深度学习在肝癌病理诊断中的研究进展
Research Progress of Deep Learning in Pathological Diagnosis of Liver Cancer
DOI: 10.12677/ACM.2023.13122654, PDF,   
作者: 陈 超, 贾巍立, 石 文, 屈 申：西安医学院研究生工作部，陕西 西安；空军军医大学第一附属医院肝胆外科，陕西 西安；赵自豪, 宋文杰*：空军军医大学第一附属医院肝胆外科，陕西 西安
关键词: 肝癌；深度学习；人工智能；病理；应用进展；Hepatocellular Carcinoma； Deep Learning； Artificial Intelligence； Pathology； Application Progress
摘要: 原发性肝癌是对人类健康威胁最大的恶性肿瘤之一，其发病率和死亡率一直居高不下。病理诊断是恶性肿瘤诊断的金标准，传统病理诊断依赖于诊断医生的视觉观测，难以充分利用病理图像信息，并且易受诊断医师主观情绪的影响，深度学习的出现有望弥补这一缺憾。深度学习在图像处理方面具有天然优势，将深度学习方法用于肝癌病理图像分析，可充分利用图像特征，减少病理医生主观感受带来的诊断误差。本文就深度学习在肝癌病理诊断中的应用进展加以归纳总结，以期为后续研究带来启发，推动深度学习辅助医疗应用于临床。
Abstract: Primary hepatic carcinoma is one of the most threatening malignant tumors to human health, and its morbidity and mortality are always high. Pathological diagnosis is the gold standard for the di-agnosis of malignant tumors. Traditional pathological diagnosis relies on the visual observation of diagnostic doctors, which is difficult to make full use of pathological image information, and is easily affected by the subjective emotions of diagnostic doctors. The emergence of deep learning is ex-pected to make up for this shortcoming. Deep learning has natural advantages in image processing. Applying deep learning method to liver cancer pathological image analysis can make full use of im-age features and reduce diagnostic errors caused by pathologists’ subjective feelings. This paper summarizes the application progress of deep learning in the pathological diagnosis of liver cancer, in order to bring inspiration for subsequent research and promote the application of deep learn-ing-assisted medical treatment in clinical practice.
文章引用：陈超, 贾巍立, 石文, 赵自豪, 屈申, 宋文杰. 深度学习在肝癌病理诊断中的研究进展[J]. 临床医学进展, 2023, 13(12): 18864-18870. https://doi.org/10.12677/ACM.2023.13122654

参考文献

[1] Chakraborty, E. and Sarkar, D. (2022) Emerging Therapies for Hepatocellular Carcinoma (HCC). Cancer, 14, Article 2798. [Google Scholar] [CrossRef] [PubMed]
[2] Sung, H., Ferlay, J., Siegel, R.L., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. [Google Scholar] [CrossRef] [PubMed]
[3] Ayuso, C., Rimola, J., Vilana, R., et al. (2018) Diagnosis and Staging of Hepatocellular Carcinoma (HCC): Current Guidelines. European Journal of Radiology, 101, 72-81. [Google Scholar] [CrossRef] [PubMed]
[4] Renganathan, V. (2019) Overview of Artificial Neural Network Models in the Biomedical Domain. Bratislava Medical Journal, 120, 536-540. [Google Scholar] [CrossRef] 
[5] LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[6] Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019) A Guide to Deep Learning in Healthcare. Nature Medicine, 25, 24-29. [Google Scholar] [CrossRef] [PubMed]
[7] Jiang, Y.H., Yang, M., Wang, S.H., Li, X.C. and Sun, Y. (2020) Emerging Role of Deep Learning-Based Artificial Intelligence in Tumor Pathology. Cancer Communications, 40, 154-166. [Google Scholar] [CrossRef] [PubMed]
[8] Deo, R.C. (2015) Machine Learning in Medicine. Circulation, 132, 1920-1930. [Google Scholar] [CrossRef] 
[9] Dolmans, D.H.J.M., Loyens, S.M.M., Marcq, H. and Gijbels, D. (2016) Deep and Surface Learning in Problem-Based Learning: A Review of the Literature. Advances in Health Sciences Education: Theory and Practice, 21, 1087-1112. [Google Scholar] [CrossRef] [PubMed]
[10] Skrede, O.J., De Raedt, S., Kleppe, A., et al. (2020) Deep Learn-ing for Prediction of Colorectal Cancer Outcome: A Discovery and Validation Study. The Lancet, 395, 350-360. [Google Scholar] [CrossRef] 
