Application of texture analysis of CT and MR images to determine the histologic grade of hepatocellular cancer and it’s differential diagnosis: a review

In recent years, more foreign publications are devoted to the use of texture analysis or radiomics in solving certain diagnostic problems, including the diagnosis of hepatocellular cancer (HCC). This method of processing medical images allows for a comprehensive assessment of the structure of neoplasms by extracting a large number of quantitative features from medical images.

The purpose of the study was to determine the role of texture analysis of CT and MR images in differential diagnosis and determination of the degree of differentiation of HCC based on a review and analysis of the results of publications.

We searched for scientific publications in the PubMed information and analytical system for 2015–2021. by keywords: “HCC”, “texture analysis” (texture analysis), “radiomics”, “CT”, “MRI”, “grade”, “differential diagnosis”. After excluding reviews of publications and studying the full text of articles, 21 articles were selected for analysis.

Despite the growing number of publications devoted to the successful use of textural analysis of CT and MR images, including non-invasive assessment of the histological grade of HCC and in the differential diagnosis of HCC with hypervascular neoplasms, metastases, regenerative and dysplastic nodes, the use of such type of analysis in routine practice is limited due to the lack of standardized methods for performing texture analysis, which leads to low reproducibility of the results. The parameters of image acquisition and methods of image preprocessing and segmentation affect the reproducibility of the obtained texture features. In addition, the presented studies were performed using different MR sequences and phases of contrast enhancement, as well as different software, which makes it difficult to compare the obtained data.

The use of texture analysis certainly demonstrates promising results and requires further investigation to systematize and standardize the obtained data in order to develop an optimal diagnostic model for wide clinical use.

1. The state of oncological care to the population of Russia in 2019. Ed. by A. D. Kaprina, V. V. Starinsky, A. O. Shakhzadova. Moscow: P.A. Herzen Moscow State Medical Research Institute − Branch of the Federal State Budgetary Institution "NMIC of Radiology" of the Ministry of Health of Russia, 2020, 252 p. (In Russ.).

2. Hanna RF, Miloushev VZ, Tang A, Finklestone LA, Brejt SZ, Sandhu RS, et al. Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY). 2016 Jan;41(1):71–90. https://doi.org/10.1007/s00261-015-0592-8

3. An C, Lee CH, Byun JH, Lee MH, Jeong WK, Choi SH, et al. Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study. Korean J Radiol. 2019 Dec;20(12):1616–1626. https://doi.org/10.3348/kjr.2019.0363

4. Martins-Filho SN, Paiva C, Azevedo RS, Alves VAF. Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne). 2017;4:193. https://doi.org/10.3389/fmed.2017.00193

5. Okusaka T, Okada S, Ueno H, Ikeda M, Shimada K, Yamamoto J, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathologic features. Cancer. 2002 Nov 1;95(9):1931–1937. https://doi.org/10.1002/cncr.10892

6. Nishie A, Yoshimitsu K, Okamoto D, Tajima T, Asayama Y, Ishigami K, et al. CT prediction of histological grade of hypervascular hepatocellular carcinoma: utility of the portal phase. Jpn J Radiol. 2013 Feb;31(2):89–98. https://doi.org/10.1007/s11604-012-0149-5

7. Lomovtseva KKh. Differential diagnosis of solid-structure liver formations: the role of diffusion-weighted images and hepatospecific contrast media: Dissertation. Moscow, 2018, 140 p. (In Russ.).

8. Jeong WK, Jamshidi N, Felker ER, Raman SS, Lu DS. Radiomics and radiogenomics of primary liver cancers. Clin Mol Hepatol. 2019 Mar;25(1):21–29. https://doi.org/10.3350/cmh.2018.1007

9. Oh J, Lee JM, Park J, Joo I, Yoon JH, Lee DH, et al. Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean J Radiol. 2019 Apr;20(4):569–579. https://doi.org/10.3348/kjr.2018.0501

10. Mao B, Zhang L, Ning P, Ding F, Wu F, Lu G, et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol. 2020 Dec;30(12):6924–6932. https://doi.org/10.1007/s00330-020-07056-5

11. Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, et al. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol. 2021;11:660509. https://doi.org/10.3389/fonc.2021.660509

12. Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2019 Jun;29(6):2802–2811. https://doi.org/10.1007/s00330-018-5787-2

13. Geng Z, Zhang Y, Wang S, Li H, Zhang C, Yin S, et al. Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features. Magn Reson Med Sci. 2021 Sep 1;20(3):253–263. https://doi.org/10.2463/mrms.mp.2020-0060

14. Chen W, DelProposto Z, Liu W, Kassir M, Wang Z, Zhao J, et al. Susceptibility-weighted imaging for the noncontrast evaluation of hepatocellular carcinoma: a prospective study with histopathologic correlation. PLoS One. 2014;9(5):e98303. https://doi.org/10.1371/journal.pone.0098303

