Radiomics in diagnosis and treatment of hepatocellular carcinoma_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

GULIZAINA Hapaer ¹ , CHE Feng ¹ , LI Qian ¹ , WEI Yi ¹ , HUANG Zixing ¹ ,  SONG Bin ^1,2

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Radiology, Sanya People’s Hospital, Sanya, Hainan Province 572000, P. R. China;

Corresponding author：

SONG Bin, Email: songlab_radiology@163.com

Keywords：

hepatocellular carcinoma; radiomics; diagnosis; treatment

DOI：

10.7507/1007-9424.202409073

Video：

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Objective To summarize the progress of radiomics in the diagnosis and treatment of hepatocellular carcinoma and discuss its future direction, limitations and challenges. Method We retrieved the literature related to radiomics in the diagnosis and treatment of hepatocellular carcinoma and made a review. Results Traditional hepatocellular carcinoma imaging examination, diagnosis and differential diagnosis had certain limitations. Radiomics as an emerging technology, it helped extract tissue biological information that could not be detected by the naked eye from high-throughput quantitative images and transform into high-dimensional qualitative quantitative data, and either alone or in combination with other clinical and molecular data such as demographics, histology, genomics or proteomics or other clinical and molecular data to solve clinical problems, such as hepatocellular carcinoma diagnosis and differential diagnosis, staging and grading, therapeutic regimen development and predicting prognosis and survival after therapy, etc. At present, there were still several problems to be solved in radiomics, such as insufficient interpretability of the combined artificial intelligence-medical imaging approach, lack of uniform standards and lack of external validation, etc.Conclusions The study of radiomics in the diagnosis and treatment of hepatocellular carcinoma has been deepened and expanded to different degrees with great potential and application prospects. Radiomics brings greater benefits to the diagnosis, treatment and management of hepatocellular carcinoma patients, provides a new direction for optimizing medical decision-making and promoting the development of precision medicine. However, there are still some deficiencies and challenges to overcome in the radiomics technology and methods, which require extensive validation and optimization through further clinical trials.

Citation： GULIZAINA Hapaer, CHE Feng, LI Qian, WEI Yi, HUANG Zixing, SONG Bin. Radiomics in diagnosis and treatment of hepatocellular carcinoma. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(1): 60-66. doi: 10.7507/1007-9424.202409073 Copy

1.	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2.	郝运, 李川, 文天夫, 等. 全球及中国的肝癌流行病学特征: 基于《2022全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(7): 781-789.
3.	姚一菲, 孙可欣, 郑荣寿. 《2022全球癌症统计报告》解读: 中国与全球对比. 中国普外基础与临床杂志, 2024, 31(7): 769-780.
4.	Roberts LR, Sirlin CB, Zaiem F, et al. Imaging for the diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis. Hepatology, 2018, 67(1): 401-421.
5.	Yang JD, Heimbach JK. New advances in the diagnosis and management of hepatocellular carcinoma. BMJ, 2020, 371: m3544. doi: 10.1136/bmj.m3544.
6.	Yao S, Ye Z, Wei Y, et al. Radiomics in hepatocellular carcinoma: a state-of-the-art review. World J Gastrointest Oncol, 2021, 13(11): 1599-1615.
7.	Tian G, Yang S, Yuan J, et al. Comparative efficacy of treatment strategies for hepatocellular carcinoma: systematic review and network meta-analysis. BMJ Open, 2018, 8(10): e021269. doi: 10.1136/bmjopen-2017-021269.
8.	Hricak H. Oncologic imaging: a guiding hand of personalized cancer care. Radiology, 2011, 259(3): 633-640.
9.	Yarchoan M, Agarwal P, Villanueva A, et al. Correction: recent developments and therapeutic strategies against hepatocellular carcinoma. Cancer Res, 2019, 79(22): 5897. doi: 10.1158/0008-5472.CAN-19-2958.