[11] Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., et al. (2017) Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer. JAMA, 318, 2199-2210. [Google Scholar] [CrossRef] [PubMed]
[12] Lin, S., Li, Z., Fu, B., et al. (2020) Feasibility of Using Deep Learn-ing to Detect Coronary Artery Disease Based on Facial Photo. European Heart Journal, 41, 4400-4411. [Google Scholar] [CrossRef] [PubMed]
[13] Ahn, J.C., Qureshi, T.A., Singal, A.G., Li, D.B. and Yang, J.D. (2021) Deep Learning in Hepatocellular Carcinoma: Current Status and Future Perspectives. World Journal of Hepatolo-gy, 13, 2039-2051. [Google Scholar] [CrossRef] [PubMed]
[14] Wang, W. and Wei, C. (2020) Advances in the Early Diagnosis of Hepatocellular Carcinoma. Genes & Diseases, 7, 308-319. [Google Scholar] [CrossRef] [PubMed]
[15] Wang, R., He, Y., Yao, C., et al. (2020) Classification and Segmentation of Hyperspectral Data of Hepatocellular Carcinoma Samples Using 1-D Convolutional Neural Network. Cytometry. Cytometry Part A, 97, 31-38. [Google Scholar] [CrossRef] [PubMed]
[16] Chen, M., Zhang, B., Topatana, W., et al. (2020) Classification and Mu-tation Prediction Based on Histopathology H&E Images in Liver Cancer Using Deep Learning. NPJ Precision Oncology, 4, Article No. 14. [Google Scholar] [CrossRef] [PubMed]
[17] Feng, S., Yu, X., Liang, W., et al. (2021) Development of a Deep Learning Model to Assist with Diagnosis of Hepatocellular Carcinoma. Frontiers in Oncology, 11, Article 762733. [Google Scholar] [CrossRef] [PubMed]
[18] Chen, W.M., Fu, M., Zhang, C.J., et al. (2022) Deep Learn-ing-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond. Frontiers in Medicine, 9, Article 853261. [Google Scholar] [CrossRef] [PubMed]
[19] Cheng, N., Ren, Y., Zhou, J., et al. (2022) Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images. Gastroenterology, 162, 1948-1961.E7. [Google Scholar] [CrossRef] [PubMed]
[20] El Jabbour, T., Lagana, S.M. and Lee, H. (2019) Update on Hepatocellular Carcinoma: Pathologists’ Review. World Journal of Gastroenterology, 25, 1653-1665. [Google Scholar] [CrossRef] [PubMed]
[21] Lin, H., Wei, C., Wang, G., et al. (2019) Automated Classification of Hepatocellular Carcinoma Differentiation Using Multiphoton Microscopy and Deep Learning. Journal of Biophotonics, 12, e201800435. [Google Scholar] [CrossRef] [PubMed]
[22] Kiani, A., Uyumazturk, B., Rajpurkar, P., et al. (2020) Impact of a Deep Learning Assistant on the Histopathologic Classification of Liver Cancer. NPJ Digital Medicine, 3, Article No. 23. [Google Scholar] [CrossRef] [PubMed]
[23] Wang, H., Jiang, Y., Li, B., et al. (2020) Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Cancer, 12, Article 3562. [Google Scholar] [CrossRef] [PubMed]
[24] Aatresh, A.A., Alabhya, K., Lal, S., Kini, J. and Saxena, P.U.P. (2021) LiverNet: Efficient and Robust Deep Learning Model for Automatic Diagnosis of Sub-Types of Liver Hepatocel-lular Carcinoma Cancer from H&E Stained Liver Histopathology Images. International Journal of Computer Assisted Radiology and Surgery, 16, 1549-1563. [Google Scholar] [CrossRef] [PubMed]
[25] Lei, Z., Li, J., Wu, D., et al. (2016) Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus-Related Hepatocellular Carcinoma within the Milan Cri-teria. JAMA Surgery, 151, 356-363. [Google Scholar] [CrossRef] [PubMed]
[26] Zhou, W., Jian, W., Cen, X., et al. (2021) Prediction of Micro-vascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Net-works. Frontiers in Oncology, 11, Article 588010. [Google Scholar] [CrossRef] [PubMed]
