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The application progress of radiomics in intrahepatic cholangiocarcinoma
GUO Shicheng  GU Siming  SUN Ziyue  YIN Xiaoping 

DOI:10.12015/issn.1674-8034.2026.07.024.


[Abstract] Intrahepatic cholangiocarcinoma (ICC) is a highly malignant and poorly prognostic subtype of primary liver cancer, and its conventional imaging assessment is challenging. In recent years, radiomics technology has been increasingly applied in the field of ICC, providing new methods for the diagnosis and treatment of this disease. Most existing reviews have not deeply associated radiomics features with the specific biological behaviors of ICC. Therefore, this article aims to systematically summarize the research progress of radiomics in the diagnosis and treatment of ICC, explore the relationship between radiomics features and the biological behaviors of ICC, and provide new ideas for future basic and clinical translational research. Radiomics has demonstrated application value in the differential diagnosis, pathological grading, invasiveness assessment, and postoperative recurrence and survival prediction of ICC. However, current studies generally face challenges such as significant data heterogeneity, poor feature reproducibility, lack of multi-center validation, and limited biological interpretability. Future research needs to develop in the directions of prospective, multi-center, standardized, and multi-modal, integrating radiomics with genomics, pathology, and other fields to construct more robust and practical auxiliary decision-making tools.
[Keywords] intrahepatic cholangiocarcinoma；radiomics；imaging diagnosis；computed tomography；magnetic resonance imaging

GUO Shicheng1, 2   GU Siming1, 2   SUN Ziyue1, 2   YIN Xiaoping1, 2*  

1 Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China

2 Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding 071000, China

Corresponding author: YIN X P, E-mail: yinxiaoping78@sina.com

Conflicts of interest   None.

Received  2026-02-02
Accepted  2026-06-03
DOI: 10.12015/issn.1674-8034.2026.07.024
DOI:10.12015/issn.1674-8034.2026.07.024.