10.	梁寻杰, 覃小珊, 黄赞松. 肝癌预后影响因素研究进展. 右江民族医学院学报, 2020, 42(5): 642-645.
11.	Di Tommaso L, Spadaccini M, Donadon M, et al. Role of liver biopsy in hepatocellular carcinoma. World J Gastroenterol, 2019, 25(40): 6041-6052.
12.	Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006. doi: 10.1038/ncomms5006.
13.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
14.	Medavaram S, Zhang Y. Emerging therapies in advanced hepatocellular carcinoma. Exp Hematol Oncol, 2018, 7: 17. doi: 10.1186/s40164-018-0109-6.
15.	Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med, 2020, 61(4): 488-495.
16.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
17.	Lv K, Cao X, Du P, et al. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma. World J Gastroenterol, 2022, 28(20): 2176-2183.
18.	Zhou HY, Cheng JM, Chen TW, et al. CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Clinics (Sao Paulo), 2023, 78: 100264. doi: 10.1016/j.clinsp.2023.100264.
19.	Hu HT, Wang Z, Huang XW, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol, 2019, 29(6): 2890-2901.
20.	Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. Radiol Med, 2021, 126(10): 1296-1311.
21.	Chen CI, Lu NH, Huang YH, et al. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks. J Xray Sci Technol, 2022, 30(5): 953-966.
22.	Massoptier L, Casciaro S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol, 2008, 18(8): 1658-1665.
23.	Wang L, Tan J, Ge Y, et al. Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software. Acta Radiol, 2021, 62(3): 291-301.
24.	Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol, 2016, 61(13): R150-R166.
25.	Yang F, Ford JC, Dogan N, et al. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol, 2018, 7(3): 445-458.
26.	Dobbin KK, Simon RM. Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics, 2011, 4: 31. doi: 10.1186/1755-8794-4-31.
27.	Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087. doi: 10.1038/srep13087.
28.	Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018, 2(1): 36. doi: 10.1186/s41747-018-0068-z.
29.	Varghese BA, Cen SY, Hwang DH, et al. Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol, 2019, 212(3): 520-528.
30.	Elderkin J, Al Hallak N, Azmi AS, et al. Hepatocellular carcinoma: surveillance, diagnosis, evaluation and management. Cancers (Basel), 2023, 15(21): 5118. doi: 10.3390/cancers15215118.
31.	Harding-Theobald E, Louissaint J, Maraj B, et al. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther, 2021, 54(7): 890-901.
32.	Ding Z, Lin K, Fu J, et al. An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver. World J Surg Oncol, 2021, 19(1): 181. doi: 10.1186/s12957-021-02266-7.
33.	Zhao X, Zhou Y, Zhang Y, et al. Radiomics based on contrast-enhanced MRI in differentiation between fat-poor angiomyolipoma and hepatocellular carcinoma in noncirrhotic liver: a multicenter analysis. Front Oncol, 2021, 11: 744756. doi: 10.3389/fonc.2021.744756.
34.	Nie P, Wang N, Pang J, et al. CT-based radiomics nomogram: a potential tool for differentiating hepatocellular adenoma from hepatocellular carcinoma in the noncirrhotic liver. Acad Radiol, 2021, 28(6): 799-807.
35.	Hu MJ, Yu YX, Fan YF, et al. CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess. Clin Radiol, 2021, 76(2): 161. e111-161. e117.
36.	Mahmoudi S, Bernatz S, Ackermann J, et al. Computed tomography radiomics to differentiate intrahepatic cholangiocarcinoma and hepatocellular carcinoma. Clin Oncol (R Coll Radiol), 2023, 35(5): e312-e318.
37.	Wang X, Wang S, Yin X, et al. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med, 2022, 141: 105058. doi: 10.1016/j.compbiomed.2021.105058.
38.	Ravina M, Mishra A, Kote R, et al. Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases. Nucl Med Commun, 2023, 44(5): 381-389.
39.	Su LY, Xu M, Chen YL, et al. Ultrasomics in liver cancer: developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound. World J Radiol, 2024, 16(7): 247-255.
