The assessment of hepatocellular carcinoma tumor microenvironment heterogeneity using multiparametric magnetic resonance imaging: the research progress_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

GAO Feifei ¹ , LI Qian ¹ , ZHANG Tong ¹ , WEI Yi 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 572000, P. R. China;

Corresponding?author：

SONG Bin, Email: songlab_radiology@163.com

Keywords：

multiparametric magnetic resonance imaging; hepatocellular carcinoma; tumor microenvironment; heterogeneity

DOI：

10.7507/1007-9424.202505009

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically summarize the research progress of multiparametric magnetic resonance imaging (mpMRI) in assessing tumor microenvironment (TME) heterogeneity in hepatocellular carcinoma (HCC), and to discuss its future development directions, limitations, and challenges. Methods A comprehensive review was conducted to review domestic and international research progress on the use of mpMRI techniques in evaluating TME heterogeneity in HCC. Results mpMRI techniques can reflect TME heterogeneity features associated with postoperative recurrence in HCC from multiple perspectives, including cellular structure, function, substance metabolism, and neovascularization. These features encompass structural heterogeneity, cellular composition heterogeneity, and metabolic heterogeneity within the TME. mpMRI emerges as a potential tool for TME heterogeneity assessment, offering advantages such as non-invasiveness, absence of radiation exposure, and excellent reproducibility. However, the application of mpMRI in evaluating TME heterogeneity in HCC is still in its preliminary stages. Most studies have not conducted in-depth and systematic explorations of the specific pathological and biological mechanisms closely related to TME heterogeneity when utilizing mpMRI. This limitation significantly restricts the further clinical translation of relevant findings and necessitates further research for confirmation. Conclusions mpMRI hold immense potential and promising application prospects in assessing TME heterogeneity in HCC, offering greater benefits for prognosis evaluation and individualized management of HCC patients. However, further exploration of the related pathological and biological mechanisms is essential to facilitate its clinical translation.

Citation： GAO Feifei, LI Qian, ZHANG Tong, WEI Yi, SONG Bin. The assessment of hepatocellular carcinoma tumor microenvironment heterogeneity using multiparametric magnetic resonance imaging: the research progress. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(12): 1534-1542. doi: 10.7507/1007-9424.202505009 Copy

