Radiomics and radiogenomics in intrahepatic cholangiocarcinoma

Purpose of the study. Analytical review and analysis of available literature on texture analysis of computed tomgraphy (CT) and magnetic resonance imaging (MRI) in noninvasive diagnosis of ICC and correlation with molecular genetic features and tumor immunophenotype.

Materials and methods. The scientific publications and clinical guidelines in the information- analytical systems PubMed, Scopus for 2012–2022 were carried out using the keywords: "mri", "radiomics", "texture analysis", "radiogenomics", "intrahepatic cholangiocarcinoma", "molecular". 49 articles were selected for analysis after excluding studies dealing with technical aspects of radiomics and describing individual clinical observations.

Results. The presented review demonstrated the broad possibilities and prospects of application of CT and MRI texture analysis in the study of cholangiocellular cancer, including the first results in the study of molecular features (signatures) of this tumor. Correlation of texture features with the expression of immunotherapy target genes KRAS/NRAS/BRAF as well as IDH1/2 mutation was shown. Texture scores were the predominant independent predictor of microvascular invasion, which was a major independent risk factor for postoperative recurrence.

Conclusion. The use of texture analysis undoubtedly demonstrates promising possibilities both in noninvasive assessment of the HCC histological differentiation grade, as well as in differential diagnosis with hepatocellular carcinoma, metastases, and requires further study for systematization and standardization of the obtained data.

1. Gurmikov BN, Kovalenko YuA, Vishnevsky VA, Chzhao AV. Molecular genetic aspects of intrahepatic cholangiocarcinoma: literature review. Advances in Molecular Oncology. 2019;6(1):37–43. (In Russ.). https://doi.org/10.17650/2313-805x-2019-6-1-37-43

2. Karmazanovsky GG. The role of MDCT and MRI in the diagnosis of focal liver diseases. Annaly khirurgicheskoy gepatologii (Annals of HPB Surgery). 2019;24(4):91–110. (In Russ.). https://doi.org/10.16931/1995-5464.2019491-110

3. Rizvi S, Khan SA, Hallemeier CL, Kelley RK, Gores GJ. Cholangiocarcinoma – evolving concepts and therapeutic strategies. Nat Rev Clin Oncol. 2018 Feb;15(2):95–111. https://doi.org/10.1038/nrclinonc.2017.157

4. Razumilava N, Gores GJ. Cholangiocarcinoma. Lancet. 2014 Jun 21;383(9935):2168–79. https://doi.org/10.1016/s0140-6736(13)61903-0

5. Gurmikov BN, Kovalenko YuA, Vishnevsky VA, Chzhao AV. Intrahepatic cholangiocellular carcinoma: diagnosis and treatment (review). Annaly khirurgicheskoy gepatologii (Annals of HPB Surgery). 2018;23(4):108–117. (In Russ.). https://doi.org/10.16931/1995-5464.20184108-117

6. Chang YT, Chang MC, Huang KW, Tung CC, Hsu C, Wong JM. Clinicopathological and prognostic significances of EGFR, KRAS and BRAF mutations in biliary tract carcinomas in Taiwan. J Gastroenterol Hepatol. 2014 May;29(5):1119–1125. https://doi.org/10.1111/jgh.12505

7. Abou-Alfa GK, Macarulla T, Javle MM, Kelley RK, Lubner SJ, Adeva J, et al. Ivosidenib in IDH1-mutant, chemotherapy-refractory cholangiocarcinoma (ClarIDHy): a multicentre, randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2020 Jun;21(6):796–807. https://doi.org/10.1016/s1470-2045(20)30157-1 Epub 2020 May 13. Erratum in: Lancet Oncol. 2020 Oct;21(10):e462. Erratum in: Lancet Oncol. 2024 Feb;25(2):e61.

8. Wang Sh, Wu Y, Lui T, Weng Sh, You H, Wei Y, et al. Amplification and overexpression of the MET gene in intrahepatic cholangiocarcinoma correlate with adverse pathological features and worse clinical outcome. Int J Clin Exp Pathol. 2017;10(6):6809–6817.

9. Zhang J, Wu Z, Zhao J, Liu S, Zhang X, Yuan F, Shi Y, Song B. Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival. Eur Radiol. 2021 Jun;31(6):3661–3672. https://doi.org/10.1007/s00330-020-07524-y

10. Sadot E, Simpson AL, Do RK, Gonen M, Shia J, Allen PJ, et al. Cholangiocarcinoma: Correlation between Molecular Profiling and Imaging Phenotypes. PLoS One. 2015 Jul 24;10(7):e0132953. https://doi.org/10.1371/journal.pone.0132953

11. Popov EV, Krivonogov NG, Okrugin SA, Sazonova SI. Radiomic image analysis in cardiology: possibilities and prospects of application: a review. Diagnostic radiology and radiotherapy. 2022;13(2):7–15. (In Russ.). https://doi.org/10.22328/2079-5343-2022-13-2-7-15

12. Ma X, Liu L, Fang J, Rao S, Lv L, Zeng M, et al. MRI features predict microvascular invasion in intrahepatic cholangiocarcinoma. Cancer Imaging. 2020 Jun 23;20(1):40. https://doi.org/10.1186/s40644-020-00318-x

13. Shao C, Chen J, Chen J, Shi J, Huang L, Qiu Y. Histological classification of microvascular invasion to predict prognosis in intrahepatic cholangiocarcinoma. Int J Clin Exp Pathol. 2017 Jul 1;10(7):7674–7681.

