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Advances in radiomics for predicting the efficacy of local treatments in liver malignancies
LONG Die  HUA Li  SHANG Wenying  ZHANG Jingyu  CHEN Haihui  CHEN Shaojun 

Cite this article as: LONG D, HUA L, SHANG W Y, et al. Advances in radiomics for predicting the efficacy of local treatments in liver malignancies[J]. Chin J Magn Reson Imaging, 2025, 16(7): 166-172. DOI:10.12015/issn.1674-8034.2025.07.027.


[Abstract] In recent years, the incidence of malignant liver tumors, including primary liver cancer and metastatic liver cancer, has been steadily increasing. Among comprehensive treatment strategies, in addition to systemic therapies, local treatments play a critical role. However, the presence of tumor heterogeneity leads to significant interpatient variability in response to local therapies. Radiomics, which extracts imperceptible intratumoral heterogeneity features from medical imaging, has greatly enhanced the ability to predict the efficacy of local treatments for malignant liver tumors. Previous studies have evaluated the predictive value of radiomics in both systemic and local treatment responses for hepatocellular carcinoma and colorectal liver metastases. Nevertheless, there is a lack of systematic reviews that comprehensively summarize the progress of radiomics applications in assessing the efficacy of local treatments for both primary and secondary malignant liver tumors. This review systematically outlines the current state of research on radiomics in various local treatment modalities for malignant liver tumors, including surgery, interventional therapy, ablation, and radiotherapy. It focuses on the application of radiomics in identifying treatment-sensitive populations, assessing recurrence risk, and predicting survival outcomes. In addition, this review addresses key obstacles in the clinical use of radiomics for local therapies of hepatic malignancies, and integrates current research focuses. It further outlines a practical, evidence-based model for precision treatment of liver cancers and highlights directions for future study.
[Keywords] radiomics；primary liver cancer；metastatic liver cancer；treatment efficacy；magnetic resonance imaging

LONG Die1   HUA Li1   SHANG Wenying2   ZHANG Jingyu3   CHEN Haihui2#*   CHEN Shaojun1*  

1 Department of Oncology, the Fourth Clinical Medical School of Guangxi medical University, Liuzhou 545005, China

2 Department of Oncology, the Third Clinical Medical College of Guangxi University of Traditional Chinese Medicine, Liuzhou 545026, China

3 State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing, 400016, China

Corresponding author: CHEN H H, E-mail: chenhh1595@163.com CHEN S J, E-mail: chenshaoiun388@163.com

Conflicts of interest   None.

Received  2025-02-17
Accepted  2025-07-07
DOI: 10.12015/issn.1674-8034.2025.07.027
Cite this article as: LONG D, HUA L, SHANG W Y, et al. Advances in radiomics for predicting the efficacy of local treatments in liver malignancies[J]. Chin J Magn Reson Imaging, 2025, 16(7): 166-172. DOI:10.12015/issn.1674-8034.2025.07.027.