40.	Martins-Filho SN, Paiva C, Azevedo RS, et al. Histological grading of hepatocellular carcinoma-a systematic review of literature. Front Med (Lausanne), 2017, 4: 193. doi: 10.3389/fmed.2017.00193. eCollection 2017.
41.	Wu C, Du X, Zhang Y, et al. Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma. J Cancer Res Clin Oncol, 2023, 149(16): 15103-15112.
42.	Yan Y, Si Z, Chun C, et al. Multiphase MRI-based radiomics for predicting histological grade of hepatocellular carcinoma. J Magn Reson Imaging, 2024, 60(5): 2117-2127.
43.	Ameli S, Venkatesh BA, Shaghaghi M, et al. Role of MRI-derived radiomics features in determining degree of tumor differentiation of hepatocellular carcinoma. Diagnostics (Basel), 2022, 12(10): 2386. doi: 10.3390/diagnostics12102386.
44.	Brancato V, Garbino N, Salvatore M, et al. MRI-based radiomic features help identify lesions and predict histopathological grade of hepatocellular carcinoma. Diagnostics (Basel), 2022, 12(5): 1085. doi: 10.3390/diagnostics12051085.
45.	Li C, Xu J, Liu Y, et al. Kupffer phase radiomics signature in sonazoid-enhanced ultrasound is an independent and effective predictor of the pathologic grade of hepatocellular carcinoma. J Oncol, 2022, 2022: 6123242. doi: 10.1155/2022/6123242. eCollection 2022.
46.	王恺悌, 巴登才仁·安蕊, 丛赟, 等. 肝细胞癌微血管侵犯术后早期复发的研究进展. 中国普外基础与临床杂志, 2024, 31(11): 1399-1405.
47.	Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol, 2019, 26(5): 1474-1493.
48.	Lim KC, Chow PK, Allen JC, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg, 2011, 254(1): 108-113.
49.	Sumie S, Nakashima O, Okuda K, et al. The significance of classifying microvascular invasion in patients with hepatocellular carcinoma. Ann Surg Oncol, 2014, 21(3): 1002-1009.
50.	Zhang ZH, Jiang C, Qiang ZY, et al. Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: a literature review. Asian J Surg, 2024, 47(5): 2138-2143.
51.	Hwang S, Lee YJ, Kim KH, et al. The impact of tumor size on long-term survival outcomes after resection of solitary hepatocellular carcinoma: single-institution experience with 2 558 patients. J Gastrointest Surg, 2015, 19(7): 1281-1290.
52.	Mazzaferro V, Llovet JM, Miceli R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol, 2009, 10(1): 35-43.
53.	Omata M, Cheng AL, Kokudo N, et al. Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatol Int, 2017, 11(4): 317-370.
54.	Xia TY, Zhou ZH, Meng XP, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology, 2023, 307(4): e222729. doi: 10.1148/radiol.222729.
55.	Chong HH, Yang L, Sheng RF, et al. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur Radiol, 2021, 31(7): 4824-4838.
56.	Li Y, Zhang Y, Fang Q, et al. Radiomics analysis of [¹⁸F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma. Eur J Nucl Med Mol Imaging, 2021, 48(8): 2599-2614.
57.	Li X, Yao Q, Liu C, et al. Macrotrabecular-massive hepatocellular carcinoma: what should we know? J Hepatocell Carcinoma, 2022, 9: 379-387.
58.	Chai F, Ma Y, Feng C, et al. Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications. Abdom Radiol (NY), 2024, 49(2): 447-457.
59.	Li M, Fan Y, You H, et al. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma. Radiology, 2023, 308(2): e230255. doi: 10.1148/radiol.230255.
60.	Hu S, Kang Y, Xie Y, et al. ¹⁸F-FDG PET/CT-based radiomics nomogram for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma: a two-center study. Abdom Radiol (NY), 2023, 48(2): 532-542.
