影像组学在肝细胞癌中的应用进展
Advances in the Application of Radiomics in Hepatocellular Carcinoma
DOI: 10.12677/ACM.2023.13122897, PDF,   
作者: 汪雪怡:三峡大学第一临床医学院,湖北 宜昌;李道俊*:宜昌市中心人民医院肿瘤科,湖北 宜昌
关键词: 影像组学肝细胞癌诊断疗效评估Radiomics Hepatocellular Carcinoma Diagnosis Efficacy Evaluation
摘要: 肝细胞癌是原发性肝癌最常见的类型,我国肝癌发病率及死亡率都处于较高水平,严重威胁着人类的生命健康。影像组学利用计算机复杂的图像分析能力以及海量的医学成像数据,为现代医学提供了强大的工具。影像组学将医学影像诊断和大数据技术相融合,通过提取肉眼无法识别的图像特征,客观量化病灶的特征变化及分布潜在规律,为肝癌的诊断、分级、评估和预后提供依据,为患者的个体化、综合性、精准性治疗提供辅助作用。本文简要介绍影像组学的概念及其在肝细胞癌领域的应用进展。
Abstract: Hepatocellular carcinoma is the most common type of primary liver cancer. The incidence and mortality of liver cancer in China are at a high level, which seriously threatens human life and health. Radiomics utilizes complex image analysis capabilities of computer and massive amounts of medical imaging data, providing a powerful tool in modern medicine. By integrating medical imag-ing diagnosis with big data technology, radiomics can objectively quantify the characteristic chang-es and potential distribution of lesions by extracting image features that cannot be recognized by the naked eye, providing a basis for the diagnosis, classification, evaluation and prognosis of liver cancer, and providing an auxiliary role for the individualized, comprehensive and accurate treat-ment of patients. This article briefly introduces the concept of radiomics and its application in hepatocellular carcinoma.
文章引用:汪雪怡, 李道俊. 影像组学在肝细胞癌中的应用进展[J]. 临床医学进展, 2023, 13(12): 20601-20606. https://doi.org/10.12677/ACM.2023.13122897

参考文献

[1] 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]
[2] Chen, W., Zheng, R., Baade, P.D., et al. (2015) Cancer Statistics in China, 2015. CA: A Cancer Journal for Clinicians, 66, 115-132. [Google Scholar] [CrossRef] [PubMed]
[3] 黄辛. 复旦大学揭示全球不同地区肝癌发病模式[J]. 肿瘤防治研究, 2019, 46(5): 497.
[4] 赵荣荣, 邓永东, 袁宏. 236例原发性肝癌患者流行病学及临床特点分析[J]. 临床肝胆病杂志, 2016, 32(8): 1538-1542.
[5] Cai, W.L. and Hong, G.B. (2018) Quantitative Image Analysis for Evaluation of Tumor Response in Clinical Oncology. Chronic Diseases and Translational Medicine, 4, 18-28. [Google Scholar] [CrossRef] [PubMed]
[6] Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
[7] Avanzo, M., Stancanello, J. and El Naqa, I. (2017) Beyond Imaging: The Promise of Radiomics. European Journal of Medical Physics, 38, 122-139. [Google Scholar] [CrossRef] [PubMed]
[8] Sherman, M. and Bruix, J. (2015) Biopsy for Liver Cancer: How to Balance Research Needs with Evidence-Based Clinical Practice. Hepatology, 61, 433-436. [Google Scholar] [CrossRef] [PubMed]
[9] Huang, Y.L., Chen, J.H. and Shen, W.C. (2006) Diagnosis of Hepatic Tu-mors with Texture Analysis in Nonenhanced Computed Tomography Images. Academic Radiology, 13, 713-720. [Google Scholar] [CrossRef] [PubMed]
[10] Wu, J.J., Liu, A.L., Cui, J.J., et al. (2019) Radiomics-Based Classi-fication of Hepatocellular Carcinoma and Hepatic Haemangioma on Precontrast Magnetic Resonance Images. BMC Med-ical Imaging, 19, Article No. 23. [Google Scholar] [CrossRef] [PubMed]
[11] Yao, Z., Dong, Y., Wu, G., et al. (2018) Preoperative Diagnosis and Prediction of Hepatocellular Carcinoma: Radiomics Analysis Based on Multi-Modal Ultrasound Images. BMC Can-cer, 18, Article No. 1089. [Google Scholar] [CrossRef] [PubMed]
[12] 夏金菊, 王添艺, 蔡权宇, 等. MRI影像组学在混合型肝癌与肝内胆管细胞癌鉴别诊断中的应用[J]. 中国医学影像学杂志, 2023, 31(9): 945-949, 955.