[1]
JIANG S W, ZHOU H J, XIE Q. Current status, challenges and future directions for hepatocellular carcinoma surveillance in China[J]. J Diagn Concepts Pract, 2024, 23(1): 9-15. DOI: 10.16150/j.1671-2870.2024.01.002.
[2]
HAO Y, LI C, WEN T F, et al. Epidemiological characteristics of liver cancer worldwide and in China: an interpretation of global cancer statistics 2022[J]. Chin J Bases Clin Gen Surg, 2024, 31(7): 781-789. DOI: 10.7507/1007-9424.202405099.
[3]
TSILIMIGRAS D I, BRODT P, CLAVIEN P A, et al. Liver metastases[J/OL]. Nat Rev Dis Primers, 2021, 7: 27 [2024-11-01]. https://pubmed.ncbi.nlm.nih.gov/33859205/. DOI: 10.1038/s41572-021-00261-6.
[4]
EISENHAUER E A, THERASSE P, BOGAERTS J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)[J]. Eur J Cancer, 2009, 45(2): 228-247. DOI: 10.1016/j.ejca.2008.10.026.
[5]
LLOVET J M, LENCIONI R. mRECIST for HCC: Performance and novel refinements[J]. J Hepatol, 2020, 72(2): 288-306. DOI: 10.1016/j.jhep.2019.09.026.
[6]
THARMASEELAN H, HERTEL A, TOLLENS F, et al. Identification of CT imaging phenotypes of colorectal liver metastases from radiomics signatures-towards assessment of interlesional tumor heterogeneity[J/OL]. Cancers (Basel), 2022, 14(7): 1646 [2024-11-04]. https://pubmed.ncbi.nlm.nih.gov/35406418/. DOI: 10.3390/cancers14071646.
[7]
LI X M, LIU Z Y. Research progress of radiomics in hepatocellular carcinoma[J]. Chin J Hepatol, 2024, 32(8): 679-687. DOI: 10.3760/cma.j.cn501113-20240528-00272.
[8]
LI Z F, YE Z X. Research progress in radiomics and deep learning for early prediction and efficacy evaluation in colorectal cancer liver metastases[J/OL]. Chin J Clin Oncol, 2024, 51(1): 36-40. DOI: 10.12354/j.issn.1000-8179.2024.20231333.
[9]
CUI Y F, YIN F F. Impact of image quality on radiomics applications[J/OL]. Phys Med Biol, 2022, 67(15): 15TR03 [2024-11-20]. https://pubmed.ncbi.nlm.nih.gov/35803254/. DOI: 10.1088/1361-6560/ac7fd7.
[10]
WANG Y, ZHU G Q, YANG R, et al. Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis[J/OL]. J Transl Med, 2023, 21(1): 734 [2024-11-21]. https://pubmed.ncbi.nlm.nih.gov/37853415/. DOI: 10.1186/s12967-023-04586-6.
[11]
LIU K F, LI K W, WU T F, et al. Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT[J]. Eur Radiol, 2022, 32(2): 1065-1077. DOI: 10.1007/s00330-021-08194-0.
[12]
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.
[13]
INCHINGOLO R, MAINO C, CANNELLA R, et al. Radiomics in colorectal cancer patients[J]. World J Gastroenterol, 2023, 29(19): 2888-2904. DOI: 10.3748/wjg.v29.i19.2888.
[14]
ZHONG Y, SHE Y, DENG J, et al. Deep learning for prediction of N2 metastasis and survival for clinical stage I non-small cell lung cancer[J]. Radiology, 2022, 302(1): 200-211. DOI: 10.1148/radiol.2021210902.
[15]
VANGURI R S, LUO J, AUKERMAN A T, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer[J]. Nat Cancer, 2022, 3(10): 1151-1164. DOI: 10.1038/s43018-022-00416-8.
[16]
WANG S X, LIU X W, WU Y, et al. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study[J/OL]. Front Oncol, 2023, 13: 1252074 [2024-11-21]. https://pubmed.ncbi.nlm.nih.gov/37954078/. DOI: 10.3389/fonc.2023.1252074.
[17]
Department of Medical Administration of the National Health Commission of the People's Republic of China. Guideline for diagnosis and treatment of primary liver cancer (2024 edition)[J]. Chin J Magn Reson Imaging, 2024, 15(6): 1-18. DOI: 10.12015/issn.1674-8034.2024.06.001.