61.	Luo M, Liu X, Yong J, et al. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma based on B-Mode US and CEUS. Eur Radiol, 2023, 33(6): 4024-4033.
62.	Renne SL, Woo HY, Allegra S, et al. Vessels encapsulating tumor clusters (VETC) is a powerful predictor of aggressive hepatocellular carcinoma. Hepatology, 2020, 71(1): 183-195.
63.	Lu L, Wei W, Huang C, et al. A new horizon in risk stratification of hepatocellular carcinoma by integrating vessels that encapsulate tumor clusters and microvascular invasion. Hepatol Int, 2021, 15(3): 651-662.
64.	Yu Y, Fan Y, Wang X, et al. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur Radiol, 2022, 32(2): 959-970.
65.	Zhang C, Zhong H, Zhao F, et al. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol, 2024, 16(3): 857-874.
66.	Dong X, Yang J, Zhang B, et al. Deep learning radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and prognosis in hepatocellular carcinoma. J Magn Reson Imaging, 2024, 59(1): 108-119.
67.	Luo Y, Ren F, Liu Y, et al. Clinicopathological and prognostic significance of high Ki-67 labeling index in hepatocellular carcinoma patients: a meta-analysis. Int J Clin Exp Med, 2015, 8(7): 10235-10247.
68.	Perou CM, Jeffrey SS, van de Rijn M, et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A, 1999, 96(16): 9212-9217.
69.	Roncalli M, Park YN, Di Tommaso L. Histopathological classification of hepatocellular carcinoma. Dig Liver Dis, 2010, 42 Suppl 3: S228-S234.
70.	Ma Y, Xu R, Liu X, et al. LY3214996 relieves acquired resistance to sorafenib in hepatocellular carcinoma cells. Int J Med Sci, 2021, 18(6): 1456-1464.
71.	Gong XQ, Liu N, Tao YY, et al. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma. Sci Rep, 2023, 13(1): 7710. doi: 10.1038/s41598-023-34763-y.
72.	Che F, Xu Q, Li Q, et al. Radiomics signature: a potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma. World J Gastroenterol, 2022, 28(14): 1479-1493.
73.	Hu X, Wang Q, Huang G, et al. Gadoxetic acid-enhanced MRI-based radiomics signature: a potential imaging biomarker for identifying cytokeratin 19-positive hepatocellular carcinoma. Comput Math Methods Med, 2023, 2023: 5424204. doi: 10.1155/2023/5424204.
74.	Qian H, Shen Z, Zhou D, et al. Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer. Front Oncol, 2023, 13: 1209111. doi: 10.3389/fonc.2023.1209111.
75.	Tabrizian P, Jibara G, Shrager B, et al. Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann Surg, 2015, 261(5): 947-955.
76.	Ji GW, Zhu FP, Xu Q, et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a multi-institutional study. Radiology, 2020, 294(3): 568-579.
77.	Li SQ, Su LL, Xu TF, et al. Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection. World J Gastroenterol, 2023, 29(26): 4186-4199.
78.	Tsurusaki M, Murakami T. Surgical and locoregional therapy of HCC: TACE. Liver Cancer, 2015, 4(3): 165-175.
79.	Zhao Y, Zhang J, Wang N, et al. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma. BMC Cancer, 2023, 23(1): 1026. doi: 10.1186/s12885-023-11491-0.
80.	Shi ZX, Li CF, Zhao LF, et al. Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int, 2024, 23(4): 361-369.
81.	Li Y, Chen J, Weng S, et al. Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression. Clin Radiol, 2019, 74(8): 653. e651-653. e659.
82.	Gillams A, Khan Z, Osborn P, et al. Survival after radiofrequency ablation in 122 patients with inoperable colorectal lung metastases. Cardiovasc Intervent Radiol, 2013, 36(3): 724-730.