1.	Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	龐北川, 譚煜煒, 李友偉, 等. 肝細胞癌免疫治療超進展潛在分子機制及預測因素研究進展. 中國普外基礎與臨床雜志, 2025, 32(7): 911-915.
3.	Shim JH, Jun MJ, Han S, et al. Prognostic nomograms for prediction of recurrence and survival after curative liver resection for hepatocellular carcinoma. Ann Surg, 2015, 261(5): 939-946.
4.	Vibert E, Schwartz M, Olthoff KM. Advances in resection and transplantation for hepatocellular carcinoma. J Hepatol, 2020, 72(2): 262-276.
5.	Calderaro J, Ziol M, Paradis V, et al. Molecular and histological correlations in liver cancer. J Hepatol, 2019, 71(3): 616-630.
6.	Kawaoka T, Ando Y, Yamauchi M, et al. Incidence of microsatellite instability-high hepatocellular carcinoma among Japanese patients and response to pembrolizumab. Hepatol Res, 2020, 50(7): 885-888.
7.	Craig AJ, von Felden J, Garcia-Lezana T, et al. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol, 2020, 17(3): 139-152.
8.	Sun Y, Wu L, Zhong Y, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell, 2021, 184(2): 404-421.
9.	Robertson-Tessi M, Gillies RJ, Gatenby RA, et al. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res, 2015, 75(8): 1567-1579.
10.	DeBerardinis RJ, Mancuso A, Daikhin E, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A, 2007, 104(49): 19345-19350.
11.	Klauss M, Mayer P, Maier-Hein K, et al. IVIM-diffusion-MRI for the differentiation of solid benign and malign hypervascular liver lesions-evaluation with two different MR scanners. Eur J Radiol, 2016, 85(7): 1289-1294.
12.	Wei Y, Ye Z, Yuan Y, et al. A new diagnostic criterion with gadoxetic acid-enhanced MRI may improve the diagnostic performance for hepatocellular carcinoma. Liver Cancer, 2020, 9(4): 414-425.
13.	Kim DH, Choi SH, Kim SY, et al. Gadoxetic acid-enhanced MRI of hepatocellular carcinoma: value of washout in transitional and hepatobiliary phases. Radiology, 2019, 291(3): 651-657.
14.	Wei Y, Chen G, Tang H, et al. Improved display of hepatic arterial anatomy using differential subsampling with cartesian ordering (DISCO) with gadoxetic acid-enhanced MRI: comparison with single arterial phase MRI and computed tomographic angiography. J Magn Reson Imaging, 2020, 51(6): 1766-1776.
15.	張韻, 魏毅, 葉錚, 等. 肝細胞癌的影像學診斷及鑒別診斷. 中華放射學雜志, 2020, 54(8): 812-816.
16.	Choi JY, Lee JM, Sirlin CB. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part Ⅱ. Extracellular agents, hepatobiliary agents, and ancillary imaging features. Radiology, 2014, 273(1): 30-50.
17.	Yin Z, Lu X, Cohen Cohen S, et al. A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas. Eur Radiol, 2021, 31(8): 5554-5564.
18.	Li M, Yin Z, Hu B, et al. MR elastography-based shear strain mapping for assessment of microvascular invasion in hepatocellular carcinoma. Eur Radiol, 2022, 32(7): 5024-5032.
19.	Yang ZF, Poon RT. Vascular changes in hepatocellular carcinoma. Anat Rec (Hoboken), 2008, 291(6): 721-734.
20.	Flores A, Asrani SK. Editorial: Magnetic resonance elastography and non-alcoholic fatty liver disease: time for an upgrade? Am J Gastroenterol, 2016, 111(7): 995-996.
21.	Yamashita YI, Imai K, Yusa T, et al. Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: predictors and optimal treatments. Ann Gastroenterol Surg, 2018, 2(3): 197-203.