[27] Yang, Y., Zhou, Y., Zhou, C. and Ma, X.L. (2022) Deep Learning Radiomics Based on Contrast Enhanced Computed Tomography Predicts Microvascular Invasion and Survival Outcome in Early Stage Hepatocellular Carcinoma. European Journal of Surgical Oncology: The Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology, 48, 1068-1077. [Google Scholar] [CrossRef] [PubMed]
[28] Zhang, Y., Wei, Q., Huang, Y., et al. (2022) Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma. Frontiers in Oncology, 12, Article 878061. [Google Scholar] [CrossRef] [PubMed]
[29] Liu, S.C., Lai, J., Huang, J.Y., et al. (2021) Predicting Microvascu-lar Invasion in Hepatocellular Carcinoma: A Deep Learning Model Validated across Hospitals. Cancer Imaging, 21, Ar-ticle No. 56. [Google Scholar] [CrossRef] [PubMed]
[30] Zhang, Y., Lv, X., Qiu, J., et al. (2021) Deep Learning with 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Journal of Magnetic Resonance Imaging, 54, 134-143. [Google Scholar] [CrossRef] [PubMed]
[31] Li, X., Qi, Z., Du, H., et al. (2022) Deep Convolutional Neural Network for Preoperative Prediction of Microvascular Invasion and Clinical Outcomes in Patients with HCCs. European Radiolo-gy, 32, 771-782. [Google Scholar] [CrossRef] [PubMed]
[32] Sun, L., Sun, Z., Wang, C., et al. (2022) PCformer: An MVI Recognition Method via Classification of the MVI Boundary according to Histopathological Images of Liver Cancer. Journal of the Optical Society of America A, 39, 1673-1681. [Google Scholar] [CrossRef] 
[33] Chen, Q., Xiao, H., Gu, Y., et al. (2022) Deep Learning for Evalua-tion of Microvascular Invasion in Hepatocellular Carcinoma from Tumor Areas of Histology Images. Hepatology Inter-nationa, 16, 590-602. [Google Scholar] [CrossRef] [PubMed]
[34] Vyas, M. and Zhang, X. (2020) Hepatocellular Carcinoma: Role of Pathology in the Era of Precision Medicine. Clinics in Liver Disease, 24, 591-610. [Google Scholar] [CrossRef] [PubMed]
[35] Saillard, C., Schmauch, B., Laifa, O., et al. (2020) Predicting Sur-vival after Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides. Hepatology, 72, 2000-2013. [Google Scholar] [CrossRef] [PubMed]
[36] Shi, J.Y., Wang, X., Ding, G.Y., et al. (2021) Exploring Prognostic Indi-cators in the Pathological Images of Hepatocellular Carcinoma Based on Deep Learning. Gut, 70, 951-961. [Google Scholar] [CrossRef] [PubMed]
[37] Yamashita, R., Long, J., Saleem, A., Rubin, D.L. and Shen, J. (2021) Deep Learning Predicts Postsurgical Recurrence of Hepatocellular Carcinoma from Digital Histopathologic Imag-es. Scientific Reports, 11, Article No. 2047. [Google Scholar] [CrossRef] [PubMed]
[38] Wang, K., Xiang, Y., Yan, J., et al. (2022) A Deep Learning Model with Incorporation of Microvascular Invasion Area as a Factor in Predicting Prognosis of Hepatocellular Carci-noma after R0 Hepatectomy. Hepatology Internationa, 16, 1188-1198. [Google Scholar] [CrossRef] [PubMed]
[39] Qu, W.F., Tian, M.X., Qiu, J.T., et al. (2022) Exploring Patho-logical Signatures for Predicting the Recurrence of Early-Stage Hepatocellular Carcinoma Based on Deep Learning. Frontiers in Oncology, 12, Article 968202. [Google Scholar] [CrossRef] [PubMed]
[40] Zeng, Q., Klein, C., Caruso, S., et al. (2022) Artificial Intelligence Predicts Immune and Inflammatory Gene Signatures Directly from Hepatocellular Carcinoma Histology. Journal of Hepatology, 77, 116-127. [Google Scholar] [CrossRef] [PubMed]

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