基于CT和MRI影像组学的机器学习模型预测肝细胞癌病理分化程度的研究进展

doi:10.3969/j.issn.1004-583X.2026.07.015

摘要/Abstract

摘要：

近年来,原发性肝癌仍是全球疾病负担最重的恶性肿瘤之一,其中肝细胞癌(hepatocellular carcinoma,HCC)占原发性肝癌的75%~85%。HCC的病理分化程度与肿瘤侵袭性、复发风险及预后密切相关,但其并非决定治疗方案的唯一因素。当前临床分期和治疗规划仍需综合肿瘤负荷、肝功能、门静脉高压及全身状态等信息。计算机断层扫描(computed tomography,CT)和磁共振成像(magnetic resonance imaging,MRI)可从形态、血供、肝胆期摄取及水分子扩散等方面提供肿瘤表型信息,为术前无创评估HCC分化程度提供了重要基础。本文围绕HCC病理分化程度的病理学标签来源、传统影像学表现、功能MRI参数、CT/MRI影像组学及机器学习建模方法进行综述,并重点讨论现有研究在病理标签一致性、肿瘤内部异质性、模型泛化能力和临床转化方面的关键瓶颈。总体而言,基于CT和MRI影像组学的机器学习模型有望作为HCC病理分化程度评估的辅助工具,但仍需在标准化病理标签、规范化影像处理、多中心外部验证、模型校准与可解释性等方面进一步完善。

关键词: 癌, 肝细胞, CT, MRI, 影像组学, 机器学习, 病理分化程度

中图分类号:

R730.261

李雅彤, 李捷, 袁庆海. 基于CT和MRI影像组学的机器学习模型预测肝细胞癌病理分化程度的研究进展[J]. 临床荟萃, 2026, 41(7): 668-672.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2026.07.015

