Possibilities of MRI Texture Analysis of Brain Images in Differential Diagnosis of Primary Extra-Axial Tumors
Paper #8987 received 13 Jun 2023; revised manuscript received 27 Sep 2023; accepted for publication 19 Oct 2023; published online 4 Dec 2023.
DOI: 10.18287/JBPE23.09.040307
Abstract
Keywords
Full Text:
PDFReferences
1. D. N. Louis, A. Perry, P. Wesseling, D. J. Brat, I. A. Cree, D. Figarella-Branger, C. Hawkins, H. K. Ng, S.M. Pfister, G. Reifenberger, R. Soffietti, A. von Deimling, and D. W. Ellison, “The 2021 WHO Classification of Tumors of the Central Nervous System: a summary,” Neuro-Oncology 23(8), 1231–1251 (2021).
2. R. Goldbrunner, P. Stavrinou, M.D. Jenkinson, F. Sahm, C. Mawrin, D. C. Weber, M. Preusser, G. Minniti, M. Lund-Johansen, F. Lefranc, E. Houdart, K. Sallabanda, E. Le Rhun, D. Nieuwenhuizen, G. Tabatabai, R. Soffietti, and M. Weller, “EANO guideline on the diagnosis and management of meningiomas,” Neuro-Oncology 23(11), 1821–1834 (2021).
3. R. Goldbrunner, M. Weller, J. Regis, M. Lund-Johansen, P. Stavrinou, D. Reuss, D. G. Evans, F. Lefranc, K. Sallabanda, A. Falini, P. Axon, O. Sterkers, L. Fariselli, W. Wick, and J. C. Tonn, “EANO guideline on the diagnosis and treatment of vestibular schwannoma,” Neuro-Oncology 22(1), 31–45 (2020).
4. D. W. Shin, J. H. Kim, S. Chong, S. W. Song, Y. H. Kim, Y. H. Cho, S. H. Hong, and S. J. Nam, “Intracranial solitary fibrous tumor/hemangiopericytoma: tumor reclassification and assessment of treatment outcome via the 2016 WHO classification,” Journal of Neuro-Oncology 154(2), 171–178 (2021).
5. P. Lohmann, N. Galldiks, M. Kocher, A. Heinzel, C. P. Filss, C. Stegmayr, F. M. Mottaghy, G. R. Fink, N. Jon Shah, and K. J. Langen, “Radiomics in neuro-oncology: Basics, workflow, and applications,” Methods 188, 112–121 (2021).
6. H. J. Aerts, E. R. Velazquez, R. T. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J. Gillies, and P. Lambin, “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature Communications 5(1), 4006 (2014).
7. H. Gu, X. Zhang, P. di Russo, X. Zhao, and T. Xu, “The Current State of Radiomics for Meningiomas: Promises and Challenges,” Frontiers in Oncology 10, e567736 (2020).
8. D. Kalasauskas, M. Kosterhon, N. Keric, O. Korczynski, A. Kronfeld, F. Ringel, A. Othman, and M. A. Brockmann, “Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors,” Cancers 14(3), 836 (2022).
9. H. J. Aerts, “The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review,” JAMA Oncology 2(12), 1636–1642 (2016).
10. А. G. Osborn, К. L. Zalcman, and M. D. Zaveri, Radiology. Brain, Panfilova Publishing House (2018). ISBN: 978-5-91839-097-9.
11. G. Saigal, L. Pisani, E. Allakhverdieva, J. Aristizabal, D. Lehmkuhl, F. Contreras, R. Bhatia, C. Sidani, and R. Quencer, “Utility of Microhemorrhage as a Diagnostic Tool in Distinguishing Vestibular Schwannomas from other Cerebellopontine Angle (CPA) Tumors,” Indian Journal of Otolaryngology and Head & Neck Surgery 73(3), 321–326 (2021).
12. D. M. Fountain, A. M. H. Young, and T. Santarius, “Malignant meningiomas,” Handbook of Clinical Neurology 170, 245–250 (2020).
13. Y. Laviv, A. Thomas, and E. M. Kasper, “The Relevance of Angiography as a Diagnostic and Therapeutic Tool and the Role of Stereotactic Radiosurgery in Management. A Comprehensive Review,” World Neurosurgery 100, 100–117 (2017).
14. R. T. Larue, G. Defraene, D. De Ruysscher, P. Lambin, and W. van Elmpt, “Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures,” The British Journal of Radiology 90(1070), e20160665 (2017).
15. Z. Liu, S. Wang, D. Dong, J. Wei, C. Fang, X. Zhou, K. Sun, L. Li, B. Li, M. Wang, and J. Tian, “The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges,” Theranostics 9(5), 1303–1322 (2019).
