Differential diagnosis of hepatocellular carcinoma and cirrhotic nodules via radiomics models based on magnetic resonance images

Changdong Ma, Changsheng Ma, Shuang Yu

Article ID: 4546
Vol 7, Issue 1, 2024

VIEWS - 2534 (Abstract) 2519 (PDF)

Abstract


Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI,86 features in spir phase of T2WI,86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.


Keywords


radiomics features; hepatocellular carcinoma; MRI; cirrhotic

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References


1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68(6): 394–424. doi:10.3322/caac.21492

2. McGlynn KA, London WT. Epidemiol-ogy and natural history of hepatocellu-lar car cinoma. Best Pract Res Clin Gas-troenterol. 2005;19(1): 3–23.

3. Theise ND, Curado MP, Franceschi S, et al. Hepatocellular carcinoma. In: Bosman FT, Carneiro F, Hruban RH, Theise ND. (editors). WHO Cassification of Tumours of the Di gestive System. IARC Publishing; 2010. pp. 205–216.

4. Choi JY, Lee JM, Sirlin CB. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part I. Devel-opment, growth, and spread: Key path-ologic and imaging aspects. Radiology. 2014; 272(3): 635–654.

5. Ronot M, Purcell Y, Vilgrain V. Hepato-cellular Carcinoma: Current Imaging Modalities for Diagnosis and Prognosis. Dig Dis Sci. 2019; 64(4): 934–950. doi:10.1007/s10620-019-05547-0

6. Forner A, Vilana R, Ayuso C, et al. Diag nosis of hepatic nodules 20 mm or smaller in cirrhosis: Prospective valida-tion of the noninvasive diagnostic cri-teria for hepato cellular carcinoma. Hepatology 2008; 47(1): 97–104.

7. Huang JY, Li JW, Lu Q, et al. Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma. Radiology. 2020; 294(2): 329–339.

8. Chen X, Yang Z, Deng J. Use of 64-Slice Spiral CT Examinations for Hepatocel-lular Carcinoma (DR LU). J BUON. 2019; 24(4): 1435–1440

9. Di Martino M, De Filippis G, De Santis A, et al. Hepatocellular carcinoma in cir-rhotic patients: prospective comparison of US, CT and MR imaging. Eur Radiol. 2013; 23(4): 887–896. doi:10.1007/s00330-012-2691-z

10. Sun XJ, Quan XY, Huang FH, Xu YK. Quantitative evaluation of diffusion-weighted magnetic resonance imaging of focal hepatic lesions. World J Gastro-enterol. 2005; 11(41): 6535–6537. doi:10.3748/wjg.v11.i41.6535

11. International Working Party. Terminol-ogy of nodular hepatocellular lesions. Hepatol ogy. 1995; 22(3): 983–993.

12. Park YN, Kim MJ. Hepatocarcinogenesis: imaging-pathologic correlation. Ab-dom Im aging 2011; 36(3): 232–243.

13. Aihara T, Noguchi S, Sasaki Y, Nakano H, Imaoka S. Clonal analysis of regen-erative nodules in hepatitis C virus-in-duced liver cirrhosis. Gastroenterology. 1994; 107(6): 1805– 1811.

14. Trevisani F, Cantarini MC, Wands JR, Bernardi M. Recent advances in the nat ural history of hepatocellular carcinoma. Carcinogenesis. 2008; 29(7): 1299–1305.

15. Brody RI, Theise ND. An inflammatory proposal for hepatocarcinogenesis. Hepa tology 2012; 56(1): 382–384.

16. Thorgeirsson SS, Grisham JW. Molecu-lar pathogenesis of human hepatocel-lular car cinoma. Nat Genet, 2002; 31(4): 339–346.

17. Theise ND. Macroregenerative (dys-plastic) nodules and hepatocarcino-genesis: theo retical and clinical consid-erations. Semin Liver Dis .1995;1 5(4): 360–371.

18. Aravalli RN, Cressman EN, Steer CJ. Cel lular and molecular mechanisms of hepato cellular carcinoma: An update. Arch Toxicol. 2013; 87(2): 227–247.

19. Sun M, Eshleman JR, Ferrell LD, et al. An early lesion in hepatic carcinogenesis: loss of heterozygosity in human cir-rhotic livers and dysplastic nodules at the 1p36-p34 region. Hepatology .2001; 33(6): 1415–1424.

20. Park YN. Update on precursor and early lesions of hepatocellular carcinomas. Arch Pathol Lab Med. 2011; 135(6): 704–715.

21. Roskams T, Kojiro M. Pathology of early hepatocellular carcinoma: conventional and molecular diagnosis. Semin Liver Dis 2010; 30(1): 17–25.

22. Stevens WR, Gulino SP, Batts KP, et al. Mosaic pattern of hepatocellular carcinoma: histologic basis for a characteristic CT appearance. J Com put Assist Tomogr. 1996; 20(3): 337–342.

23. Lambin P, Rios-Velazquezet E, Leijenaaral R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012; 48: 441–446.

24. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn. Reson. Im-aging. 2012; 30, 1234–1248.

25. Haase AT, Henry K, Zupancic M, et al. Quantitative image analysis of HIV-1 infection in lymphoid tissue. Science. 1996; 274, 985–989.

26. Schoolman H, Bernstein L. Com-puter use in diagnosis, prognosis, and therapy. Science. 1978; 200: 926–931.

27. Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020; 196(10): 879–887. doi:10.1007/s00066-020-01625-9

28. Jiang Y, Chen C, Xie J, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine. 2018; 36: 171–182. doi:10.1016/j.ebiom.2018.09.007

29. Smith CP, Czarniecki M, Mehralivand S, et al. Radiomics and radiogenomics of prostate cancer. Abdom Radiol (NY). 2019; 44(6): 2021–2029. doi:10.1007/s00261-018-1660-7

30. Tsai A, Buch K, Fujita A, et al. Using CT texture analysis to differentiate between naso-pharyngeal carcinoma and age-matched adenoid controls. Eur J Radiol. 2018; 108: 208–14.

31. Thawani R, McLane M, Beig N, et al. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer. 2018; 115: 34–41. doi:10.1016/j.lungcan.2017.10.015

32. Wei K, Su H, Zhou G,et al. Potential application of radiomics for differentiating solitary pul-monary nodules,OMICS J Ra-diol. 2016; 5(2): 1000218




DOI: https://doi.org/10.24294/irr4546

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