https://www.lchc.cn/CN/Y2026/V41/I7/668

参考文献 32

[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[J]. CA Cancer J Clin, 2024, 74(3):229-263. doi:10.3322/caac.21834.
[2]	Rumgay H, Arnold M, Ferlay J, et al. Global burden of primary liver cancer in 2020 and predictions to 2040[J]. J Hepatol, 2022, 77(6):1598-1606. doi:10.1016/j.jhep.2022.08.021. pmid: 36208844
[3]	Singal AG, Llovet JM, Yarchoan M, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma[J]. Hepatology, 2023, 78(6):1922-1965. doi:10.1097/HEP.0000000000000466. pmid: 37199193
[4]	Vogel A, Chan SL, Dawson LA, et al. Hepatocellular carcinoma: ESMO clinical practice guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2025, 36(5):491-506. doi:10.1016/j.annonc.2025.02.006. pmid: 39986353
[5]	Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update[J]. J Hepatol, 2022, 76(3):681-693. doi:10.1016/j.jhep.2021.11.018.
[6]	Edmondson HA, Steiner PE. Primary carcinoma of the liver: A study of 100 cases among 48900 necropsies[J]. Cancer, 1954, 7(3):462-503. doi: 10.1002/1097-0142(195405)7:3<462::aid-cncr2820070308>3.0.co;2-e.
[7]	Shinkawa H, Tanaka S, Kabata D, et al. The prognostic impact of tumor differentiation on recurrence and survival after resection of hepatocellular carcinoma is dependent on tumor size[J]. Liver Cancer, 2021, 10(5):461-472. doi:10.1159/000517992. pmid: 34721508
[8]	International Consensus Group for Hepatocellular Neoplasia. Pathologic diagnosis of early hepatocellular carcinoma: A report of the international consensus group for hepatocellular neoplasia[J]. Hepatology, 2009, 49(2):658-664. doi:10.1002/hep.22709. pmid: 19177576
[9]	Huang Y, Chen L, Ding Q, et al. CT-based radiomics for predicting pathological grade in hepatocellular carcinoma[J]. Front Oncol, 2024, 14:1295575. doi:10.3389/fonc.2024.1295575.
[10]	Chen W, Zhang T, Xu L, et al. Radiomics analysis of contrast-enhanced CT for hepatocellular carcinoma grading[J]. Front Oncol, 2021, 11:660509. doi:10.3389/fonc.2021.660509.
[11]	Tong X, Li J. Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics[J]. Eur J Radiol Open, 2022, 9:100424. doi:10.1016/j.ejro.2022.100424.
[12]	Li XM, Nguyen T, Sparks HD, et al. Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in hepatocellular carcinoma: A review of emerging applications for locoregional therapy[J]. Bioengineering (Basel), 2025, 12(8):870. doi:10.3390/bioengineering12080870.
[13]	Mao Y, Wang J, Zhu Y, et al. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma[J]. Hepatobiliary Surg Nutr, 2022, 11(1):13-24. doi:10.21037/hbsn-19-870.
[14]	Hu X, Li C, Wang Q, et al. Development and external validation of a radiomics model derived from preoperative gadoxetic acid-enhanced MRI for predicting histopathologic grade of hepatocellular carcinoma[J]. Diagnostics (Basel), 2023, 13(3):413. doi:10.3390/diagnostics13030413.
[15]	Yang D, She H, Wang X, et al. Diagnostic accuracy of quantitative diffusion parameters in the pathological grading of hepatocellular carcinoma: A meta-analysis[J]. J Magn Reson Imaging, 2020, 51(5):1581-1593. doi:10.1002/jmri.26963. pmid: 31654537
[16]	Xu YS, Liu HF, Xi DL, et al. Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: A correlation study with histological grade of hepatocellular carcinoma[J]. Abdom Radiol (NY), 2019, 44(9):3089-3098. doi:10.1007/s00261-019-02109-w.
[17]	Zhou Y, Yang G, Gong XQ, et al. A study of the correlations between IVIM-DWI parameters and histologic differentiation of hepatocellular carcinoma[J]. Sci Rep, 2021, 11(1):10392. doi:10.1038/s41598-021-89784-2. pmid: 34001962
[18]	Yang CC, Wei XQ, Zheng J, et al. A correlative study between IVIM-DWI parameters and VEGF and MMPs expression in hepatocellular carcinoma[J]. Quant Imaging Med Surg, 2023, 13(3):1887-1898. doi:10.21037/qims-22-271.
[19]	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4):441-446. doi:10.1016/j.ejca.2011.11.036. pmid: 22257792
[20]	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2):563-577. doi:10.1148/radiol.2015151169. pmid: 26579733
[21]	van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype[J]. Cancer Res, 2017, 77(21):e104-e107. doi:10.1158/0008-5472.CAN-17-0339.
[22]	Zwanenburg A, Vallieres M, Abdalah MA, et al. The Image Biomarker Standardisation Initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2):328-338. doi:10.1148/radiol.2020191145. pmid: 32154773
[23]	Collins GS, Moons KGM, van Smeden M, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods[J]. BMJ, 2024, 385:e078378. doi:10.1136/bmj-2023-078378.
[24]	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12):749-762. doi:10.1038/nrclinonc.2017.141. pmid: 28975929
[25]	Lambin P, Woodruff HC, Mali SA, et al. Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine[J]. Nat Rev Clin Oncol, 2025, 22(11):831-846. doi:10.1038/s41571-025-01067-1.
[26]	Orlhac F, Frouin F, Nioche C, et al. Validation of a method to compensate multicenter effects affecting CT radiomics[J]. Radiology, 2019, 291(1):53-59. doi:10.1148/radiol.2019182023. pmid: 30694160
[27]	Liu HF, Wang M, Wang Q, et al. Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma[J]. Insights Imaging, 2024, 15(1):97. doi:10.1186/s13244-024-01623-w.
[28]	Han YE, Cho Y, Kim MJ, et al. Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: A two-center study[J]. Abdom Radiol (NY), 2023, 48(1):244-256. doi:10.1007/s00261-022-03679-y.
[29]	Ameli S, Venkatesh BA, Shaghaghi M, et al. Role of MRI-derived radiomics features in determining degree of tumor differentiation of hepatocellular carcinoma[J]. Diagnostics (Basel), 2022, 12(10):2386. doi:10.3390/diagnostics12102386.
[30]	Geng Z, Zhang Y, Wang S, et al. Radiomics analysis of susceptibility weighted imaging for hepatocellular carcinoma: Exploring the correlation between histopathology and radiomics features[J]. Magn Reson Med Sci, 2021, 20(3):253-263. doi:10.2463/mrms.mp.2020-0060.
[31]	Wei J, Ji Q, Gao Y, et al. A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma[J]. Med Phys, 2023, 50(4):2290-2302. doi:10.1002/mp.16127.
[32]	Wang Q, Liu Q, Xu X, et al. Preoperative CT-and MRI-based radiomics for predicting histopathologic grade of hepatocellular carcinoma: A systematic review and radiomics quality score assessment[J]. Eur J Radiol, 2023, 164:110838. doi:10.1016/j.ejrad.2023.110838.