83.	Horvat N, Araujo-Filho JAB, Assuncao-Jr AN, et al. Radiomic analysis of MRI to predict sustained complete response after radiofrequency ablation in patients with hepatocellular carcinoma - a pilot study. Clinics (Sao Paulo), 2021, 76: e2888. doi: 10.6061/clinics/2021/e2888.
84.	Zhang X, Wang C, Zheng D, et al. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation. Front Oncol, 2022, 12: 1013770. doi: 10.3389/fonc.2022.1013770.
85.	Zheng BH, Liu LZ, Zhang ZZ, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer, 2018, 18(1): 1148. doi: 10.1186/s12885-018-5024-z.
86.	Schön F, Kieslich A, Nebelung H, et al. Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma. Sci Rep, 2024, 14(1): 590. doi: 10.1038/s41598-023-50451-3.
87.	Ferro M, de Cobelli O, Vartolomei MD, et al. Prostate cancer radiogenomics-from imaging to molecular characterization. Int J Mol Sci, 2021, 22(18): 9971. doi: 10.3390/ijms22189971.
88.	Liu Z, Duan T, Zhang Y, et al. Radiogenomics: a key component of precision cancer medicine. Br J Cancer, 2023, 129(5): 741-753.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. 郝运, 李川, 文天夫, 等. 全球及中国的肝癌流行病学特征: 基于《2022全球癌症统计报告》解读. 中国普外基础与临床杂志, 2024, 31(7): 781-789.
3. 姚一菲, 孙可欣, 郑荣寿. 《2022全球癌症统计报告》解读: 中国与全球对比. 中国普外基础与临床杂志, 2024, 31(7): 769-780.
4. Roberts LR, Sirlin CB, Zaiem F, et al. Imaging for the diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis. Hepatology, 2018, 67(1): 401-421.
5. Yang JD, Heimbach JK. New advances in the diagnosis and management of hepatocellular carcinoma. BMJ, 2020, 371: m3544. doi: 10.1136/bmj.m3544.
6. Yao S, Ye Z, Wei Y, et al. Radiomics in hepatocellular carcinoma: a state-of-the-art review. World J Gastrointest Oncol, 2021, 13(11): 1599-1615.
7. Tian G, Yang S, Yuan J, et al. Comparative efficacy of treatment strategies for hepatocellular carcinoma: systematic review and network meta-analysis. BMJ Open, 2018, 8(10): e021269. doi: 10.1136/bmjopen-2017-021269.
8. Hricak H. Oncologic imaging: a guiding hand of personalized cancer care. Radiology, 2011, 259(3): 633-640.
9. Yarchoan M, Agarwal P, Villanueva A, et al. Correction: recent developments and therapeutic strategies against hepatocellular carcinoma. Cancer Res, 2019, 79(22): 5897. doi: 10.1158/0008-5472.CAN-19-2958.
10. 梁寻杰, 覃小珊, 黄赞松. 肝癌预后影响因素研究进展. 右江民族医学院学报, 2020, 42(5): 642-645.
11. Di Tommaso L, Spadaccini M, Donadon M, et al. Role of liver biopsy in hepatocellular carcinoma. World J Gastroenterol, 2019, 25(40): 6041-6052.
12. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006. doi: 10.1038/ncomms5006.
13. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
14. Medavaram S, Zhang Y. Emerging therapies in advanced hepatocellular carcinoma. Exp Hematol Oncol, 2018, 7: 17. doi: 10.1186/s40164-018-0109-6.
15. Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med, 2020, 61(4): 488-495.
16. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
17. Lv K, Cao X, Du P, et al. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma. World J Gastroenterol, 2022, 28(20): 2176-2183.
18. Zhou HY, Cheng JM, Chen TW, et al. CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Clinics (Sao Paulo), 2023, 78: 100264. doi: 10.1016/j.clinsp.2023.100264.
19. Hu HT, Wang Z, Huang XW, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol, 2019, 29(6): 2890-2901.
20. Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics. Radiol Med, 2021, 126(10): 1296-1311.
21. Chen CI, Lu NH, Huang YH, et al. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks. J Xray Sci Technol, 2022, 30(5): 953-966.