15. Yang S, Lin J, Lu F, Han Z, Fu C, Gu H. Use of Ultrasmall Superparamagnetic Iron Oxide Enhanced Susceptibility Weighted Imaging and Mean Vessel Density Imaging to Monitor Antiangiogenic Effects of Sorafenib on Experimental Hepatocellular Carcinoma. Contrast Media Mol Imaging. 2017;2017:9265098. https://doi.org/10.1155/2017/9265098

16. Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017 May;45(5):1476–1484. https://doi.org/10.1002/jmri.25454

17. Feng M, Zhang M, Liu Y, Jiang N, Meng Q, Wang J, et al. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study. BMC Cancer. 2020 Jun 30;20(1):611. https://doi.org/10.1186/s12885-020-07094-8

18. Yang X, Yuan C, Zhang Y, Wang Z. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study. Medicine (Baltimore). 2021 May 14;100(19):e25838. https://doi.org/10.1097/MD.0000000000025838

19. Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020 Jan;30(1):558–570. https://doi.org/10.1007/s00330-019-06347-w

20. Zhong X, Tang H, Lu B, You J, Piao J, Yang P, et al. Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging. Front Oncol. 2019;9:1382. https://doi.org/10.3389/fonc.2019.01382

21. Zhong X, Guan T, Tang D, Li J, Lu B, Cui S, et al. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol. 2021 Apr 7;21(1):155. https://doi.org/10.1186/s12876-021-01710-y

22. Raman SP, Schroeder JL, Huang P, Chen Y, Coquia SF, Kawamoto S, et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress. J Comput Assist Tomogr. 2015 Jun;39(3):383–395. https://doi.org/10.1097/RCT.0000000000000217

23. Stocker D, Marquez HP, Wagner MW, Raptis DA, Clavien PA, Boss A, et al. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon. 2018 Nov;4(11):e00987. https://doi.org/10.1016/j.heliyon.2018.e00987

24. Wu J, Liu A, Cui J, Chen A, Song Q, Xie L. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images. BMC Med Imaging. 2019 Mar 11;19(1):23. https://doi.org/10.1186/s12880-019-0321-9

25. Nie P, Yang G, Guo J, Chen J, Li X, Ji Q, et al. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver. Cancer Imaging. 2020 Feb 24;20(1):20. https://doi.org/10.1186/s40644-020-00297-z

26. Nie P, Wang N, Pang J, Yang G, Duan S, Chen J, et al. CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver. Acad Radiol. 2021 Jun;28(6):799–807. https://doi.org/10.1016/j.acra.2020.04.027

27. Song S, Li Z, Niu L, Zhou X, Wang G, Gao Y, et al. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification. Clin Radiol. 2019 Aug;74(8):653.e11–653.e18. https://doi.org/10.1016/j.crad.2019.05.010

28. Oyama A, Hiraoka Y, Obayashi I, Saikawa Y, Furui S, Shiraishi K, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep. 2019 Jun 19;9(1):8764. https://doi.org/10.1038/s41598-019-45283-z

29. Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017 Jul 13;17(1):42. https://doi.org/10.1186/s12880-017-0212-x

30. Liang W, Shao J, Liu W, Ruan S, Tian W, Zhang X, et al. Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models. Front Oncol. 2020;10:564307. https://doi.org/10.3389/fonc.2020.564307

31. Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2021 Jan;31(1):244–255. https://doi.org/10.1007/s00330-020-07119-7

32. Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015 Nov;50(11):757–765. https://doi.org/10.1097/RLI.0000000000000180

33. Hu HT, Shan QY, Chen SL, Li B, Feng ST, Xu EJ, et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med. 2020 Aug;125(8):697–705. https://doi.org/10.1007/s11547-020-01174-2

34. Mackin D, Ger R, Dodge C, Fave X, Chi PC, Zhang L, et al. Effect of tube current on computed tomography radiomic features. Sci Rep. 2018 Feb 5;8(1):2354. https://doi.org/10.1038/s41598-018-20713-6

35. 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

36. Li Y, Tan G, Vangel M, Hall J, Cai W. Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study. Quant Imaging Med Surg. 2020 Sep;10(9):1775–1785. https://doi.org/10.21037/qims-19-921

37. 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 Mar;44(3):1050–1062. https://doi.org/10.1002/mp.12123

38. Leijenaar RTH, Nalbantov G, Carvalho S, van Elmpt WJC, Troost EGC, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015 Aug 5;5:11075. https://doi.org/10.1038/srep11075

39. Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013 Feb;82(2):342–348. https://doi.org/10.1016/j.ejrad.2012.10.023

40. Park HJ, Kim JH, Choi SY, Lee ES, Park SJ, Byun JY, et al. Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings. AJR Am J Roentgenol. 2017 Oct;209(4):W211–W220. https://doi.org/10.2214/AJR.16.17398

41. Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020 Apr;93(1108):20190948. https://doi.org/10.1259/bjr.20190948

42. Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020 Jan;30(1):558–570. https://doi.org/10.1007/s00330-019-06347-w

43. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328–338. https://doi.org/10.1148/radiol.2020191145