[18]
LIU Y, YANG W J, GUO W. Research progress on the predicting value of serum markers related to postoperative recurrence in patients with hepatocellular carcinoma after curative resection[J]. Chin J Lab Med, 2023, 46(1): 93-97.
[19]
YU Y, WANG X H, HU W J, et al. Patterns, risk factors, and outcomes of recurrence after hepatectomy for hepatocellular carcinoma with and without microvascular invasion[J]. J Hepatocell Carcinoma, 2024, 11: 801-812. DOI: 10.2147/JHC.S438850.
[20]
TENG F, MAO J X, SONG S H, et al. Major issues of liver transplantation for nonresectable liver-only colorectal liver metastases[J]. Chin J Organ Transplant, 2023, 44(12): 717-722. DOI: 10.3760/cma.j.cn421203-20230913-00088.
[21]
QIAN G X, XU Z L, LI Y H, et al. Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis[J]. World J Gastroenterol, 2024, 30(15): 2128-2142. DOI: 10.3748/wjg.v30.i15.2128.
[22]
YAN M, ZHANG X, ZHANG B, et al. Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy[J]. Eur Radiol, 2023, 33(7): 4949-4961. DOI: 10.1007/s00330-023-09419-0.
[23]
SHUR J, ORTON M, CONNOR A, et al. A clinical-radiomic model for improved prognostication of surgical candidates with colorectal liver metastases[J]. J Surg Oncol, 2020, 121(2): 357-364. DOI: 10.1002/jso.25783.
[24]
GRANATA V, FUSCO R, DE MUZIO F, et al. EOB-MR based radiomics analysis to assess clinical outcomes following liver resection in colorectal liver metastases[J/OL]. Cancers (Basel), 2022, 14(5): 1239 [2024-11-25]. https://pubmed.ncbi.nlm.nih.gov/35267544/. DOI: 10.3390/cancers14051239.
[25]
SONG Y D, ZHOU G Y, ZHOU Y C, et al. Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study[J]. Hepatol Int, 2023, 17(4): 1016-1027. DOI: 10.1007/s12072-023-10487-z.
[26]
NIE P, ZHANG J T, MIAO W J, et al. Incremental value of radiomics-based heterogeneity to the existing risk criteria in predicting recurrence of hepatocellular carcinoma after liver transplantation[J]. Eur Radiol, 2023, 33(9): 6608-6618. DOI: 10.1007/s00330-023-09591-3.
[27]
DENG P Z, ZHAO B G, HUANG X H, et al. Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma[J]. World J Gastroenterol, 2022, 28(31): 4376-4389. DOI: 10.3748/wjg.v28.i31.4376.
[28]
TANG Y Y, ZHANG T, ZHOU X H, et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma[J/OL]. World J Surg Oncol, 2021, 19(1): 45 [2024-11-25]. https://pubmed.ncbi.nlm.nih.gov/34334138/. DOI: 10.1186/s12957-021-02162-0.
[29]
LIU J, ZHANG J, WANG Y, et al. HAIC versus TACE for patients with unresectable hepatocellular carcinoma: a systematic review and meta-analysis[J/OL]. Medicine (Baltimore), 2022, 101(51): e32390 [2024-12-01]. https://pubmed.ncbi.nlm.nih.gov/36595819/. DOI: 10.1097/md.0000000000032390.
[30]
BI X Y, CHEN X, HAN Y, et al. Chinese expert consensus on MDT diagnosis and treatment of colorectal cancer liver metastasis(2024 edition)[J]. Electron J Liver Tumor, 2024, 11(3): 1-13. DOI: 10.3969/j.issn.2095-7815.2024.03.002.
[31]
CHAPIRO J, DURAN R, LIN M D, et al. Early survival prediction after intra-arterial therapies: a 3D quantitative MRI assessment of tumour response after TACE or radioembolization of colorectal cancer metastases to the liver[J]. Eur Radiol, 2015, 25(7): 1993-2003. DOI: 10.1007/s00330-015-3595-5.
[32]
