REFERENCES
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;74:229-63.
2. Singal AG, Llovet JM, Yarchoan M, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78:1922-65.
3. European Association for the Study of the Liver. EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma. J Hepatol. 2025;82:315-74.
4. Chernyak V, Fowler KJ, Kamaya A, et al. Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients. Radiology. 2018;289:816-30.
5. Kamaya A, Fetzer DT, Seow JH, et al. LI-RADS US surveillance version 2024 for surveillance of hepatocellular carcinoma: an update to the american college of radiology US LI-RADS. Radiology. 2024;313:e240169.
6. Aslam A, Chernyak V, Tang A, et al. CT/MRI LI-RADS 2024 update: treatment response assessment. Radiology. 2024;313:e232408.
7. Lyshchik A, Fetzer DT, Kono Y, et al. Liver Imaging Reporting and Data System contrast-enhanced US nonradiation treatment response assessment version 2024. Radiology. 2024;311:e232369.
8. Choi SH, Fowler KJ, Chernyak V, Sirlin CB. LI-RADS: current status and future directions. Korean J Radiol. 2024;25:1039-46.
9. Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis. 2010;30:52-60.
10. Koo E, Singal AG. Hepatocellular carcinoma surveillance: evidence-based tailored approach. Surg Oncol Clin N Am. 2024;33:13-28.
11. Tzartzeva K, Obi J, Rich NE, et al. Surveillance imaging and alpha fetoprotein for early detection of hepatocellular carcinoma in patients with cirrhosis: a meta-analysis. Gastroenterology. 2018;154:1706-18.e1.
12. Kudo M. Defect reperfusion imaging with sonazoid®: a breakthrough in hepatocellular carcinoma. Liver Cancer. 2016;5:1-7.
13. Aziz MU, Eisenbrey JR, Deganello A, et al. Microvascular flow imaging: a state-of-the-art review of clinical use and promise. Radiology. 2022;305:250-64.
14. Wilson A, Lim AKP. Microvascular imaging: new Doppler technology for assessing focal liver lesions. Is it useful? Clin Radiol. 2022;77:e807-20.
15. Jeon SK, Lee JY, Kang HJ, Han JK. Additional value of superb microvascular imaging of ultrasound examinations to evaluate focal liver lesions. Eur J Radiol. 2022;152:110332.
16. Dietrich CF, Nolsøe CP, Barr RG, et al. Guidelines and Good Clinical Practice Recommendations for contrast enhanced ultrasound (CEUS) in the liver - update 2020 - WFUMB in cooperation with EFSUMB, AFSUMB, AIUM, and FLAUS. Ultraschall Med. 2020;41:562-85.
17. D’Onofrio M, Crosara S, De Robertis R, Canestrini S, Mucelli RP. Contrast-enhanced ultrasound of focal liver lesions. AJR Am J Roentgenol. 2015;205:W56-66.
18. Zhang Z, Ma C, Luo Y. Diagnostic value of liver contrast-enhanced ultrasound in early hepatocellular carcinoma: a systematic review and meta-analysis. J Gastrointest Oncol. 2023;14:626-35.
19. Liu Q, Liu T, Liu X, et al. The efficacy of modified contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) using Sonazoid in diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis. Quant Imaging Med Surg. 2024;14:2927-37.
20. Makoyeva A, Kim TK, Jang HJ, Medellin A, Wilson SR. Use of CEUS LI-RADS for the accurate diagnosis of nodules in patients at risk for hepatocellular carcinoma: a validation study. Radiol Imaging Cancer. 2020;2:e190014.
21. Hawley J, Tang Y, Sjöström A, Fuentes-Alburo A, Tranquart F. The clinical utility of liver-specific ultrasound contrast agents during hepatocellular carcinoma imaging. Ultrasound Med Biol. 2025;51:415-27.
22. Luo Y, Huang Q, Wen D, Yan J, Liu F, Qiao L. Review of clinical applications of sonazoid ultrasound contrast for liver evaluation. Ultrasound Q. 2024;40:e00692.
23. Jeong WK, Kang HJ, Choi SH, et al. Diagnosing hepatocellular carcinoma using sonazoid contrast-enhanced ultrasonography: 2023 Guidelines From the Korean Society of Radiology and the Korean Society of Abdominal Radiology. Korean J Radiol. 2023;24:482-97.