[1]
MORIS D, PALTA M, KIM C, et al. Advances in the treatment of intrahepatic cholangiocarcinoma: an overview of the current and future therapeutic landscape for clinicians[J]. CA Cancer J Clin, 2023, 73(2): 198-222. DOI: 10.3322/caac.21759.
[2]
QURASHI M, VITHAYATHIL M, KHAN S A. Epidemiology of cholangiocarcinoma[J/OL]. Eur J Surg Oncol, 2025, 51(2): 107064 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/37709624/. DOI: 10.1016/j.ejso.2023.107064.
[3]
XU L, CHEN Z A, ZHU D, et al. The application status of radiomics-based machine learning in intrahepatic cholangiocarcinoma: Systematic review and meta-analysis[J/OL]. J Med Internet Res, 2025, 27: e69906[2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40323647/. DOI: 10.2196/69906.
[4]
WAKABAYASHI T, OUHMICH F, GONZALEZ-CABRERA C, et al. Radiomics in hepatocellular carcinoma: A quantitative review[J]. Hepatol Int, 2019, 13(5): 546-559. DOI: 10.1007/s12072-019-09973-0.
[5]
LAMBIN P, LEIJENAAR R T H, DEIST T M, 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.
[6]
SCAPICCHIO C, GABELLONI M, BARUCCI A, et al. A deep look into radiomics[J]. Radiol Med, 2021, 126(10): 1296-1311. DOI: 10.1007/s11547-021-01389-x.
[7]
DEMIRCIOĞLU A. Benchmarking feature projection methods in radiomics[J/OL]. Sci Rep, 2025, 15(1): 32368 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40913054/. DOI: 10.1038/s41598-025-16070-w.
[8]
WANG Q, MA X J, LI G T, et al. Research progress of radiomics in diagnosis and treatment of intrahepatic cholangiocarcinoma[J]. Chin Imaging J Integr Tradit West Med, 2024, 22(3): 272-277. DOI: 10.3969/j.issn.1672-0512.2024.03.006.
[9]
KODALI S, CONNOR A A, BROMBOSZ E W, et al. Update on the screening, diagnosis, and management of cholangiocarcinoma[J]. Gastroenterol Hepatol, 2024, 20(3): 151-158.
[10]
WANG D B, SUN L X. Diagnostic performance of radiomics for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma: a systematic review and meta-analysis[J]. Acad Radiol, 2025, 32(11): 6555-6569. DOI: 10.1016/j.acra.2025.05.056.
[11]
XU X L, MAO Y F, TANG Y Q, et al. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on radiomic analysis[J/OL]. Comput Math Meth Med, 2022, 2022: 5334095 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/35237341/. DOI: 10.1155/2022/5334095.
[12]
SU L Y, XU M, CHEN Y L, et al. Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound[J]. World J Radiol, 2024, 16(7): 247-255. DOI: 10.4329/wjr.v16.i7.247.
[13]
JIANG C J, ZHAO L W, XIN B W, et al. 18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma[J]. Quant Imaging Med Surg, 2022, 12(8): 4135-4150. DOI: 10.21037/qims-21-1167.
[14]
LIU N, WU Y K, TAO Y Y, et al. Differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma through MRI radiomics[J/OL]. Cancers, 2023, 15(22): 5373 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/38001633/. DOI: 10.3390/cancers15225373.
[15]
JIN Y M, WANG Y W, ZHANG J, et al. The value of radiomics in the differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma[J]. Chin J CT MRI, 2021, 19(11): 118-122, 126. DOI: 10.3969/j.issn.1672-5131.2021.11.038.
[16]
XIA J J, WANG T Y, CAI Q Y, et al. Application of MRI radiomics in the differential diagnosis of mixed liver cancer and intrahepatic cholangiocarcinoma[J]. Chin J Med Imaging, 2023, 31(9): 945-949, 955. DOI: 10.3969/j.issn.1005-5185.2023.09.009.
[17]
PENG Y T, LIN P, WU L Y, et al. Ultrasound-based radiomics analysis for preoperatively predicting different histopathological subtypes of primary liver cancer[J/OL]. Front Oncol, 2020, 10: 1646 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/33072550/. DOI: 10.3389/fonc.2020.01646.
[18]
XUE B H, WU S J, ZHENG M H, et al. Development and validation of a radiomic-based model for prediction of intrahepatic cholangiocarcinoma in patients with intrahepatic lithiasis complicated by imagologically diagnosed mass[J/OL]. Front Oncol, 2020, 10: 598253 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/33072550/. DOI: 10.3389/fonc.2020.598253.
[19]
XU Y, YE F, LI L, et al. MRI-based radiomics nomogram for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma from resectable colorectal liver metastases[J]. Acad Radiol, 2023, 30(9): 2010-2020. DOI: 10.1016/j.acra.2023.04.030.
[20]
ZHUO L Y, LI X M, DAI S, et al. Multiparametric MRI-based intratumoral and peritumoral radiomics for distinguishing solitary intrahepatic mass-forming cholangiocarcinoma from colorectal liver metastases[J/OL]. Cancer Med, 2025, 14(15): e71120[2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40762357/. DOI: 10.1002/cam4.71120.