22. Massoptier L, Casciaro S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur Radiol, 2008, 18(8): 1658-1665.
23. Wang L, Tan J, Ge Y, et al. Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software. Acta Radiol, 2021, 62(3): 291-301.
24. Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol, 2016, 61(13): R150-R166.
25. Yang F, Ford JC, Dogan N, et al. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol, 2018, 7(3): 445-458.
26. Dobbin KK, Simon RM. Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics, 2011, 4: 31. doi: 10.1186/1755-8794-4-31.
27. Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep, 2015, 5: 13087. doi: 10.1038/srep13087.
28. Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018, 2(1): 36. doi: 10.1186/s41747-018-0068-z.
29. Varghese BA, Cen SY, Hwang DH, et al. Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol, 2019, 212(3): 520-528.
30. Elderkin J, Al Hallak N, Azmi AS, et al. Hepatocellular carcinoma: surveillance, diagnosis, evaluation and management. Cancers (Basel), 2023, 15(21): 5118. doi: 10.3390/cancers15215118.
31. Harding-Theobald E, Louissaint J, Maraj B, et al. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther, 2021, 54(7): 890-901.
32. Ding Z, Lin K, Fu J, et al. An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver. World J Surg Oncol, 2021, 19(1): 181. doi: 10.1186/s12957-021-02266-7.
33. Zhao X, Zhou Y, Zhang Y, et al. Radiomics based on contrast-enhanced MRI in differentiation between fat-poor angiomyolipoma and hepatocellular carcinoma in noncirrhotic liver: a multicenter analysis. Front Oncol, 2021, 11: 744756. doi: 10.3389/fonc.2021.744756.
34. Nie P, Wang N, Pang J, et al. CT-based radiomics nomogram: a potential tool for differentiating hepatocellular adenoma from hepatocellular carcinoma in the noncirrhotic liver. Acad Radiol, 2021, 28(6): 799-807.
35. Hu MJ, Yu YX, Fan YF, et al. CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess. Clin Radiol, 2021, 76(2): 161. e111-161. e117.
36. Mahmoudi S, Bernatz S, Ackermann J, et al. Computed tomography radiomics to differentiate intrahepatic cholangiocarcinoma and hepatocellular carcinoma. Clin Oncol (R Coll Radiol), 2023, 35(5): e312-e318.
37. Wang X, Wang S, Yin X, et al. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med, 2022, 141: 105058. doi: 10.1016/j.compbiomed.2021.105058.
38. Ravina M, Mishra A, Kote R, et al. Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases. Nucl Med Commun, 2023, 44(5): 381-389.
39. Su LY, Xu M, Chen YL, et al. Ultrasomics in liver cancer: developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound. World J Radiol, 2024, 16(7): 247-255.
40. Martins-Filho SN, Paiva C, Azevedo RS, et al. Histological grading of hepatocellular carcinoma-a systematic review of literature. Front Med (Lausanne), 2017, 4: 193. doi: 10.3389/fmed.2017.00193. eCollection 2017.
41. Wu C, Du X, Zhang Y, et al. Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma. J Cancer Res Clin Oncol, 2023, 149(16): 15103-15112.
42. Yan Y, Si Z, Chun C, et al. Multiphase MRI-based radiomics for predicting histological grade of hepatocellular carcinoma. J Magn Reson Imaging, 2024, 60(5): 2117-2127.
43. Ameli S, Venkatesh BA, Shaghaghi M, et al. Role of MRI-derived radiomics features in determining degree of tumor differentiation of hepatocellular carcinoma. Diagnostics (Basel), 2022, 12(10): 2386. doi: 10.3390/diagnostics12102386.
44. Brancato V, Garbino N, Salvatore M, et al. MRI-based radiomic features help identify lesions and predict histopathological grade of hepatocellular carcinoma. Diagnostics (Basel), 2022, 12(5): 1085. doi: 10.3390/diagnostics12051085.