24. Huang J, Gao L, Li J, et al. Head-to-head comparison of Sonazoid and SonoVue in the diagnosis of hepatocellular carcinoma for patients at high risk. Front Oncol. 2023;13:1140277.
25. Huang Z, Zhu RH, Li SS, Luo HC, Li KY. Diagnostic performance of Sonazoid-enhanced CEUS in identifying definitive hepatocellular carcinoma in cirrhotic patients according to KLCA-NCC 2022 and APASL 2017 guidelines. Insights Imaging. 2024;15:263.
26. Sandrin L, Fourquet B, Hasquenoph JM, et al. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol. 2003;29:1705-13.
27. Castera L, Forns X, Alberti A. Non-invasive evaluation of liver fibrosis using transient elastography. J Hepatol. 2008;48:835-47.
28. Xu C, Jiang D, Tan B, Shen C, Guo J. Preoperative diagnosis and prediction of microvascular invasion in hepatocellularcarcinoma by ultrasound elastography. BMC Med Imaging. 2022;22:88.
29. Zhong X, Long H, Chen L, et al. Stiffness on shear wave elastography as a potential microenvironment biomarker for predicting tumor recurrence in HBV-related hepatocellular carcinoma. Insights Imaging. 2023;14:147.
30. Ahn SJ, Lee JM, Lee DH, et al. Real-time US-CT/MR fusion imaging for percutaneous radiofrequency ablation of hepatocellular carcinoma. J Hepatol. 2017;66:347-54.
31. Lu YB, Huang YN, Weng YC, et al. Contrast-enhanced ultrasonography guidance avoids US-CT/MR fusion error for percutaneous radiofrequency ablation of hepatocellular carcinoma. BMC Med Imaging. 2024;24:323.
32. Jie T, Guoying F, Gang T, Zhengrong S, Maoping L. Efficacy and safety of fusion imaging in radiofrequency ablation of hepatocellular carcinoma compared to ultrasound: a meta-analysis. Front Surg. 2021;8:728098.
33. Flohr TG, Schaller S, Stierstorfer K, Bruder H, Ohnesorge BM, Schoepf UJ. Multi-detector row CT systems and image-reconstruction techniques. Radiology. 2005;235:756-73.
34. McCollough CH, Bruesewitz MR, Kofler JM Jr. CT dose reduction and dose management tools: overview of available options. Radiographics. 2006;26:503-12.
35. Coursey CA, Nelson RC, Boll DT, et al. Dual-energy multidetector CT: how does it work, what can it tell us, and when can we use it in abdominopelvic imaging? Radiographics 2010;30:1037-55.
36. Asmundo L, Rizzetto F, Srinivas Rao S, et al. Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review. Abdom Radiol. 2024;49:3811-23.
37. Bhayana R, Parakh A, Kambadakone A. Material decomposition with dual- and multi-energy computed tomography. MRS Communications. 2020;10:558-65.
38. Lv P, Lin XZ, Chen K, Gao J. Spectral CT in patients with small HCC: investigation of image quality and diagnostic accuracy. Eur Radiol. 2012;22:2117-24.
39. Reimer RP, Hokamp NG, Niehoff J, et al. Value of spectral detector computed tomography for the early assessment of technique efficacy after microwave ablation of hepatocellular carcinoma. PLoS One. 2021;16:e0252678.
40. Jensen CT, Gupta S, Saleh MM, et al. Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology. 2022;303:90-8.
41. Jaruvongvanich V, Muangsomboon K, Teerasamit W, et al. Optimizing computed tomography image reconstruction for focal hepatic lesions: deep learning image reconstruction vs iterative reconstruction. Heliyon. 2024;10:e34847.
42. Caruso D, De Santis D, Del Gaudio A, et al. Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm. Eur Radiol. 2024;34:2384-93.
43. Onishi H, Tsuboyama T, Nakamoto A, et al. Photon-counting CT: technical features and clinical impact on abdominal imaging. Abdom Radiol. 2024;49:4383-99.
44. Wildman-Tobriner B, Felice N, Kalisz KR, et al. Photon-counting CT effects on sensitivity for liver lesion detection: a reader study using virtual imaging. Radiology. 2025;314:e241568.
