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Dergiler / Marmara Medical Journal / 2026 / Volume: 39 Issue: 2

Assessment of the correlation between MRI texture analysis findings and histopathological diagnoses in solid liver lesions

Gülçin Zengin(MARMARA UNIVERSITY)0000-0002-4273-6425
Tahsin Aybal(Kartal Dr Lütfi Kırdar City Hospital)0000-0003-2597-6221
Burcu Özdemir(Marmara University)0000-0002-7486-6590
Çiğdem Çelikel(Marmara University)0000-0002-4427-0569
Rabia Ergelen(Marmara University)0000-0002-9647-4125
Sayfa
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Yayın Tarihi
2026-05-21
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Pre-contrast T1W sequences were the most informative, highlighting the potential utility of texture analysis for patients unable to receive contrast agents.

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Amaç & Hipotez:Objective: To evaluate the diagnostic value of texture analysis in differentiating benign and malignant liver lesions, as well as distinguishing primary malignant liver tumors from metastases using pre-contrast magnetic resonance imaging (MRI) sequences.
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Temel Bulgular & Veri:Materials and Methods: This retrospective study included 101 liver lesions from 100 patients who underwent upper abdominal MRI followed by biopsy or surgical resection.
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Sonuç & Katkı:Pre-contrast T1W sequences were the most informative, highlighting the potential utility of texture analysis for patients unable to receive contrast agents.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Solid liver lesions · Magnetic resonance imaging · Texture analysis

Abstract

Objective: To evaluate the diagnostic value of texture analysis in differentiating benign and malignant liver lesions, as well as distinguishing primary malignant liver tumors from metastases using pre-contrast magnetic resonance imaging (MRI) sequences. Materials and Methods: This retrospective study included 101 liver lesions from 100 patients who underwent upper abdominal MRI followed by biopsy or surgical resection. Texture analysis was performed on pre-contrast T1-weighted (T1W), T2-weighted (T2W), and apparent diffusion coefficient (ADC) sequences using first-order (histogram-based) and second-order (gray level co-occurrence matrix (GLCM)-based) statistical parameters. Group comparisons were conducted using Mann-Whitney U test, with a significance threshold of p < 0.05. Receiver operating characteristic (ROC) analysis was used to demonstrate the success of texture analysis parameters in distinguishing lesion subtypes. Results: Among 101 lesions, 90 were malignant and 11 were benign. Pre-contrast T1W, first-order parameters—mean, 50th, 90th, and 99th percentiles were significantly lower in malignant lesions compared to benign lesions. Primary malignant tumors and hepatocellular carcinoma (HCC) exhibited higher first-order values than metastatic lesions. Certain second-order parameters from pre-contrast T1W images also showed statistical significance, while ADC and T2W sequences revealed fewer differences. Conclusions: Texture analysis of pre-contrast MRI provides quantitative and objective evaluation of liver lesions, aiding differentiation between benign and malignant lesions and between primary and metastatic tumors. Pre-contrast T1W sequences were the most informative, highlighting the potential utility of texture analysis for patients unable to receive contrast agents.

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Bu Makaleye Atıf Yap

Zengin, G., Aybal, T., Özdemir, B., Çelikel, Ç., Ergelen, R. (2026). Assessment of the correlation between MRI texture analysis findings and histopathological diagnoses in solid liver lesions. Marmara Medical Journal, Volume: 39 Issue: 2. https://doi.org/10.5472/marumj.1953909

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Kaynakça (27)

