radiofrequency ablation versus liver transplantation and surgical resection for hepatocellular carcinoma within Milan criteria:A population-based study[J]. European Radiology,2021, 31(7):5379-5389.
[29] Peng J, Kang S, Ning Z Y, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging[J]. European Radiology, 2020, 30(1):413-424.
[30] Morshid A, Elsayes K M, Khalaf A M, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization[J]. Radiology Artificial Intelligence, 2019, 1(5):e180021.
[31] Wu C F, Wu Y J, Liang P C, et al. Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation[J]. Journal of the Formosan Medical Association,2017, 116(10):765-773.
[32] 杨志企, 陈小凤, 杨佳达, 等. 双源CT双动脉期、双门脉期对肝硬化背景下小肝癌的诊断价值[J]. 实用放射学杂志, 2020,36(1):136-138.