[1]
|
Chakraborty, E. and Sarkar, D. (2022) Emerging Therapies for Hepatocellular Carcinoma (HCC). Cancer, 14, Article 2798. https://doi.org/10.3390/cancers14112798
|
[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.
https://doi.org/10.3322/caac.21660
|
[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. https://doi.org/10.1016/j.ejrad.2018.01.025
|
[4]
|
Renganathan, V. (2019) Overview of Artificial Neural Network Models in the Biomedical Domain. Bratislava Medical Journal, 120, 536-540. https://doi.org/10.4149/BLL_2019_087
|
[5]
|
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444.
https://doi.org/10.1038/nature14539
|
[6]
|
Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019) A Guide to Deep Learning in Healthcare. Nature Medicine, 25, 24-29. https://doi.org/10.1038/s41591-018-0316-z
|
[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. https://doi.org/10.1002/cac2.12012
|
[8]
|
Deo, R.C. (2015) Machine Learning in Medicine. Circulation, 132, 1920-1930.
https://doi.org/10.1161/CIRCULATIONAHA.115.001593
|
[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.
https://doi.org/10.1007/s10459-015-9645-6
|
[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. https://doi.org/10.1016/S0140-6736(19)32998-8
|
[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.
https://doi.org/10.1001/jama.2017.14585
|
[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. https://doi.org/10.1093/eurheartj/ehaa640
|
[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.
https://doi.org/10.4254/wjh.v13.i12.2039
|
[14]
|
Wang, W. and Wei, C. (2020) Advances in the Early Diagnosis of Hepatocellular Carcinoma. Genes & Diseases, 7, 308-319. https://doi.org/10.1016/j.gendis.2020.01.014
|
[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.
https://doi.org/10.1002/cyto.a.23871
|
[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.
https://doi.org/10.1038/s41698-020-0120-3
|
[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. https://doi.org/10.3389/fonc.2021.762733
|
[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.
https://doi.org/10.3389/fmed.2022.853261
|
[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.
https://doi.org/10.1053/j.gastro.2022.02.025
|
[20]
|
El Jabbour, T., Lagana, S.M. and Lee, H. (2019) Update on Hepatocellular Carcinoma: Pathologists’ Review. World Journal of Gastroenterology, 25, 1653-1665. https://doi.org/10.3748/wjg.v25.i14.1653
|
[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.
https://doi.org/10.1002/jbio.201800435
|
[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. https://doi.org/10.1038/s41746-020-0232-8
|
[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.
https://doi.org/10.3390/cancers12123562
|
[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.
https://doi.org/10.1007/s11548-021-02410-4
|
[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.
https://doi.org/10.1001/jamasurg.2015.4257
|
[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.
https://doi.org/10.3389/fonc.2021.588010
|
[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. https://doi.org/10.1016/j.ejso.2021.11.120
|
[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.
https://doi.org/10.3389/fonc.2022.878061
|
[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.
https://doi.org/10.1186/s40644-021-00425-3
|
[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.
https://doi.org/10.1002/jmri.27538
|
[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.
https://doi.org/10.1007/s00330-021-08198-w
|
[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.
https://doi.org/10.1364/JOSAA.463439
|
[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.
https://doi.org/10.1007/s12072-022-10323-w
|
[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. https://doi.org/10.1016/j.cld.2020.07.010
|
[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. https://doi.org/10.1002/hep.31207
|
[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. https://doi.org/10.1136/gutjnl-2020-320930
|
[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.
https://doi.org/10.1038/s41598-021-81506-y
|
[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. https://doi.org/10.1007/s12072-022-10393-w
|
[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.
https://doi.org/10.3389/fonc.2022.968202
|
[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.
https://doi.org/10.1016/j.jhep.2022.01.018
|