  1. Liu PS, Francis IR. Hepatic imaging for metastatic disease. Cancer J 2010;16:93-102. doi: 10.1097/PPO.0b013e3181d7ea21
  2. Liu PS. Liver mass evaluation in patients without cirrhosis. Radiol Clin North Am 2015;53:903-18. doi: 10.1016/j. rcl.2015.05.008
  3. Mainenti PP, Mancini M, Mainolfi C, et al. Detection of colorectal liver metastases: prospective comparison of contrast enhanced US, multidetector CT, PET/CT, and 1.5 Tesla MR with extracellular and reticulo-endothelial cell specific contrast agents. Abdom Imaging 2010;35:511-21 doi: 10.1007/ s00261-009-9555-2.
  4. Guglielmo FF, Mitchell DG, Roth CG, Deshmukh S. Hepatic MR imaging techniques, optimization, and artifacts. Magn Reson Imaging Clin N Am 2014;22:263-82. doi: 10.1016/j. mric.2014.04.004
  5. Liu PS, Hussain HK. Contemporary and emerging technologies in abdominal magnetic resonance imaging. Semin Roentgenol 2013;48:203-13. doi: 10.1053/j.ro.2013.03.008
  6. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017;37:1483-503. doi: 10.1148/rg.2017170056.
  7. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-77. doi: 10.1148/radiol.2015151169.
  8. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004;59:1061-69. doi: 10.1016/j. crad.2004.07.008
  9. Stocker D, Marquez HP, Wagner MW, et al. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon 2018;4:e00987. doi: 10.1016/j.heliyon.2018.e00987
  10. Chernyak V, Horowitz JM, Kamel IR, et al. ACR appropriateness criteria liver lesion-initial characterization. J Am Coll Radiol 2020;17:S429-S446. doi: 10.1016/j.jacr.2020.09.005
  11. Singal AG, Llovet JM, Yarchoan M, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology 2023;78:1922-65.doi: 10.1097/HEP.0000000000000466
  12. Galle PR, Forner A, Llovet JM, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 2018;69:182-36. doi: 10.1016/j.jhep.2018.03.019
  13. Rybczynska DN, Markiet KE, Pienkowska JM, Szurowska E, Frydrychowski A. To assess the quantitative features of focal liver lesions in gadoxetic acid enhanced MRI and to determine whether these features can accurately differentiate benign form malignant lesions. Eur J Radiol 2024;171:111288. doi: 10.1016/j.ejrad.2024.111288
  14. Sandrasegaran K, Akisik FM, Lin C, Tahir B, Rajan J, Aiesen AM. The value of diffusionweighted imaging in characterizing focal liver masses. Acad Radiol 2009;16:1208-14. doi: 10.1016/j.acra.2009.05.013
  15. Sutherland T, Steele E, Van Tonder F, Yap K. Solid focal liver lesion characterisation with apparent diffusion coefficient ratios. J Med Imaging Radiat Oncol 2014;58:32-7. doi: 10.1111/1754-9485.12087
  16. Jahic E, Sofic A, Selimovic A. DWI/ADC in differentiation of benign from malignant focal liver lesion. Acta Inform Med 2016;24:244-7. doi: 10.5455/aim.2016.24.244-247
  17. Li Y, Chen Z, Wang J. Differential diagnosis between malignant and benign hepatic tumors using apparent diffusion coefficient on 1.5-T MR imaging: a meta analysis. Eur J Radiol 2012;81:484-90. doi: 10.1016/j.ejrad.2010.12.069
  18. Zhang H, Sheng F, Pan J, et al. High signal liver focal lesions on DWI: the differential diagnostic value of morphological characteristics and ADC value. Chin J Acad Radiol 2021;4:48- 55. doi: 10.1007/s42058-021-00052-8
  19. Kim JH, Ko ES, Lim Y, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology 2017;282:665-75. doi: 10.1148/radiol.201.616.0261
  20. Li Y, Li J, Meng M, Duan S, Shi H, Hang J. Development and validation of a radiomics nomogram for liver metastases originating from gastric and colorectal cancer. Diagnostics 2023;13:2937. doi: 10.3390/diagnostics13182937
  21. Waugh SA, Purdie CA, Jordan LB, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016;26:322-30. doi: 10.1007/s00330-015- 384
  22. Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating noncancerous prostate from prostate cancer and differentiating prostate cancers with different gleason scores. Eur Radiol 2015;25:2840-50. doi: 10.1007/s00330-015-3701-8
  23. Goyal A, Razik A, Kandasamy D, et al. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol 2019;44:3336- 49. doi: 10.1007/s00330-015-3701-8.
  24. Hu LS, Ning S, Eschbacher JM, et al. Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One 2015;10:e0141506. doi: 10.1371/journal.pone.0141506
  25. Davnall F, Yip CSP, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice. Insights Imaging 2012;3:573-89. doi: 10.1007/ s13244.012.0196-6
  26. Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Acad Radiol 2006;13:713-20. doi: 10.1016/j. acra.2005.07.014
  27. Li Z, Mao Y, Huang W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging 2017;17:42. doi: 10.1186/ s12880.017.0212-x