45. Li C, Xu J, Liu Y, et al. Kupffer phase radiomics signature in sonazoid-enhanced ultrasound is an independent and effective predictor of the pathologic grade of hepatocellular carcinoma. J Oncol, 2022, 2022: 6123242. doi: 10.1155/2022/6123242. eCollection 2022.
46. 王恺悌, 巴登才仁·安蕊, 丛赟, 等. 肝细胞癌微血管侵犯术后早期复发的研究进展. 中国普外基础与临床杂志, 2024, 31(11): 1399-1405.
47. Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol, 2019, 26(5): 1474-1493.
48. Lim KC, Chow PK, Allen JC, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg, 2011, 254(1): 108-113.
49. Sumie S, Nakashima O, Okuda K, et al. The significance of classifying microvascular invasion in patients with hepatocellular carcinoma. Ann Surg Oncol, 2014, 21(3): 1002-1009.
50. Zhang ZH, Jiang C, Qiang ZY, et al. Role of microvascular invasion in early recurrence of hepatocellular carcinoma after liver resection: a literature review. Asian J Surg, 2024, 47(5): 2138-2143.
51. Hwang S, Lee YJ, Kim KH, et al. The impact of tumor size on long-term survival outcomes after resection of solitary hepatocellular carcinoma: single-institution experience with 2 558 patients. J Gastrointest Surg, 2015, 19(7): 1281-1290.
52. Mazzaferro V, Llovet JM, Miceli R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol, 2009, 10(1): 35-43.
53. Omata M, Cheng AL, Kokudo N, et al. Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatol Int, 2017, 11(4): 317-370.
54. Xia TY, Zhou ZH, Meng XP, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology, 2023, 307(4): e222729. doi: 10.1148/radiol.222729.
55. Chong HH, Yang L, Sheng RF, et al. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur Radiol, 2021, 31(7): 4824-4838.
56. Li Y, Zhang Y, Fang Q, et al. Radiomics analysis of [¹⁸F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma. Eur J Nucl Med Mol Imaging, 2021, 48(8): 2599-2614.
57. Li X, Yao Q, Liu C, et al. Macrotrabecular-massive hepatocellular carcinoma: what should we know? J Hepatocell Carcinoma, 2022, 9: 379-387.
58. Chai F, Ma Y, Feng C, et al. Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications. Abdom Radiol (NY), 2024, 49(2): 447-457.
59. Li M, Fan Y, You H, et al. Dual-energy CT deep learning radiomics to predict macrotrabecular-massive hepatocellular carcinoma. Radiology, 2023, 308(2): e230255. doi: 10.1148/radiol.230255.
60. Hu S, Kang Y, Xie Y, et al. ¹⁸F-FDG PET/CT-based radiomics nomogram for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma: a two-center study. Abdom Radiol (NY), 2023, 48(2): 532-542.
61. Luo M, Liu X, Yong J, et al. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma based on B-Mode US and CEUS. Eur Radiol, 2023, 33(6): 4024-4033.
62. Renne SL, Woo HY, Allegra S, et al. Vessels encapsulating tumor clusters (VETC) is a powerful predictor of aggressive hepatocellular carcinoma. Hepatology, 2020, 71(1): 183-195.
63. Lu L, Wei W, Huang C, et al. A new horizon in risk stratification of hepatocellular carcinoma by integrating vessels that encapsulate tumor clusters and microvascular invasion. Hepatol Int, 2021, 15(3): 651-662.
64. Yu Y, Fan Y, Wang X, et al. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur Radiol, 2022, 32(2): 959-970.
65. Zhang C, Zhong H, Zhao F, et al. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol, 2024, 16(3): 857-874.
66. Dong X, Yang J, Zhang B, et al. Deep learning radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and prognosis in hepatocellular carcinoma. J Magn Reson Imaging, 2024, 59(1): 108-119.
