Selection of Optimal Pulse Sequences and Enhancement Phases of MRI Study for Radiomics Analysis in the Diagnosis of Early Hepatocellular Carcinoma

Purpose: To compare the importance of different MRI sequences and enhancement phases in creation of a diagnostic radiomics model in MRI diagnostics of early hepatocellular carcinoma (HCC).

Material and methods: Data from 72 patients with 93 masses who underwent Gadoxetic acid-enhanced MRI scans was retrospectively analyzed, a comparative assessment of the indicators of four sequences and enhancement phases of MRI studies was performed.

Results: As a result of the study, machine- learning radiomics based models on various MRI sequences and enhancement phases with high discriminatory capabilities were created. The area under the ROC curve (Area Under Curve, AUC) ranged from 0.58 to 0.94 in various models; the best results were performed in Random Forest model based on MRI-hepatobiliary enhancement phase — AUC 0.949684, the combination of different enhancement sequences — AUC 0.914342.

Conclusion: The hepatobiliary phase of MRI study independently, as well as the combination of four enhancement phases and sequences of MRI study, have the greatest discriminatory capabilities for creating machine- learning radiomics based models on enhanced MR images in diagnostics of early HCC.

1. Global Burden of Disease Liver Cancer Collaboration, Akinyemiju T, Abera S, et al. The Burden of Primary Liver Cancer and Underlying Etiologies From 1990 to 2015 at the Global, Regional, and National Level: Results From the Global Burden of Disease Study 2015. JAMA Oncol. 2017;3(12):1683-91. https://doi.org/10.1001/jamaoncol.2017.3055

2. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391(10127):1301-14. https://doi.org/10.1016/S0140-6736(18)30010-2

3. Каприн АД, Старинский ВВ, ˌахзадова АО и др. Состояние онкологической помощи населению России в 2022 году. М.: МНИОИ им. П.А. Герцена о филиал ФГБУ ͨНМИЦ радиологииͩ Минздрава России. 2022.

4. Kаrmаzаnovsky GG, Shantarevich MY, Stashkiv VI, et al. Reproducibility of CT and MRI texture features of hepatocellular carcinoma. Medical Visualization. 2023;27(3):84-93. (In Russ.) https://doi.org/10.24835/1607-0763-1372

5. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68(2):723-50. https://doi.org/10.1002/hep.29913

6. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182-236. https://doi.org/10.1016/j.jhep.2018.03.019

7. Motosugi U, Bannas P, Sano K, Reeder SB. Hepatobiliary MR contrast agents in hypovascular hepatocellular carcinoma. J Magn Reson Imaging. 2015;41(2):251-65. https://doi.org/10.1002/jmri.24712

8. Kierans AS, Fowler KJ, Chernyak V. LI-RADS in 2024: recent updates, planned refinements, and future directions. Abdom Radiol (NY). Published online December 13, 2024. https://doi.org/10.1007/s00261-024-04730-w

9. Karmazanovsky GG, Kondratyev EV, Gruzdev IS, et al. Radiation diagnostics and intelligent personalized technologies in hepatopancreatology. Vestnik Rossijskoj akademii nauk 2022;77(4):245-53 (In Russ.) https://doi.org/10.15690/vramn2053

10. Mirestean CC, Pagute O, Buzea C, et al. Radiomic Machine Learning and Texture Analysis — New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019;14(2):126-30. https://doi.org/10.26574/maedica.2019.14.2.126

11. Sagir Kahraman A. Radiomics in Hepatocellular Carcinoma. J Gastrointest Cancer. 2020;51(4):1165-8. https://doi.org/10.1007/s12029-020-00493-x

12. Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging. 2020;20(1):67. https://doi.org/10.1186/s40644-020-00341-y

13. Molostova IV, Medvedeva BM, Kondratyev EV et al. The capabilities of machine learning radiomics based models in the MRI diagnosis of early HCC. Journal of Oncology: Diagnostic Radiology and Radiotherapy. 2024;7(4):68-73. (In Russ.). https://doi.org/10.37174/2587-7593-2024-7-4-68-73

14. Navin PJ, Venkatesh SK. Hepatocellular Carcinoma: State of the Art Imaging and Recent Advances. J Clin Transl Hepatol. 2019;7(1):72-85. https://doi.org/10.14218/JCTH.2018.00032

15. Stashkiv VI, Shantarevich MY, Kаrmаzаnovsky GG Prediction of the degree of differentiation of hepatocellular carcinoma using texture analysis of magnetic resonance imaging. Diagnostic interventional Θ Radiology. 2023;17(3№1):48-57. (In Russ.) https://doi.org/10.25512/DIR.2023.17.3(1).07

16. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing plaƞorm for the Yuantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323-41. https://doi.org/10.1016/j.mri.2012.05.001

17. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

18.