22.	Atamaniuk V, Hańczyk ?, Chen J, et al. 3D vector MR elastography applications in small organs. Magn Reson Imaging, 2024, 112: 54-62.
23.	Liu G, Shen Z, Chong H, et al. Three-dimensional multifrequency MR elastography for microvascular invasion and prognosis assessment in hepatocellular carcinoma. J Magn Reson Imaging, 2024, 60(6): 2626-2640.
24.	Zhang L, Li M, Zhu J, et al. The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma. Eur Radiol, 2023, 33(6): 4103-4114.
25.	Gao S, Zhang Y, Sun W, et al. Assessment of an MR elastography-based nomogram as a potential imaging biomarker for predicting microvascular invasion of hepatocellular carcinoma. J Magn Reson Imaging, 2023, 58(2): 392-402.
26.	Reiter R, Shahryari M, Tzsch?tzsch H, et al. Influence of fibrosis progression on the viscous properties of in vivo liver tissue elucidated by shear wave dispersion in multifrequency MR elastography. J Mech Behav Biomed Mater, 2021, 121: 104645. doi: 10.1016/j.jmbbm.2021.104645.
27.	Shahryari M, Tzsch?tzsch H, Guo J, et al. Tomoelastography distinguishes noninvasively between benign and malignant liver lesions. Cancer Res, 2019, 79(22): 5704-5710.
28.	Iima M. Perfusion-driven intravoxel incoherent motion (IVIM) MRI in oncology: applications, challenges, and future trends. Magn Reson Med Sci, 2021, 20(2): 125-138.
29.	Wei Y, Huang Z, Tang H, et al. IVIM improves preoperative assessment of microvascular invasion in HCC. Eur Radiol, 2019, 29(10): 5403-5414.
30.	Matsui O, Kobayashi S, Sanada J, et al. Hepatocelluar nodules in liver cirrhosis: hemodynamic evaluation (angiography-assisted CT) with special reference to multi-step hepatocarcinogenesis. Abdom Imaging, 2011, 36(3): 264-272.
31.	Hong SB, Choi SH, Kim SY, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: a systematic review and meta-analysis. Liver Cancer, 2021, 10(2): 94-106.
32.	Lee S, Kim SH, Lee JE, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol, 2017, 67(3): 526-534.
33.	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.
34.	Fang JH, Xu L, Shang LR, et al. Vessels that encapsulate tumor clusters (VETC) pattern is a predictor of sorafenib benefit in patients with hepatocellular carcinoma. Hepatology, 2019, 70(3): 824-839.
35.	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.
36.	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.
37.	Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology, 2009, 137(3): 850-855.
38.	Li J, Zhang L, Xing H, et al. The absence of intra-tumoral tertiary lymphoid structures is associated with a worse prognosis and mTOR signaling activation in hepatocellular carcinoma with liver transplantation: a multicenter retrospective study. Adv Sci (Weinh), 2024, 11(21): e2309348. doi: 10.1002/advs.202309348.
39.	Shu DH, Ho WJ, Kagohara LT, et al. Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma. Nat Immunol, 2024, 25(11): 2110-2123.
40.	Long S, Li M, Chen J, et al. Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study. J Immunother Cancer, 2024, 12(12): e009879. doi: 10.1136/jitc-2024-009879.
41.	Hsu CL, Ou DL, Bai LY, et al. Exploring markers of exhausted CD8 T cells to predict response to immune checkpoint inhibitor therapy for hepatocellular carcinoma. Liver Cancer, 2021, 10(4): 346-359.