16. S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon, E. J. Van Beeke, D. Yankelevitz, A. M. Biancardi, P. H. Bland, M. S. Brown, R.M. Engelmann, G. E. Laderach, D. Max, R. C. Pais, D. P. Qing, R. Y. Roberts, A. R. Smith, A. Starkey, P. Batrah, P. Caligiuri, A. Farooqi, G. W. Gladish, C. M. Jude, R. F. Munden, I. Petkovska, L. E. Quint, L. H. Schwartz, B. Sundaram, L. E. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. V. Casteele, S. Gupte, M. Sallamm, M. D. Heath, M. H. Kuhn, E. Dharaiya, R. Burns, D. S. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, and B. Y. Croft, “The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans,” Medical Physics 38(2), 915–931 (2011).
17. F. H. van Velden, G. M. Kramer, V. Frings, I. A. Nissen, E. R. Mulder, A. J. de Langen, O. S. Hoekstra, E. F. Smit, and R. Boellaard, “Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation,” Molecular Imaging and Biology 18(5), 788–795 (2016).
18. C. Parmar, E. Rios Velazquez, R. Leijenaar, M. Jermoumi, S. Carvalho, R. H. Mak, S. Mitra, B. U. Shankar, R. Kikinis, B. Haibe-Kains, P. Lambin, and H. J. Aerts, “Robust Radiomics feature quantification using semiautomatic volumetric segmentation,” PLoS One 9(7) 102107 (2014).
19. Y. Liu, J. Kim, F. Qu, S. Liu, H. Wang, Y. Balagurunathan, Z. Ye, and R. J. Gillies, “CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma,” Radiology 280(1), 271–280 (2016).
20. E. Scalco, G. Rizzo, “Texture analysis of medical images for radiotherapy applications,” The British Journal of Radiology 90(1070), 20160642 (2017).
21. N. Soni, S. Priya, and G. Bathla, “Texture Analysis in Cerebral Gliomas: A Review of the Literature,” American Journal of Neuroradiology 40(6), 928–934 (2019).
22. Y. D. Agafonova, A. V. Gaidel, P. M. Zelter, A. V. Kapishnikov, A. V. Kuznetsov, E. N. Surovtsev, and A. V. Nikonorov, “Joint analysis of radiological reports and CT images for automatic validation of pathological brain conditions,” Computer Optics 47(1), 152–159 (2023).
23. A. M. S. Al-Temimi, V. S. Pilidi, and M. K. I. Ibraheem, “Novel approach of simplification detected contours on X-ray medical images,” Computer Optics 46(3), 479–482 (2022).
24. P. Lambin, R. T. H. Leijenaar, T. M. Deist, J. Peerlings, E. E. C. de Jong, J. van Timmeren, S. Sanduleanu, R. T. H. M. Larue, A. J. G. Even, A. Jochems, Y. van Wijk, H. Woodruff, J. van Soest, T. Lustberg, E. Roelofs, W. van Elmpt, A. Dekker, F. M Mottaghy, J. E. Wildberger, and S.Walsh, “Radiomics: the bridge between medical imaging and personalized medicine,” Nature Reviews Clinical Oncology 14(12), 749–762 (2017).
25. L. He, Y. Huang, Z. Ma, C. Liang, C. Liang, and Z. Liu, “Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule,” Scientific Reports 6(1), 34921 (2016).
26. S. J. Savio, L. C. Harrison, T. Luukkaala, T. Heinonen, P. Dastidar, S. Soimakallio, and H. J. Eskola, “Effect of slice thickness on brain magnetic resonance image texture analysis,” Biomedical Engineering Online 9(1), 60 (2010).
27. F. Yang, N. Dogan, R. Stoyanova, and J. C. Ford, “Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth,” Physica Medica 50, 26–36 (2018).
28. N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, J. and C. Gee, “N4ITK: improved N3 bias correction,” IEEE Transactions on Medical Imaging 29(6), 1310–1320 (2010).
29. S. A. Waugh, C. A. Purdie, L. B. Jordan, S. Vinnicombe, R. A. Lerski, P. Martin, and A. M. Thompson, “Magnetic resonance imaging texture analysis classification of primary breast cancer,” European Radiology 26(2), 322-330 (2016).
30. “MaZda,” A computer software for calculation of texture parameters in digitized images (accessed 28 October 2022). [http://www.eletel.p.lodz.pl/programy/mazda/index.php?action=mazda_46].
31. P. M. Szczypiński, M. Strzelecki, A. Materka, and A. Klepaczko, “MaZda – a software package for image texture analysis,” Computer Methods and Programs in Biomedicine 94(1), 66–76 (2009).
32. Yu. D. Agafonova, A. V. Gaidel, P. M. Zelter, and A. V. Kapishnikov, “Efficiency of machine learning algorithms and convolutional neural network for detection of pathological changes in MR images of the brain,” Computer Optics 44(2), 266–273 (2020).
