Application of Radiomics in TACE Treatment of Hepatocellular Carcinoma

doi:10.7517/issn.1674-0475.230905

Abstract

Abstract: In recent years, the application of radiomics in personalized diagnosis, treatment plan selection, efficacy and prognosis evaluation of various benign and malignant tumors has developed rapidly, becoming a new direction and research hotspot in the field of imaging medicine. Imaging omics is the process of transforming image features into data features, exploring the relationship between images and tissue pathology and physiology through a large amount of data features, in order to achieve the goals of disease diagnosis, prognosis evaluation, and so on. This article mainly elaborates on the application of radiomics in the treatment of transcatheter hepatic artery chemoembolization in hepatocellular carcinoma patients, including the prediction of postoperative efficacy of radiomics in TACE, screening of patients who are not suitable for TACE treatment, limitations of current radiomics application in TACE treatment of HCC patients, and future prospects and research directions.

Key words: radiomics, hepatocellular carcinoma, transcatheter arterial chemoembolization, prognostic prediction, efficacy prediction

SHUAI Shi, TANG Yanlong. Application of Radiomics in TACE Treatment of Hepatocellular Carcinoma[J]. IMAGING SCIENCE AND PHOTOCHEMISTRY, 2024, 42(1): 71-76.

References

[1] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA:A Cancer Journal for Clinicians, 2018, 68(6):394-424.
[2] Llovet J M, Bruix J. Systematic review of randomized trials for unresectable hepatocellular carcinoma:Chemoembolization improves survival[J]. Hepatology (Baltimore, Md.), 2003, 37(2):429-442.
[3] Raoul J-L, Sangro B, Forner A, et al. Evolving strategies for the management of intermediate-stage hepatocellular carcinoma:Available evidence and expert opinion on the use of transarterial chemoembolization[J]. Cancer Treatment Reviews, 2011, 37(3):212-220.
[4] Lencioni R, de Baere T, Soulen M C, et al. Lipiodol transarterial chemoembolization for hepatocellular carcinoma:A systematic review of efficacy and safety data[J]. Hepatology (Baltimore, Md.), 2016, 64(1):106-116.
[5] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics:Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer (Oxford, England:1990), 2012, 48(4):441-446.
[6] 隋赫,莫展豪,孙旭,等.影像组学的图像分析及模型构建[J].中国医疗设备, 2019, 34(4):25-28,37.
[7] Gao X F, Ran X, Ding W. The progress of radiomics in thyroid nodules[J]. Frontiers in Oncology, 2023, 13:1109319.
[8] Xu X S, Qu J Q, Zhang Y J, et al. Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma[J]. Frontiers in Oncology, 2023, 13:1074445.
[9] Calimano-Ramirez L F, Virarkar M K, Hernandez M, et al. MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer:A systematic review[J]. Abdominal Radiology (New York), 2023, 48(7):2379-2400.
[10] White L M, Atinga A, Naraghi A M, et al. T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma:Predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival[J]. Skeletal Radiology, 2023, 52(3):553-564.
[11] 苏会芳,周国锋,谢传淼,等.放射组学的兴起和研究进展[J].中华医学杂志, 2015, 95(7):553-556.
[12] 李双双,侯震,刘娟,等.影像组学分析与建模工具综述[J].中国医学物理学杂志, 2018, 35(9):1043-1049.
[13] 胡文墨,杨华瑜,毛一雷.基于人工智能的影像组学在肝脏疾病中的应用[J].中华普通外科杂志, 2019, 34(7):646-648.
[14] 中华人民共和国国家卫生健康委员会.原发性肝癌诊疗指南(2022年版)[J].肿瘤综合治疗电子杂志, 2022, 8(2):251-276.
[15] European Association for the Study of the Liver. EASL clinical practice guidelines:Management of hepatocellular carcinoma[J]. Journal of Hepatology, 2018, 69(1):182-236.
[16] Ferreira Junior J R Jr, Koenigkam-Santos M, Machado C V B, et al. Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity[J]. Radiologia Brasileira, 2021, 54(2):87-93.
[17] Tsurusaki M, Murakami T. Surgical and locoregional therapy of HCC:TACE[J]. Liver Cancer, 2015, 4(3):165-175.