42.	Sun L, Mu L, Zhou J, et al. Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma. Cancer Immunol Immunother, 2022, 71(1): 25-38.
43.	Chen J, Wu Z, Zhang Z, et al. Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma. Eur Radiol, 2024, 34(11): 6980-6991.
44.	Lv Z, Cai X, Weng X, et al. Tumor-stroma ratio is a prognostic factor for survival in hepatocellular carcinoma patients after liver resection or transplantation. Surgery, 2015, 158(1): 142-150.
45.	Calon A, Tauriello DV, Batlle E. TGF-beta in CAF-mediated tumor growth and metastasis. Semin Cancer Biol, 2014, 25: 15-22.
46.	Reichl P, Haider C, Grubinger M, et al. TGF-β in epithelial to mesenchymal transition and metastasis of liver carcinoma. Curr Pharm Des, 2012, 18(27): 4135-4147.
47.	Gordic S, Ayache JB, Kennedy P, et al. Value of tumor stiffness measured with MR elastography for assessment of response of hepatocellular carcinoma to locoregional therapy. Abdom Radiol (NY), 2017, 42(6): 1685-1694.
48.	Lewis S, Dyvorne H, Cui Y, et al. Diffusion-weighted imaging of the liver: techniques and applications. Magn Reson Imaging Clin N Am, 2014, 22(3): 373-395.
49.	Wang L, Yang JD, Yoo CC, et al. Magnetic resonance imaging for characterization of hepatocellular carcinoma metabolism. Front Physiol, 2022, 13: 1056511. doi: 10.3389/fphys.2022.1056511.
50.	Zheng SS, Chen XH, Yin X, et al. Prognostic significance of HIF-1α expression in hepatocellular carcinoma: a meta-analysis. PLoS One, 2013, 8(6): e65753. doi: 10.1371/journal.pone.0065753.
51.	Huang Z, Xu X, Meng X, et al. Correlations between ADC values and molecular markers of Ki-67 and HIF-1α in hepatocellular carcinoma. Eur J Radiol, 2015, 84(12): 2464-2469.
52.	Liang J, Ma R, Chen H, et al. Detection of hyperacute reactions of desacetylvinblastine monohydrazide in a xenograft model using intravoxel incoherent motion DWI and R2* mapping. AJR Am J Roentgenol, 2019, 212(4): 717-726.
53.	Li X, Huang W, Holmes JH. Dynamic contrast-enhanced (DCE) MRI. Magn Reson Imaging Clin N Am, 2024, 32(1): 47-61.
54.	Yopp AC, Schwartz LH, Kemeny N, et al. Antiangiogenic therapy for primary liver cancer: correlation of changes in dynamic contrast-enhanced magnetic resonance imaging with tissue hypoxia markers and clinical response. Ann Surg Oncol, 2011, 18(8): 2192-2199.
55.	Park HJ, Kim YK, Min JH, et al. Feasibility of blood oxygenation level-dependent MRI at 3T in the characterization of hepatic tumors. Abdom Imaging, 2014, 39(1): 142-152.
56.	Li B, Xu A, Huang Y, et al. Oxygen-challenge blood oxygen level-dependent magnetic resonance imaging for evaluation of early change of hepatocellular carcinoma to chemoembolization: a feasibility study. Acad Radiol, 2021, 28 Suppl 1: S13-S19.
57.	Deen SS, Rooney C, Shinozaki A, et al. Hyperpolarized carbon 13 MRI: clinical applications and future directions in oncology. Radiol Imaging Cancer, 2023, 5(5): e230005. doi: 10.1148/rycan.230005.
58.	Larson PEZ, Bernard JML, Bankson JA, et al. Current methods for hyperpolarized [1-¹³C] pyruvate MRI human studies. Magn Reson Med, 2024, 91(6): 2204-2228.
59.	Bliemsrieder E, Kaissis G, Grashei M, et al. Hyperpolarized ¹³C pyruvate magnetic resonance spectroscopy for in vivo metabolic phenotyping of rat HCC. Sci Rep, 2021, 11(1): 1191. doi: 10.1038/s41598-020-80952-4.