14. Gurmikov BN., Zhao AV, et al. Cholangiocellular carcinoma. Monograph. Moscow: GEOTAR-Media; 2021; pp. 5–20. (In Russ.).

15. Zhang H, Yang T, Wu M, Shen F. Intrahepatic cholangiocarcinoma: Epidemiology, risk factors, diagnosis and surgical management. Cancer Lett. 2016 Sep 1;379(2):198–205. dhttps://doi.org/10.1016/j.canlet.2015.09.008

16. Chung YE, Kim MJ, Park YN, Choi JY, Pyo JY, Kim YC, et al. Varying appearances of cholangiocarcinoma: radiologic-pathologic correlation. Radiographics. 2009 May-Jun;29(3):683–700. https://doi.org/10.1148/rg.293085729

17. Fujita N, Asayama Y, Nishie A, Ishigami K, Ushijima Y, Takayama Y, et al. Mass-forming intrahepatic cholangiocarcinoma: Enhancement patterns in the arterial phase of dynamic hepatic CT – Correlation with clinicopathological findings. Eur Radiol. 2017 Feb;27(2):498–506. https://doi.org/10.1007/s00330-016-4386-3

18. Chalasani N, Baluyut A, Ismail A, Zaman A, Sood G, Ghalib R, et al. Cholangiocarcinoma in patients with primary sclerosing cholangitis: a multicenter case-control study. Hepatology. 2000 Jan;31(1):7–11. https://doi.org/10.1002/hep.510310103

19. Nanashima A, Sakamoto I, Hayashi T, Tobinaga S, Araki M, Kunizaki M, et alT. Preoperative diagnosis of lymph node metastasis in biliary and pancreatic carcinomas: evaluation of the combination of multi-detector CT and serum CA19-9 level. Dig Dis Sci. 2010 Dec;55(12):3617–3626. https://doi.org/10.1007/s10620-010-1180-y

20. Noji T, Kondo S, Hirano S, Tanaka E, Suzuki O, Shichinohe T. Computed tomography evaluation of regional lymph node metastases in patients with biliary cancer. Br J Surg. 2008 Jan;95(1):92–96. https://doi.org/10.1002/bjs.5920

21. Zhan PC, Yang T, Zhang Y, Liu KY, Li Z, Zhang YY, et al. Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study. Eur Radiol. 2023 Aug 17. https://doi.org/10.1007/s00330-023-10108-1

22. Spolverato G, Kim Y, Alexandrescu S, Marques HP, Lamelas J, Aldrighetti L, et al. Management and Outcomes of Patients with Recurrent Intrahepatic Cholangiocarcinoma Following Previous Curative-Intent Surgical Resection. Ann Surg Oncol. 2016 Jan;23(1):235–243. https://doi.org/10.1245/s10434-015-4642-9

23. Ciresa M, De Gaetano AM, Pompili M, Saviano A, Infante A, Montagna M, et al. Enhancement patterns of intrahepatic mass-forming cholangiocarcinoma at multiphasic computed tomography and magnetic resonance imaging and correlation with clinicopathologic features. Eur Rev Med Pharmacol Sci. 2015 Aug;19(15):2786–2797.

24. Park HJ, Park B, Park SY, Choi SH, Rhee H, Park JH, et al. Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features. Eur Radiol. 2021 Nov;31(11):8638–8648. https://doi.org/10.1007/s00330-021-07926-6

25. Hu LS, Weiss M, Popescu I, Marques HP, Aldrighetti L, Maithel SK, et al. Impact of microvascular invasion on clinical outcomes after curative-intent resection for intrahepatic cholangiocarcinoma. J Surg Oncol. 2019 Jan;119(1):21–29. https://doi.org/10.1002/jso.25305

26. Zhang L, Yu X, Wei W, Pan X, Lu L, Xia J, et al. Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: correlation with pathology. Eur Radiol. 2020 Oct;30(10):5327–5336. https://doi.org/10.1007/s00330-020-06895-6

27. Silva M, Maddalo M, Leoni E, Giuliotti S, Milanese G, Ghetti C, et al. Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics. Abdom Radiol (NY). 2021 Oct;46(10):4689–4700. https://doi.org/10.1007/s00261-021-03183-9

28. Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020 Apr;21(4):387–401. https://doi.org/10.3348/kjr.2019.0752

29. Pavic M, Bogowicz M, Würms X, Glatz S, Finazzi T, Riesterer O, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol. 2018 Aug;57(8):1070–1074. https://doi.org/10.1080/0284186x.2018.1445283

30. Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44(3):1050–1062. https://doi.org/10.1002/mp.12123

31. Grobmyer SR, Wang L, Gonen M, Fong Y, Klimstra D, D'Angelica M, et al. Perihepatic lymph node assessment in patients undergoing partial hepatectomy for malignancy. Ann Surg. 2006 Aug;244(2):260–264. https://doi.org/10.1097/01.sla.0000217606.59625.9d

32. Zhang S, Huang S, He W, Wei J, Huo L, Jia N, et al. Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography. Ann Surg Oncol. 2022 Oct;29(11):6786–6799. https://doi.org/10.1245/s10434-022-12028-8

33. Liang W, Xu L, Yang P, Zhang L, Wan D, Huang Q, et al. Novel Nomogram for Preoperative Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma. Front Oncol. 2018 Sep 4;8:360. https://doi.org/10.3389/fonc.2018.00360

34. Chu H, Liu Z, Liang W, Zhou Q, Zhang Y, Lei K, et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma. Eur Radiol. 2021 Apr;31(4):2368–2376. https://doi.org/10.1007/s00330-020-07250-5

35. Zhu Y, Mao Y, Chen J, Qiu Y, Guan Y, Wang Z, He J. Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep. 2021 Sep 15;11(1):18347. https://doi.org/10.1038/s41598-021-97796-1

36. Zhou Y, Zhou G, Zhang J, Xu C, Wang X, Xu P. Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol. 2021 Sep;31(9):6846–6855. https://doi.org/10.1007/s00330-021-07793-1

37. Qian X, Lu X, Ma X, Zhang Y, Zhou C, Wang F, et al. A Multi-Parametric Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion Status in Intrahepatic Cholangiocarcinoma. Front Oncol. 2022 Feb 24;12:838701. https://doi.org/10.3389/fonc.2022.838701

38. Yang Y, Zou X, Zhou W, Yuan G, Hu D, Kuang D, et al. Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy. J Magn Reson Imaging. 2022 Sep;56(3):739–751. https://doi.org/10.1002/jmri.28071

39. Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019 Jun;70(6):1133–1144. https://doi.org/10.1016/j.jhep.2019.02.023

40. Hennedige TP, Neo WT, Venkatesh SK. Imaging of malignancies of the biliary tract- an update. Cancer Imaging. 2014 Apr 22;14(1):14. https://doi.org/10.1186/1470-7330-14-14

41. Xu X, Mao Y, Tang Y, Liu Y, Xue C, Yue Q, et al. Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis. Comput Math Methods Med. 2022 Feb 21;2022:5334095. https://doi.org/10.1155/2022/5334095

42. Banales JM, Marin JJG, Lamarca A, Rodrigues PM, Khan SA, Roberts LR, et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat Rev Gastroenterol Hepatol. 2020 Sep;17(9):557–588. https://doi.org/10.1038/s41575-020-0310-z

43. Zhu H, Ji K, Wu W, Zhao S, Zhou J, Zhang C, et al. Describing Treatment Patterns for Elderly Patients with Intrahepatic Cholangiocarcinoma and Predicting Prognosis by a Validated Model: A Population-Based Study. J Cancer. 2021 Mar 30;12(11):3114–3125. https://doi.org/10.7150/jca.53978

44. Perrin T, Midya A, Yamashita R, Chakraborty J, Saidon T, Jarnagin WR, et al. Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging. Abdom Radiol (NY). 2018 Dec;43(12):3271–3278. https://doi.org/10.1007/s00261-018-1600-6

45. Banerjee S, Wang DS, Kim HJ, Sirlin CB, Chan MG, Korn RL, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology. 2015 Sep;62(3):792–800. https://doi.org/10.1002/hep.27877

46. Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol. 2018 May;28(5):2058–2067. https://doi.org/10.1007/s00330-017-5146-8

47. Hoivik EA, Hodneland E, Dybvik JA, Wagner-Larsen KS, Fasmer KE, Berg HF, et al. A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol. 2021 Dec 6;4(1):1363. https://doi.org/10.1038/s42003-021-02894-5

48. Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007 Jun;25(6):675–680. https://doi.org/10.1038/nbt1306

49. Idris T, Barghash M, Kotrotsou A, Huang HJ, Subbiah V, Kaseb AO, et al. CT-based radiogenomic signature to identify isocitrate dehydrogenase(IDH)1/2 mutations in advanced intrahepatic cholangiocarcinoma. Journal of Clinical Oncology. 2019;37(15). https://doi.org/10.1200/jco.2019.37.15_suppl.4081