33. N. Ilyasova, R. Paringer, and A. Shirokanev, “A smart feature selection technique for object localization in ocular fundus images with the aid of color subspaces,” Procedia Engineering 201, 736–745 (2017).
34. Yu. HeiShun, B. Tischler, M. M. Qureshi, J. A. Soto, S. Anderson, N. Daginawala, B. Li, and K. Buch, “Using texture analyses of contrast enhanced CT to assess hepatic fibrosis,” European Journal of Radiology 85(3), 511–517 (2016).
35. N. Ilyasova, A. Kupriyanov, and R. Paringer, “Particular Use of BIG DATA in Medical Diagnostic Tasks,” Pattern Recognition and Image Analysis 28(1), 114–121 (2018).
36. U. R. Acharya, E. Y. Ng, J. H. Tan, S. V. Sree, and K. H. Ng, “An integrated index for the identification of diabetic retinopathy stages using texture parameters,” Journal of Medical Systems 36(3), 2011– 2012 (2020).
37. M. Hajek, M. Dezortova, A. Materka, and R. Lerski (Eds.), Texture Analysis for Magnetic Resonance Imaging, Med4publishing, Prague, Czech Republic (2006). ISBN: 80-903660-0-7.
38. N. Daginawala, B. Li, K. Buch, H. S. Yu, B. Tischler, M. M. Qureshi, J. A. Soto, and S. Anderson, “Using texture analyses of con-trast enhanced CT to assess hepatic fibrosis,” European Journal of Radiology 85(3), 511–517 (2016).
39. H. Gentillon, L. Stefańczyk, M. Strzelecki, and M. Respondek-Liberska, “Parameter set for computer-assisted texture analysis of fetal brain,” BMC Research Notes 9(1), 496 (2016).
40. S. B. Ginsburg, D. A. Lynch, R. P. Bowler, and J. D. Schroeder, “Automated Texture-based Quantification of Centrilobular Nodularity and Centrilobular Emphysema in Chest CT Images,” Academic Radiology 19(10), 1241–1251 (2012).
41. V. Nikitaev, B. Flury, “Sposob raspoznavaniya izobrazheniya tekstury kletok,” Biometrika 97(1), 33–41 (2010) [in Russian]
42. Z. Kakushadze, W. Yuc, “*K-means and cluster models for cancer signatures,” Biomolecular Detection and Quantification 31, 7–31 (2017).
43. N. Yu. Ilyasova, A. S. Shirokanev, A. V. Kupriyanov, and R. A. Paringer, “Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on retina,” Computer Optics 43(2), 304–315 (2019).
44. A. V. Gaidel, P. M. Zelter, A. V. Kapishnikov, and A. G. Khramov, “Computed tomography texture analysis possibilities of the chronic obstructive pulmonary disease diagnosis,” Computer Optics 38(4), 843–849 (2014).
45. A. V. Gaidel, S. S. Pervushkin, “Research of the textural features for the bony tissue diseases diagnostics using the roentgenograms,” Computer Optics 37(1), 113–119 (2013).
46. S. S. Sadykov, Yu. A. Bulanova, and E. A. Zakharova “Computer diagnosis of tumors in mammograms,” Computer Optics 38(1), 131–138 (2014).
47. N. I. Glumov, A. V. Kapishnikov, “Computer processing lung’s scintigraphic images,” Computer Optics 25, 158–164 (2003). [in Russian]
48. M. Julià-Sapé, D. Acosta, C. Majós, A. Moreno-Torres, P. Wesseling, J. J. Acebes, J. R. Griffiths, and C. Arús, “Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database,” Journal of Neurosurgery 105(1), 6–14 (2006).
49. S. Kabashi, M. Ugurel, K. Dedushi, and S. Mucaj, “The role of Magnetic Resonance Imaging (MRI) in diagnostics of Acoustic Schwannoma,” Acta Informatica Medica 28(4), 287 (2020).
50. P.-F. Yan, L. Yan, Z. Zhang, A. Salim, L. Wang, T.-T. Hu, and H.-Y. Zhao, “Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases,” International Journal of Surgery 36, 109–117 (2016).
51. H. Gu, X. Zhang, P. di Russo, X. Zhao, and T. Xu, “The Current State of Radiomics for Meningiomas: Promises and Challenges,” Frontiers in Oncology 10, e567736 (2020).
52. H. Nagano, K. Sakai, J. Tazoe, M. Yasuike, K. Akazawa, and K. Yamada, “Whole-tumor histogram analysis of DWI and QSI for differentiating between meningioma and schwannoma: a pilot study,” Japanese Journal of Radiology 37(10), 694–700 (2019).
Сontact
34 Moskovskoe shosse, Samara, 443086, Russian Federation
Email: j-bpe@ssau.ru
Phone: +7-846-267-4550
© 2014-2025 J-BPE