[18] Kim J, Choi S J, Lee S H, et al. Predicting survival using pretreatment CT for patients with hepatocellular carcinoma treated with transarterial chemoembolization:Comparison of models using radiomics[J]. American Journal of Roentgenology, 2018, 211(5):1026-1034.
[19] 刘颖,魏小琴,王芳,等.基于CT影像组学预测肝细胞癌TACE术后疗效[J].临床放射学杂志, 2023, 42(2):311-316.
[20] Meng X P, Wang Y C, Ju S H, et al. Radiomics analysis on multiphase contrast-enhanced CT:A survival prediction tool in patients with hepatocellular carcinoma undergoing transarterial chemoembolization[J]. Frontiers in Oncology, 2020, 10:1196.
[21] Li L L, Kan X F, Zhao Y J, et al. Radiomics Signature:A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma[J]. International Journal of Medical Sciences, 2021, 18(11):2276-2284.
[22] Guo Z, Zhong N Y, Xu X M, et al. Prediction of hepatocellular carcinoma response to transcatheter arterial chemoembolization:A real-world study based on non-contrast computed tomography radiomics and general image features[J]. Journal of Hepatocellular Carcinoma, 2021, 8:773-782.
[23] Sheen H S, Kim J S, Lee J K, et al. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion[J]. Abdominal Radiology (New York), 2021, 46(6):2839-2849.
[24] Wang D D, Zhang J F, Zhang L H, et al. Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma:A multicenter study[J]. Hepatobiliary&Pancreatic Diseases International, 2023, 22(6):594-604.
[25] Niu X K, He X F. Development of a computed tomography-based radiomics nomogram for prediction of transarterial chemoembolization refractoriness in hepatocellular carcinoma[J]. World Journal of Gastroenterology, 2021, 27(2):189-207.
[26] Jin Z C, Chen L, Zhong B Y, et al. Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy:A multicenter study[J].Translational Oncology, 2021, 14(4):101034.
[27] Song W L, Yu X L, Guo D J, et al. MRI-based radiomics:Associations with the recurrence-free survival of patients with hepatocellular carcinoma treated with conventional transcatheter arterial chemoembolization[J]. Journal of Magnetic Resonance Imaging, 2020, 52(2):461-473.
[28] Zhao Y, Wang N, Wu J J, et al. Radiomics analysis based on contrast-enhanced MRI for prediction of therapeutic response to transarterial chemoembolization in hepatocellular carcinoma[J]. Frontiers in Oncology, 2021, 11:582788.
[29] Zhao Y, Huang F, Liu S Y, et al. Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score[J]. Journal of Cancer Research and Clinical Oncology, 2023, 149(8):5181-5192.
[30] Sun Y J, Bai H L, Xia W, et al. Predicting the outcome of transcatheter arterial embolization therapy for unresectable hepatocellular carcinoma based on radiomics of preoperative multiparameter MRI[J]. Journal of Magnetic Resonance Imaging, 2020, 52(4):1083-1090.
[31] Fang S J, Lai L Q, Zhu J Y, et al. A radiomics signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization plus radiofrequency ablation[J]. Frontiers in Molecular Biosciences, 2021, 8:662366.
[32] Wang F, Chen Q Q, Zhang Y Y, et al. CT-based radiomics for the recurrence prediction of hepatocellular carcinoma after surgical resection[J]. Journal of Hepatocellular Carcinoma, 2022, 9:453-465.
[33] Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology[J]. Nature Reviews Cancer, 2018, 18(8):500-510.
[34] Peng J, Huang J H, Huang G J, et al. Predicting the initial treatment response to transarterial chemoembolization in intermediate-stage hepatocellular carcinoma by the integration of radiomics and deep learning[J]. Frontiers in Oncology, 2021, 11:730282.
[35] nce O,Önder H, Gençtürk M, et al. Prediction of response of hepatocellular carcinoma to radioembolization:machine learning using preprocedural clinical factors and MR imaging radiomics[J]. Journal of Vascular and Interventional Radiology, 2023, 34(2):235-243.e3.