[13] Sasaki, A., Kai, S., Iwashita, Y., Hirano, S., Ohta, M. and Kitano, S. (2005) Microsatellite Distribution and Indication for Locoregional Therapy in Small Hepatocellular Carcinoma. Cancer, 103, 299-306. [Google Scholar] [CrossRef] [PubMed]
[14] Ng, I.O., Lai, E.C., Fan, S.T., Ng, M.M. and So, M.K. (1995) Prognostic Significance of Pathologic Features of Hepatocellular Carcinoma a Multivariate Analysis of 278 Patients. Cancer, 76, 2443-2448. [Google Scholar] [CrossRef
[15] Wu, M., Tan, H., Gao, F., et al. (2019) Predicting the Grade of Hepatocellular Carcinoma Based on Non-Contrast- Enhanced MRI Radiomics Signature. European Radiology, 29, 2802-2811. [Google Scholar] [CrossRef] [PubMed]
[16] Oh, J., Lee, J.M., Park, J., et al. (2019) Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean Journal of Radiology, 20, 569-579. [Google Scholar] [CrossRef] [PubMed]
[17] Peng, J., Kang, S., Ning, Z., et al. (2020) Residual Convolutional Neu-ral Network for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma from CT Imaging. European Radiology, 30, 413-424. [Google Scholar] [CrossRef] [PubMed]
[18] Mulé, S., Thiefin, G., Costentin, C., et al. (2018) Advanced Hepatocellular Carcinoma: Pretreatment Contrast-Enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib. Radiology, 288, 445-455. [Google Scholar] [CrossRef] [PubMed]
[19] Sun, R., Limkin, E.J., Vakalopoulou, M., et al. (2018) A Radi-omics Approach to Assess Tumour-Infiltrating CD8 Cells and Response to Anti-PD-1 or Anti-PD-L1 Immunotherapy: An Imaging Biomarker, Retrospective Multicohort Study. The Lancet Oncology, 19, 1180-1191. [Google Scholar] [CrossRef
[20] Lee, S., Kim, S.H., Lee, J.E., Sinn, D.H. and Park, C.K. (2017) Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Microvascular Invasion in Patients with Single Hepa-tocellular Carcinoma. Journal of Hepatology, 67, 526-534. [Google Scholar] [CrossRef] [PubMed]
[21] Li, Z., Lei, Z., Xia, Y., et al. (2018) Association of Preoperative Antiviral Treatment with Incidences of Microvascular Invasion and Early Tumor Recurrence in Hepatitis B Virus-Related Hepatocellular Carcinoma. JAMA Surgery, 153, e182721. [Google Scholar] [CrossRef] [PubMed]
[22] Wang, H., Wu, M.C. and Cong, W.M. (2019) Microvascular Invasion Predicts a Poor Prognosis of Solitary Hepatocellular Car-cinoma up to 2cm Based on Propensity Score Matching Analysis. Hepatology Research, 49, 344-354. [Google Scholar] [CrossRef] [PubMed]
[23] Zhou, Y., He, L., Huang, Y., et al. (2017) CT-Based Radiomics Signature: A Potential Biomarker for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma. Abdominal Radiol-ogy, 42, 1695-1704. [Google Scholar] [CrossRef] [PubMed]
[24] Taketomi, A., Fukuhara, T., Morita, K., et al. (2010) Improved Results of a Surgical Resection for the Recurrence of Hepatocellular Carcinoma after Living Donor Liver Transplantation. Annals of Surgical Oncology, 17, 2283-2289. [Google Scholar] [CrossRef] [PubMed]
[25] Ivanics, T., Salinas-Miranda, E., Abreu, P., et al. (2021) A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker. Transplantation, 105, 2435-2444. [Google Scholar] [CrossRef
[26] Guo, D., Gu, D., Wang, H., et al. (2019) Radiomics Analysis Enables Recurrence Prediction for Hepatocellular Carcinoma after Liver Transplantation. European Journal of Radiology, 117, 33-40. [Google Scholar] [CrossRef] [PubMed]
[27] Segal, E., Sirlin, C.B., Ooi, C., et al. (2007) Decoding Global Gene Expression Programs in Liver Cancer by Noninvasive Imaging. Nature Biotechnology, 25, 675-680. [Google Scholar] [CrossRef] [PubMed]
[28] Saini, A., Breen, I., Pershad, Y., et al. (2018) Radiogenomics and Radiomics in Liver Cancers. Diagnostics, 9, Article 4. [Google Scholar] [CrossRef] [PubMed]
[29] Hectors, S.J., Lewis, S., Besa, C., et al. (2020) MRI Radiomics Features Predict Immuno-Oncological Characteristics of Hepatocellular Carci-noma. European Radiology, 30, 3759-3769. [Google Scholar] [CrossRef] [PubMed]
[30] Gillies, R.J., Ki-nahan, P.E. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577. [Google Scholar] [CrossRef] [PubMed]