45. Kim SH, Kamaya A, Willmann JK. CT perfusion of the liver: principles and applications in oncology. Radiology. 2014;272:322-44.
46. Jiang T, Kambadakone A, Kulkarni NM, Zhu AX, Sahani DV. Monitoring response to antiangiogenic treatment and predicting outcomes in advanced hepatocellular carcinoma using image biomarkers, CT perfusion, tumor density, and tumor size (RECIST). Invest Radiol. 2012;47:11-7.
47. Wallace MJ, Murthy R, Kamat PP, et al. Impact of C-arm CT on hepatic arterial interventions for hepatic malignancies. J Vasc Interv Radiol. 2007;18:1500-7.
48. Tacher V, Radaelli A, Lin M, Geschwind JF. How I do it: Cone-beam CT during transarterial chemoembolization for liver cancer. Radiology. 2015;274:320-34.
49. Pung L, Ahmad M, Mueller K, et al. The role of cone-beam CT in transcatheter arterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis. J Vasc Interv Radiol. 2017;28:334-41.
50. Kim DJ, Chul-Nam I, Park SE, et al. Added value of cone-beam computed tomography for detecting hepatocellular carcinomas and feeding arteries during transcatheter arterial chemoembolization focusing on radiation exposure. Medicina. 2023;59:1121.
51. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 1999;42:952-62.
52. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 2002;47:1202-10.
53. Rofsky NM, Lee VS, Laub G, et al. Abdominal MR imaging with a volumetric interpolated breath-hold examination. Radiology. 1999;212:876-84.
54. Maung ST, Tanpowpong N, Satja M, Treeprasertsuk S, Chaiteerakij R. MRI for hepatocellular carcinoma and the role of abbreviated MRI for surveillance of hepatocellular carcinoma. J Gastroenterol Hepatol. 2024;39:1969-81.
55. Huppertz A, Balzer T, Blakeborough A, et al. ; European EOB Study Group. Improved detection of focal liver lesions at MR imaging: multicenter comparison of gadoxetic acid-enhanced MR images with intraoperative findings. Radiology. 2004;230:266-75.
56. Golfieri R, Renzulli M, Lucidi V, Corcioni B, Trevisani F, Bolondi L. Contribution of the hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI to dynamic MRI in the detection of hypovascular small (≤ 2 cm) HCC in cirrhosis. Eur Radiol. 2011;21:1233-42.
57. Liu X, Jiang H, Chen J, Zhou Y, Huang Z, Song B. Gadoxetic acid disodium-enhanced magnetic resonance imaging outperformed multidetector computed tomography in diagnosing small hepatocellular carcinoma: a meta-analysis. Liver Transpl. 2017;23:1505-18.
59. Woo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology. 2014;270:758-67.
60. Zhou Y, Zheng J, Yang C, et al. Application of intravoxel incoherent motion diffusion-weighted imaging in hepatocellular carcinoma. World J Gastroenterol. 2022;28:3334-45.
61. Wang WT, Yang L, Yang ZX, et al. Assessment of microvascular invasion of hepatocellular carcinoma with diffusion kurtosis imaging. Radiology. 2018;286:571-80.
62. Venkatesh SK, Yin M, Ehman RL. Magnetic resonance elastography of liver: technique, analysis, and clinical applications. J Magn Reson Imaging. 2013;37:544-55.
63. Lee DH, Lee JM, Yi NJ, et al. Hepatic stiffness measurement by using MR elastography: prognostic values after hepatic resection for hepatocellular carcinoma. Eur Radiol. 2017;27:1713-21.
64. Tamaki N, Higuchi M, Keitoku T, et al. Magnetic resonance elastography for the prediction of hepatocellular carcinoma in chronic hepatitis B. JGH Open. 2024;8:e13067.
65. Brunsing RL, Chen DH, Schlein A, et al. Gadoxetate-enhanced abbreviated MRI for hepatocellular carcinoma surveillance: preliminary experience. Radiol Imaging Cancer. 2019;1:e190010.
66. Maung ST, Deepan N, Decharatanachart P, Chaiteerakij R. Abbreviated MRI for hepatocellular carcinoma surveillance - a systematic review and meta-analysis. Acad Radiol. 2024;31:3142-56.
67. Yokoo T, Masaki N, Parikh ND, et al. Multicenter validation of abbreviated MRI for detecting early-stage hepatocellular carcinoma. Radiology. 2023;307:e220917.