[21]
YANG M C, LIU H Y, ZHANG Y M, et al. The diagnostic value of a nomogram based on enhanced CT radiomics for differentiating between intrahepatic cholangiocarcinoma and early hepatic abscess[J/OL]. Front Mol Biosci, 2024, 11: 1409060 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/39247207/. DOI: 10.3389/fmolb.2024.1409060.
[22]
WANG X C, LIANG J H, HUANG X Y, et al. A machine learning model based on CT radiomics for preoperatively differentiating intrahepatic mass-type cholangiocarcinoma and inflammatory pseudotumours[J/OL]. BMC Cancer, 2025, 25(1): 1106 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40597066/. DOI: 10.1186/s12885-025-14488-z.
[23]
WANG Q, QIAN X, ZHANG Y, et al. Multi-regional radiomics for predicting microvascular invasion and lymph node metastasis in intrahepatic cholangiocarcinoma[J/OL]. Clin Radiol, 2025, 88: 106979 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40706418/. DOI: 10.1016/j.crad.2025.106979.
[24]
LI X M, XING L H, ZHUO L Y, et al. Development and validation of a model for predicting pathological grade of intrahepatic mass-forming cholangiocarcinoma based on intratumoral and peritumoral features on MRI[J]. Chin J Magn Reson Imaging, 2025, 16(2): 51-58. DOI: 10.12015/issn.1674-8034.2025.02.008.
[25]
XING L H, WANG S P, ZHUO L Y, et al. Comparison of machine learning models using diffusion-weighted images for pathological grade of intrahepatic mass-forming cholangiocarcinoma[J]. J Imaging Inform Med, 2024, 37(5): 2252-2263. DOI: 10.1007/s10278-024-01103-z.
[26]
CHEN P Y, YANG Z W, NING P G, et al. To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions[J/OL]. Cancer Imaging, 2025, 25(1): 19 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40011960/. DOI: 10.1186/s40644-025-00842-8.
[27]
PENG Y T, PANG J S, LIN P, et al. Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers[J/OL]. BMC Med Imaging, 2025, 25(1): 4 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/39748308/. DOI: 10.1186/s12880-024-01542-8.
[28]
QIAN X L, NI X Y, MIAO G Y, et al. Association between MRI-based radiomics features and regional lymph node metastasis in intrahepatic cholangiocarcinoma and its clinical outcome[J]. Magnetic Resonance Imaging, 2025, 61(2): 997-1010. DOI: 10.1002/jmri.29477.
[29]
XIANG F, WEI S M, LIU X Y, et al. Radiomics analysis of contrast-enhanced CT for the preoperative prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma[J/OL]. Front Oncol, 2021, 11: 774117 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/34869018/. DOI: 10.3389/fonc.2021.774117.
[30]
LIU Z W, CHEN H X, YANG S M, et al. Preoperative prediction model of microvascular invasion in intrahepatic cholangiocarcinoma patients based on CT radiomics can assist clinical surgical decision-making: a multicenter study[J]. Eur Radiol, 2026, 36(2): 1395-1408. DOI: 10.1007/s00330-025-11900-x.
[31]
CHEN S, ZHU Y M, WAN L J, et al. Predicting the microvascular invasion and tumor grading of intrahepatic mass-forming cholangiocarcinoma based on magnetic resonance imaging radiomics and morphological features[J]. Quant Imaging Med Surg, 2023, 13(12): 8079-8093. DOI: 10.21037/qims-23-11.
[32]
MA X J, QIAN X L, WANG Q, et al. Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma[J]. La Radiol Med, 2023, 128(11): 1296-1309. DOI: 10.1007/s11547-023-01704-8.
[33]
LIU Z W, LUO C, CHEN X J, et al. Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study[J]. Int J Surg, 2024, 110(2): 1039-1051. DOI: 10.1097/JS9.0000000000000881.
[34]
CHEN M C, ZHOU X Q, LIU Z W, et al. Preoperative MRI prediction and molecular pathway study of perineural invasion in intrahepatic cholangiocarcinoma: Insights from bioinformatics approach[J/OL]. Eur J Surg Oncol, 2026, 52(1): 110546 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/41176814/. DOI: 10.1016/j.ejso.2025.110546.
[35]
XU Y, LI Z, YANG Y, et al. A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma[J/OL]. Insights Imaging, 2023, 14(1): 173 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/37840098/. DOI: 10.1186/s13244-023-01527-1.
[36]
XU Y, LI Z, YANG Y, et al. Association between MRI radiomics and intratumoral tertiary lymphoid structures in intrahepatic cholangiocarcinoma and its prognostic significance[J]. J Magn Reson Imaging, 2024, 60(2): 715-728. DOI: 10.1002/jmri.29128.