67. Luo Y, Ren F, Liu Y, et al. Clinicopathological and prognostic significance of high Ki-67 labeling index in hepatocellular carcinoma patients: a meta-analysis. Int J Clin Exp Med, 2015, 8(7): 10235-10247.
68. Perou CM, Jeffrey SS, van de Rijn M, et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A, 1999, 96(16): 9212-9217.
69. Roncalli M, Park YN, Di Tommaso L. Histopathological classification of hepatocellular carcinoma. Dig Liver Dis, 2010, 42 Suppl 3: S228-S234.
70. Ma Y, Xu R, Liu X, et al. LY3214996 relieves acquired resistance to sorafenib in hepatocellular carcinoma cells. Int J Med Sci, 2021, 18(6): 1456-1464.
71. Gong XQ, Liu N, Tao YY, et al. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma. Sci Rep, 2023, 13(1): 7710. doi: 10.1038/s41598-023-34763-y.
72. Che F, Xu Q, Li Q, et al. Radiomics signature: a potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma. World J Gastroenterol, 2022, 28(14): 1479-1493.
73. Hu X, Wang Q, Huang G, et al. Gadoxetic acid-enhanced MRI-based radiomics signature: a potential imaging biomarker for identifying cytokeratin 19-positive hepatocellular carcinoma. Comput Math Methods Med, 2023, 2023: 5424204. doi: 10.1155/2023/5424204.
74. Qian H, Shen Z, Zhou D, et al. Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer. Front Oncol, 2023, 13: 1209111. doi: 10.3389/fonc.2023.1209111.
75. Tabrizian P, Jibara G, Shrager B, et al. Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann Surg, 2015, 261(5): 947-955.
76. Ji GW, Zhu FP, Xu Q, et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a multi-institutional study. Radiology, 2020, 294(3): 568-579.
77. Li SQ, Su LL, Xu TF, et al. Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection. World J Gastroenterol, 2023, 29(26): 4186-4199.
78. Tsurusaki M, Murakami T. Surgical and locoregional therapy of HCC: TACE. Liver Cancer, 2015, 4(3): 165-175.
79. Zhao Y, Zhang J, Wang N, et al. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma. BMC Cancer, 2023, 23(1): 1026. doi: 10.1186/s12885-023-11491-0.
80. Shi ZX, Li CF, Zhao LF, et al. Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int, 2024, 23(4): 361-369.
81. Li Y, Chen J, Weng S, et al. Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression. Clin Radiol, 2019, 74(8): 653. e651-653. e659.
82. Gillams A, Khan Z, Osborn P, et al. Survival after radiofrequency ablation in 122 patients with inoperable colorectal lung metastases. Cardiovasc Intervent Radiol, 2013, 36(3): 724-730.
83. Horvat N, Araujo-Filho JAB, Assuncao-Jr AN, et al. Radiomic analysis of MRI to predict sustained complete response after radiofrequency ablation in patients with hepatocellular carcinoma - a pilot study. Clinics (Sao Paulo), 2021, 76: e2888. doi: 10.6061/clinics/2021/e2888.
84. Zhang X, Wang C, Zheng D, et al. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation. Front Oncol, 2022, 12: 1013770. doi: 10.3389/fonc.2022.1013770.
85. Zheng BH, Liu LZ, Zhang ZZ, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer, 2018, 18(1): 1148. doi: 10.1186/s12885-018-5024-z.
86. Schön F, Kieslich A, Nebelung H, et al. Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma. Sci Rep, 2024, 14(1): 590. doi: 10.1038/s41598-023-50451-3.
87. Ferro M, de Cobelli O, Vartolomei MD, et al. Prostate cancer radiogenomics-from imaging to molecular characterization. Int J Mol Sci, 2021, 22(18): 9971. doi: 10.3390/ijms22189971.
88. Liu Z, Duan T, Zhang Y, et al. Radiogenomics: a key component of precision cancer medicine. Br J Cancer, 2023, 129(5): 741-753.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Radiomics in diagnosis and treatment of hepatocellular carcinoma

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