68. Yao Y, Civelek AC, Li XF. The application of 18F-FDG PET/CT imaging for human hepatocellular carcinoma: a narrative review. Quant Imaging Med Surg. 2023;13:6268-79.
69. Park JW, Kim JH, Kim SK, et al. A prospective evaluation of 18F-FDG and 11C-acetate PET/CT for detection of primary and metastatic hepatocellular carcinoma. J Nucl Med. 2008;49:1912-21.
70. Mohebbi A, Kiani I, Mohammadzadeh S, Mohammadi A, Tavangar SM. Qualitative and quantitative differentiation efficiency of dual-tracer PET/CT with 18F-fluorodeoxyglucose and 11C-acetate for primary hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol. 2025;50:198-212.
71. Ghidaglia J, Golse N, Pascale A, Sebagh M, Besson FL. 18F-FDG /18F-choline dual-tracer PET behavior and tumor differentiation in hepatocellular carcinoma. A systematic review. Front Med. 2022;9:924824.
72. Zhang J, Jiang S, Li M, et al. Head-to-head comparison of 18F-FAPI and 18F-FDG PET/CT in staging and therapeutic management of hepatocellular carcinoma. Cancer Imaging. 2023;23:106.
73. Semeraro L, Frantellizzi V, Filippi L, Palumbo B, De Vincentis G, De Feo MS. Positron emission tomography with fibroblast activation protein-targeted radiopharmaceuticals in primary hepatic tumors: a systematic review. Appl Sci. 2025;15:2025.
74. Awali M, El Homsi M, Fraum TJ, et al. PET/MRI: pictorial review of hepatobiliary and pancreatic applications. Abdom Radiol. 2025;50:875-901.
75. Goertz RS, Bernatik T, Strobel D, Hahn EG, Haendl T. Software-based quantification of contrast-enhanced ultrasound in focal liver lesions--a feasibility study. Eur J Radiol. 2010;75:e22-6.
76. Tang A, Bashir MR, Corwin MT, et al. ; LI-RADS Evidence Working Group. Evidence supporting LI-RADS major features for CT- and MR imaging-based diagnosis of hepatocellular carcinoma: a systematic review. Radiology. 2018;286:29-48.
77. Lee S, Kim SS, Roh YH, Choi JY, Park MS, Kim MJ. Diagnostic performance of CT/MRI liver imaging reporting and data system v2017 for hepatocellular carcinoma: a systematic review and meta-analysis. Liver Int. 2020;40:1488-97.
78. Lin M, Pellerin O, Bhagat N, et al. Quantitative and volumetric European Association for the Study of the Liver and Response Evaluation Criteria in Solid Tumors measurements: feasibility of a semiautomated software method to assess tumor response after transcatheter arterial chemoembolization. J Vasc Interv Radiol. 2012;23:1629-37.
79. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441-6.
80. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
81. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563-77.
82. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500-10.
83. Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol. 2021;13:2039-51.
84. Xia Y, Zhou J, Xun X, et al. Deep learning for oncologic treatment outcomes and endpoints evaluation from CT scans in liver cancer. NPJ Precis Oncol. 2024;8:263.
85. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328-38.
86. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.
87. Tejani AS, Klontzas ME, Gatti AA, et al. ; CLAIM 2024 Update Panel. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 Update. Radiol Artif Intell. 2024;6:e240300.
88. Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505.
89. Bilic P, Christ P, Li HB, et al. The liver tumor segmentation benchmark (LiTS). Med Image Anal. 2023;84:102680.
90. Gross M, Arora S, Huber S, Kücükkaya AS, Onofrey JA. LiverHccSeg: a publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data Brief. 2023;51:109662.
91. Moawad AW, Morshid A, Khalaf AM, et al. Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation. Sci Data. 2023;10:33.
92. Tacher V, Lin M, Duran R, et al. Comparison of existing response criteria in patients with hepatocellular carcinoma treated with transarterial chemoembolization using a 3D quantitative approach. Radiology. 2016;278:275-84.
93. Xu J, Yin Y, Yang J, et al. Modified quantitative and volumetric response evaluation criteria for patients with hepatocellular carcinoma after transarterial chemoembolization. Front Oncol. 2023;13:957722.