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 龐北川, 譚煜煒, 李友偉, 等. 肝細胞癌免疫治療超進展潛在分子機制及預測因素研究進展. 中國普外基礎與臨床雜志, 2025, 32(7): 911-915.
3. Shim JH, Jun MJ, Han S, et al. Prognostic nomograms for prediction of recurrence and survival after curative liver resection for hepatocellular carcinoma. Ann Surg, 2015, 261(5): 939-946.
4. Vibert E, Schwartz M, Olthoff KM. Advances in resection and transplantation for hepatocellular carcinoma. J Hepatol, 2020, 72(2): 262-276.
5. Calderaro J, Ziol M, Paradis V, et al. Molecular and histological correlations in liver cancer. J Hepatol, 2019, 71(3): 616-630.
6. Kawaoka T, Ando Y, Yamauchi M, et al. Incidence of microsatellite instability-high hepatocellular carcinoma among Japanese patients and response to pembrolizumab. Hepatol Res, 2020, 50(7): 885-888.
7. Craig AJ, von Felden J, Garcia-Lezana T, et al. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol, 2020, 17(3): 139-152.
8. Sun Y, Wu L, Zhong Y, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell, 2021, 184(2): 404-421.
9. Robertson-Tessi M, Gillies RJ, Gatenby RA, et al. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res, 2015, 75(8): 1567-1579.
10. DeBerardinis RJ, Mancuso A, Daikhin E, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A, 2007, 104(49): 19345-19350.
11. Klauss M, Mayer P, Maier-Hein K, et al. IVIM-diffusion-MRI for the differentiation of solid benign and malign hypervascular liver lesions-evaluation with two different MR scanners. Eur J Radiol, 2016, 85(7): 1289-1294.
12. Wei Y, Ye Z, Yuan Y, et al. A new diagnostic criterion with gadoxetic acid-enhanced MRI may improve the diagnostic performance for hepatocellular carcinoma. Liver Cancer, 2020, 9(4): 414-425.
13. Kim DH, Choi SH, Kim SY, et al. Gadoxetic acid-enhanced MRI of hepatocellular carcinoma: value of washout in transitional and hepatobiliary phases. Radiology, 2019, 291(3): 651-657.
14. Wei Y, Chen G, Tang H, et al. Improved display of hepatic arterial anatomy using differential subsampling with cartesian ordering (DISCO) with gadoxetic acid-enhanced MRI: comparison with single arterial phase MRI and computed tomographic angiography. J Magn Reson Imaging, 2020, 51(6): 1766-1776.
15. 張韻, 魏毅, 葉錚, 等. 肝細胞癌的影像學診斷及鑒別診斷. 中華放射學雜志, 2020, 54(8): 812-816.
16. Choi JY, Lee JM, Sirlin CB. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part Ⅱ. Extracellular agents, hepatobiliary agents, and ancillary imaging features. Radiology, 2014, 273(1): 30-50.
17. Yin Z, Lu X, Cohen Cohen S, et al. A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas. Eur Radiol, 2021, 31(8): 5554-5564.
18. Li M, Yin Z, Hu B, et al. MR elastography-based shear strain mapping for assessment of microvascular invasion in hepatocellular carcinoma. Eur Radiol, 2022, 32(7): 5024-5032.
19. Yang ZF, Poon RT. Vascular changes in hepatocellular carcinoma. Anat Rec (Hoboken), 2008, 291(6): 721-734.
20. Flores A, Asrani SK. Editorial: Magnetic resonance elastography and non-alcoholic fatty liver disease: time for an upgrade? Am J Gastroenterol, 2016, 111(7): 995-996.
21. Yamashita YI, Imai K, Yusa T, et al. Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: predictors and optimal treatments. Ann Gastroenterol Surg, 2018, 2(3): 197-203.
22. Atamaniuk V, Hańczyk ?, Chen J, et al. 3D vector MR elastography applications in small organs. Magn Reson Imaging, 2024, 112: 54-62.
23. Liu G, Shen Z, Chong H, et al. Three-dimensional multifrequency MR elastography for microvascular invasion and prognosis assessment in hepatocellular carcinoma. J Magn Reson Imaging, 2024, 60(6): 2626-2640.
24. Zhang L, Li M, Zhu J, et al. The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma. Eur Radiol, 2023, 33(6): 4103-4114.
25. Gao S, Zhang Y, Sun W, et al. Assessment of an MR elastography-based nomogram as a potential imaging biomarker for predicting microvascular invasion of hepatocellular carcinoma. J Magn Reson Imaging, 2023, 58(2): 392-402.
26. Reiter R, Shahryari M, Tzsch?tzsch H, et al. Influence of fibrosis progression on the viscous properties of in vivo liver tissue elucidated by shear wave dispersion in multifrequency MR elastography. J Mech Behav Biomed Mater, 2021, 121: 104645. doi: 10.1016/j.jmbbm.2021.104645.
27. Shahryari M, Tzsch?tzsch H, Guo J, et al. Tomoelastography distinguishes noninvasively between benign and malignant liver lesions. Cancer Res, 2019, 79(22): 5704-5710.
28. Iima M. Perfusion-driven intravoxel incoherent motion (IVIM) MRI in oncology: applications, challenges, and future trends. Magn Reson Med Sci, 2021, 20(2): 125-138.
29. Wei Y, Huang Z, Tang H, et al. IVIM improves preoperative assessment of microvascular invasion in HCC. Eur Radiol, 2019, 29(10): 5403-5414.
30. Matsui O, Kobayashi S, Sanada J, et al. Hepatocelluar nodules in liver cirrhosis: hemodynamic evaluation (angiography-assisted CT) with special reference to multi-step hepatocarcinogenesis. Abdom Imaging, 2011, 36(3): 264-272.
31. Hong SB, Choi SH, Kim SY, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: a systematic review and meta-analysis. Liver Cancer, 2021, 10(2): 94-106.