CHEN M, KONG C, QIAO E, et al. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI[J/OL]. Insights Imaging, 2023, 14(1): 38 [2024-12-03]. https://link.Springer.com/article/10.1186/s13244-023-01380-2. DOI: 10.1186/s13244-023-01380-2.
[33]
LIU Q P, YANG K L, XU X, et al. Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study[J]. Abdom Radiol (NY), 2022, 47(2): 651-663. DOI: 10.1007/s00261-021-03375-3.
[34]
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[J/OL]. BMC Cancer, 2023, 23(1): 1026 [2024-11-29]. https://pubmed.ncbi.nlm.nih.gov/37875815/. DOI: 10.1186/s12885-023-11491-0.
[35]
BAI H L, MENG S Y, XIONG C F, et al. Preoperative CECT-based radiomic signature for predicting the response of transarterial chemoembolization (TACE) therapy in hepatocellular carcinoma[J]. Cardiovasc Intervent Radiol, 2022, 45(10): 1524-1533. DOI: 10.1007/s00270-022-03221-z.
[36]
LIU D, LIU F, XIE X Y, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound[J]. Eur Radiol, 2020, 30(4): 2365-2376. DOI: 10.1007/s00330-019-06553-6.
[37]
XU Z M, AN C, SHI F, et al. Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram[J]. Eur Radiol, 2023, 33(12): 9038-9051. DOI: 10.1007/s00330-023-09953-x.
[38]
LIU P, ZHU H T, ZHU H B, et al. Predicting survival for hepatic arterial infusion chemotherapy of unresectable colorectal liver metastases: radiomics analysis of pretreatment computed tomography[J]. J Transl Int Med, 2022, 10(1): 56-64. DOI: 10.2478/jtim-2022-0004.
[39]
YIN L N, LIU R B, LI W, et al. Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors[J/OL]. BMC Gastroenterol, 2025, 25(1): 24 [2025-02-16] https://pubmed.ncbi.nlm.nih.gov/39838292/. DOI: 10.1186/s12876-024-03555-7.
[40]
Surgeons Branch of Chinese Medical Doctor Association, Gastrointestinal Surgery Branch of Chinese Medical Association, Colorectal Surgery Branch of Chinese Medical Association, etc. Chinese Guidelines for Diagnosis and Comprehensive Treatment of liver metastases of Colorectal Cancer (V2023)[J]. Chinese e-journal of colorectal diseases, 2023, 12(1): 1-16. DOI: 10.3877/cma.j.issn.2095-3224.2023.01.001.
[41]
TANG Y, ZHONG H, WANG Y Y, et al. Efficacy of microwave ablation versus radiofrequency ablation in the treatment of colorectal liver metastases: a systematic review and meta-analysis[J/OL]. Clin Res Hepatol Gastroenterol, 2023, 47(7): 102182 [2024-12-05]. https://pubmed.ncbi.nlm.nih.gov/37479137/. DOI: 10.1016/j.clinre.2023.102182.
[42]
ZHANG X J, WANG C D, ZHENG D, et al. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation[J/OL]. Front Oncol, 2022, 12: 1013770 [2024-11-18]. https://pubmed.ncbi.nlm.nih.gov/36439458/. DOI: 10.3389/fonc.2022.1013770.
[43]
PETUKHOVA-GREENSTEIN A, ZEEVI T, YANG J L, et al. MR imaging biomarkers for the prediction of outcome after radiofrequency ablation of hepatocellular carcinoma: qualitative and quantitative assessments of the liver imaging reporting and data system and radiomic features[J]. J Vasc Interv Radiol, 2022, 33(7): 814-824. DOI: 10.1016/j.jvir.2022.04.006.
[44]
HUANG W R, PAN Y F, WANG H F, et al. Delta-radiomics analysis based on multi-phase contrast-enhanced MRI to predict early recurrence in hepatocellular carcinoma after percutaneous thermal ablation[J]. Acad Radiol, 2024, 31(12): 4934-4945. DOI: 10.1016/j.acra.2024.06.002.
[45]