[37]
QIAN X L, ZHOU C W, WANG F, et al. Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics[J]. La Radiol Med, 2023, 128(3): 274-288. DOI: 10.1007/s11547-023-01597-7.
[38]
CHEN P Y, YANG Z W, ZHANG H F, et al. Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend[J/OL]. Front Oncol, 2023, 13: 1133867 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/37035147/. DOI: 10.3389/fonc.2023.1133867.
[39]
CHU H P, LIU Z L, LIANG W, et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma[J]. Eur Radiol, 2021, 31(4): 2368-2376. DOI: 10.1007/s00330-020-07250-5.
[40]
FIZ F, ROSSI N, LANGELLA S, et al. Radiomics of intrahepatic cholangiocarcinoma and peritumoral tissue predicts postoperative survival: development of a CT-based clinical-radiomic model[J]. Ann Surg Oncol, 2024, 31(9): 5604-5614. DOI: 10.1245/s10434-024-15457-9.
[41]
ZHANG J H, ZHAO Z X, CUI J, et al. Predicting postoperative survival time of mass-forming intrahepatic cholangiocarcinoma based on MRI radiomics and clinical features[J]. J Hepatopancreatobiliary Surg, 2021, 33(7): 407-410, 418. DOI: 10.11952/j.issn.1007-1954.2021.07.005.
[42]
KWON R, KIM H, AHN K S, et al. A machine learning-based clustering using radiomics of F-18 fluorodeoxyglucose positron emission tomography/computed tomography for the prediction of prognosis in patients with intrahepatic cholangiocarcinoma[J/OL]. Diagnostics (Basel), 2024, 14(19): 2245 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/39410649/. DOI: 10.3390/diagnostics14192245.
[43]
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.
[44]
BO Z Y, CHEN B, YANG Y, et al. Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: a multicentre cohort study[J]. Eur J Nucl Med Mol Imaging, 2023, 50(8): 2501-2513. DOI: 10.1007/s00259-023-06184-6.
[45]
GAN Y Q, CHEN Z Y, ZOU E G, et al. Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: a Multi-Center study[J]. Eur J Nucl Med Mol Imaging, 2026, 53(4): 2337-2350. DOI: 10.1007/s00259-025-07538-y.
[46]
XU L, WAN Y D, LUO C, et al. Integrating intratumoral and peritumoral features to predict tumor recurrence in intrahepatic cholangiocarcinoma[J/OL]. Phys Med Biol, 2021, 66(12) [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/34096890/. DOI: 10.1088/1361-6560/ac01f3.
[47]
HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510. DOI: 10.1038/s41568-018-0016-5.
[48]
RUNDO L, MILITELLO C. Image biomarkers and explainable AI: handcrafted features versus deep learned features[J/OL]. Eur Radiol Exp, 2024, 8(1): 130 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/39560820/. DOI: 10.1186/s41747-024-00529-y.
[49]
WU Q, ZHANG T, XU F, et al. MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: A multicenter study[J/OL]. Insights Imaging, 2025, 16(1): 27 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/39881111/. DOI: 10.1186/s13244-025-01904-y.
[50]
CHENG M, ZHANG H Y, GUO Y M, et al. Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma[J/OL]. Sci Rep, 2025, 15(1): 9629 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40113926/. DOI: 10.1038/s41598-025-92263-7.
[51]
WANG C, WANG C, WANG Q, et al. Preoperative MVI prediction in intrahepatic cholangiocarcinoma via deep learning analysis of intratumoral and peritumoral features on multi-sequence MRI[J/OL]. BMC Med Imaging, 2025, 26(1): 33 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/41366360/. DOI: 10.1186/s12880-025-02107-z.
[52]
QI Z C, YUAN H, LI Q S, et al. An MRI-based fusion model for preoperative prediction of perineural invasion status in patients with intrahepatic cholangiocarcinoma[J/OL]. World J Surg Oncol, 2025, 23(1): 164 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/40287750/. DOI: 10.1186/s12957-025-03819-w.
[53]
WAKIYA T, ISHIDO K, KIMURA N, et al. CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma[J/OL]. Sci Rep, 2022, 12: 8428 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/35590089/. DOI: 10.1038/s41598-022-12604-8.
[54]
ZHU Y, MAO Y F, CHEN J, et al. Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma[J/OL]. Sci Rep, 2021, 11: 6933 [2026-02-01]. https://pubmed.ncbi.nlm.nih.gov/33767315/. DOI: 10.1038/s41598-021-86497-4.
[55]
VIGANÒ L, ZANUSO V, FIZ F, et al. CT-based radiogenomics of intrahepatic cholangiocarcinoma[J]. Dig Liver Dis, 2025, 57(1): 118-124. DOI: 10.1016/j.dld.2024.06.033.

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