32. Lee S, Kim SH, Lee JE, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol, 2017, 67(3): 526-534.
33. 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.
34. Fang JH, Xu L, Shang LR, et al. Vessels that encapsulate tumor clusters (VETC) pattern is a predictor of sorafenib benefit in patients with hepatocellular carcinoma. Hepatology, 2019, 70(3): 824-839.
35. 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.
36. 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.
37. Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology, 2009, 137(3): 850-855.
38. Li J, Zhang L, Xing H, et al. The absence of intra-tumoral tertiary lymphoid structures is associated with a worse prognosis and mTOR signaling activation in hepatocellular carcinoma with liver transplantation: a multicenter retrospective study. Adv Sci (Weinh), 2024, 11(21): e2309348. doi: 10.1002/advs.202309348.
39. Shu DH, Ho WJ, Kagohara LT, et al. Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma. Nat Immunol, 2024, 25(11): 2110-2123.
40. Long S, Li M, Chen J, et al. Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study. J Immunother Cancer, 2024, 12(12): e009879. doi: 10.1136/jitc-2024-009879.
41. Hsu CL, Ou DL, Bai LY, et al. Exploring markers of exhausted CD8 T cells to predict response to immune checkpoint inhibitor therapy for hepatocellular carcinoma. Liver Cancer, 2021, 10(4): 346-359.
42. Sun L, Mu L, Zhou J, et al. Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma. Cancer Immunol Immunother, 2022, 71(1): 25-38.
43. Chen J, Wu Z, Zhang Z, et al. Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma. Eur Radiol, 2024, 34(11): 6980-6991.
44. Lv Z, Cai X, Weng X, et al. Tumor-stroma ratio is a prognostic factor for survival in hepatocellular carcinoma patients after liver resection or transplantation. Surgery, 2015, 158(1): 142-150.
45. Calon A, Tauriello DV, Batlle E. TGF-beta in CAF-mediated tumor growth and metastasis. Semin Cancer Biol, 2014, 25: 15-22.
46. Reichl P, Haider C, Grubinger M, et al. TGF-β in epithelial to mesenchymal transition and metastasis of liver carcinoma. Curr Pharm Des, 2012, 18(27): 4135-4147.
47. Gordic S, Ayache JB, Kennedy P, et al. Value of tumor stiffness measured with MR elastography for assessment of response of hepatocellular carcinoma to locoregional therapy. Abdom Radiol (NY), 2017, 42(6): 1685-1694.
48. Lewis S, Dyvorne H, Cui Y, et al. Diffusion-weighted imaging of the liver: techniques and applications. Magn Reson Imaging Clin N Am, 2014, 22(3): 373-395.
49. Wang L, Yang JD, Yoo CC, et al. Magnetic resonance imaging for characterization of hepatocellular carcinoma metabolism. Front Physiol, 2022, 13: 1056511. doi: 10.3389/fphys.2022.1056511.
50. Zheng SS, Chen XH, Yin X, et al. Prognostic significance of HIF-1α expression in hepatocellular carcinoma: a meta-analysis. PLoS One, 2013, 8(6): e65753. doi: 10.1371/journal.pone.0065753.
51. Huang Z, Xu X, Meng X, et al. Correlations between ADC values and molecular markers of Ki-67 and HIF-1α in hepatocellular carcinoma. Eur J Radiol, 2015, 84(12): 2464-2469.
52. Liang J, Ma R, Chen H, et al. Detection of hyperacute reactions of desacetylvinblastine monohydrazide in a xenograft model using intravoxel incoherent motion DWI and R2* mapping. AJR Am J Roentgenol, 2019, 212(4): 717-726.
53. Li X, Huang W, Holmes JH. Dynamic contrast-enhanced (DCE) MRI. Magn Reson Imaging Clin N Am, 2024, 32(1): 47-61.
54. Yopp AC, Schwartz LH, Kemeny N, et al. Antiangiogenic therapy for primary liver cancer: correlation of changes in dynamic contrast-enhanced magnetic resonance imaging with tissue hypoxia markers and clinical response. Ann Surg Oncol, 2011, 18(8): 2192-2199.
55. Park HJ, Kim YK, Min JH, et al. Feasibility of blood oxygenation level-dependent MRI at 3T in the characterization of hepatic tumors. Abdom Imaging, 2014, 39(1): 142-152.
56. Li B, Xu A, Huang Y, et al. Oxygen-challenge blood oxygen level-dependent magnetic resonance imaging for evaluation of early change of hepatocellular carcinoma to chemoembolization: a feasibility study. Acad Radiol, 2021, 28 Suppl 1: S13-S19.
57. Deen SS, Rooney C, Shinozaki A, et al. Hyperpolarized carbon 13 MRI: clinical applications and future directions in oncology. Radiol Imaging Cancer, 2023, 5(5): e230005. doi: 10.1148/rycan.230005.
58. Larson PEZ, Bernard JML, Bankson JA, et al. Current methods for hyperpolarized [1-¹³C] pyruvate MRI human studies. Magn Reson Med, 2024, 91(6): 2204-2228.
59. Bliemsrieder E, Kaissis G, Grashei M, et al. Hyperpolarized ¹³C pyruvate magnetic resonance spectroscopy for in vivo metabolic phenotyping of rat HCC. Sci Rep, 2021, 11(1): 1191. doi: 10.1038/s41598-020-80952-4.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

The assessment of hepatocellular carcinoma tumor microenvironment heterogeneity using multiparametric magnetic resonance imaging: the research progress

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