LV X L, CHEN M J, KONG C L, et al. Construction of a novel radiomics nomogram for the prediction of aggressive intrasegmental recurrence of HCC after radiofrequency ablation[J/OL]. Eur J Radiol, 2021, 144: 109955 [2024-11-20]. https://pubmed.ncbi.nlm.nih.gov/34600237/. DOI: 10.1016/j.ejrad.2021.109955.
[46]
DELLA CORTE A, MORI M, CALABRESE F, et al. Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation[J/OL]. Int J Hyperth, 2024, 41(1) [2024-11-27]. https://pubmed.ncbi.nlm.nih.gov/38754994/. DOI: 10.1080/02656736.2024.2349059.
[47]
ZHANG G Y, LIU P, WANG G Z, et al. Local tumor progression predictive model based on MRI for colorectal cancer liver metastases after radiofrequency ablation[J]. Discov Med, 2024, 36(183): 765-777. DOI: 10.24976/Discov.Med.202436183.72.
[48]
SHAHVERANOVA A, BALLI H T, AIKIMBAEV K, et al. Prediction of local tumor progression after microwave ablation in colorectal carcinoma liver metastases patients by MRI radiomics and clinical characteristics-based combined model: preliminary results[J]. Cardiovasc Intervent Radiol, 2023, 46(6): 713-725. DOI: 10.1007/s00270-023-03454-6.
[49]
GRAVELL R, FROOD R, LITTLEJOHNS A, et al. Can patient characteristics and pre-treatment MRI features predict survival after stereotactic ablative radiotherapy (SABR) treatment in hepatocellular carcinoma (HCC): preliminary assessment[J]. Curr Oncol, 2024, 31(10): 6384-6394. DOI: 10.3390/curroncol31100474.
[50]
PARK J W, LEE H S, HONG H, et al. Efficacy of radiomics in predicting oncologic outcome of liver-directed combined radiotherapy in locally advanced hepatocellular carcinoma[J/OL]. Cancers (Basel), 2023, 15(22): 5405 [2024-11-24]. https://pubmed.ncbi.nlm.nih.gov/38001665/. DOI: 10.3390/cancers15225405.
[51]
COZZI L, DINAPOLI N, FOGLIATA A, et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy[J/OL]. BMC Cancer, 2017, 17(1): 829 [2024-12-03]. https://pubmed.ncbi.nlm.nih.gov/29207975/. DOI: 10.1186/s12885-017-3847-7.
[52]
HU R, CHEN I, PEOPLES J, et al. Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy[J]. Phys Imaging Radiat Oncol, 2022, 24: 36-42. DOI: 10.1016/j.phro.2022.09.004.
[53]
JIN W H, SIMPSON G N, DOGAN N, et al. MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy[J/OL]. Sci Rep, 2022, 12(1): 18631 [2024-12-15]. https://pubmed.ncbi.nlm.nih.gov/36329116/. DOI: 10.1038/s41598-022-22826-5.
[54]
İNCE 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]. J Vasc Interv Radiol, 2023, 34(2): 235-243. DOI: 10.1016/j.jvir.2022.11.004.
[55]
BLANC-DURAND P, VAN DER GUCHT A, JREIGE M, et al. Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma[J]. Oncotarget, 2017, 9(4): 4549-4558. DOI: 10.18632/oncotarget.23423.
[56]
KOBE A, ZGRAGGEN J, MESSMER F, et al. Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: a proof of concept study[J/OL]. Eur J Radiol Open, 2021, 8: 100375 [2024-12-07]. https://pubmed.ncbi.nlm.nih.gov/34485629/. DOI: 10.1016/j.ejro.2021.100375.
[57]
HU Y H, SHEN F, YANG X, et al. Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity[J/OL]. Clin Epigenetics, 2023, 15(1): 161 [2024-11-20]. https://pubmed.ncbi.nlm.nih.gov/37821906/. DOI: 10.1186/s13148-023-01574-x.
[58]
LIU Y, WEI X Q, FENG X, et al. Repeatability of radiomics studies in colorectal cancer: a systematic review[J/OL]. BMC Gastroenterol, 2023, 23(1): 125 [2024-12-10]. https://pubmed.ncbi.nlm.nih.gov/37059990/. DOI: 10.1186/s12876-023-02743-1.

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