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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Introduction

The diagnosis and characterization of vertebral compression fractures are very important for clinical management. In this evaluation, which is usually performed with diagnostic (conventional) imaging, the findings are not always typical or diagnostic. Therefore, it is important to have new information to support imaging findings. Texture analysis is a method that can evaluate information contained in diagnostic images and is not visually noticeable. This study aimed to evaluate the magnetic resonance images of cases diagnosed with vertebral compression fractures by the texture analysis method, compare them with histopathological data, and investigate the effectiveness of this method in the differentiation of benign and malignant vertebral compression fractures.

Methods

Fifty-five patients with a total of 56 vertebral compression fractures were included in the study. Magnetic resonance images were examined and segmented using Local Image Feature Extraction (LIFEx) software, which is an open-source program for texture analysis. The results were compared with the histopathological diagnosis.

Results

The application of the Decision Tree algorithm to the dataset yielded impressively accurate predictions (≈95% in accuracy, precision, and recall).

Conclusion

Interpreting tissue analysis parameters together with conventional magnetic resonance imaging findings can improve the abilities of radiologists, lead to accurate diagnoses, and prevent unnecessary invasive procedures. Further prospective trials in larger populations are needed to verify the role and performance of texture analysis in patients with vertebral compression fractures.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-02-26
2025-01-01

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References

  1. MauchJ.T. CarrC.M. CloftH. DiehnF.E. Review of the imaging features of benign osteoporotic and malignant vertebral compression fractures.AJNR Am. J. Neuroradiol.20183991584159210.3174/ajnr.A552829348133
     [Google Scholar]
  2. JungH.S. JeeW.H. McCauleyT.R. HaK.Y. ChoiK.H. Discrimination of metastatic from acute osteoporotic compression spinal fractures with MR imaging.Radiographics200323117918710.1148/rg.23102504312533652
     [Google Scholar]
  3. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images are more than pictures, they are data.Radiology2016278256357710.1148/radiol.201515116926579733
     [Google Scholar]
  4. VargheseB.A. CenS.Y. HwangD.H. DuddalwarV.A. Texture analysis of imaging: What radiologists need to know.AJR Am. J. Roentgenol.2019212352052810.2214/AJR.18.2062430645163
     [Google Scholar]
  5. AvanzoM StancanelloJ El NaqaI. Beyond imaging: The promise of radiomics.Physica Medica201738122139
     [Google Scholar]
  6. CattellR ChenS HuangC Robustness of radiomic features in magnetic resonance imaging: Review and a phantom study.Journal Ital Assoc Biomed Phys2019211910.1186/s42492‑019‑0025‑6
     [Google Scholar]
  7. RizzoS. BottaF. RaimondiS. OriggiD. FanciulloC. MorgantiA.G. BellomiM. Radiomics: The facts and the challenges of image analysis.Eur. Radiol. Exp.2018213610.1186/s41747‑018‑0068‑z30426318
     [Google Scholar]
  8. LambinP. Rios-VelazquezE. LeijenaarR. CarvalhoS. van StiphoutR.G.P.M. GrantonP. ZegersC.M.L. GilliesR. BoellardR. DekkerA. AertsH.J.W.L. Radiomics: Extracting more information from medical images using advanced feature analysis.Eur. J. Cancer201248444144610.1016/j.ejca.2011.11.03622257792
     [Google Scholar]
  9. FisherR. PusztaiL. SwantonC. Cancer heterogeneity: Implications for targeted therapeutics.Br. J. Cancer2013108347948510.1038/bjc.2012.58123299535
     [Google Scholar]
  10. LiS. ZhouB. A review of radiomics and genomics applications in cancers: The way towards precision medicine.Radiat. Oncol.202217121710.1186/s13014‑022‑02192‑236585716
     [Google Scholar]
  11. LiZ. YuL. WangX. YuH. GaoY. RenY. WangG. ZhouX. Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors.Clin. Breast Cancer2018184e621e62710.1016/j.clbc.2017.11.00429199085
     [Google Scholar]
  12. UrsprungS. BeerL. BruiningA. WoitekR. StewartG.D. GallagherF.A. SalaE. Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis.Eur. Radiol.20203063558356610.1007/s00330‑020‑06666‑332060715
     [Google Scholar]
  13. Frighetto-PereiraL. RangayyanR.M. MetznerG.A. de Azevedo-MarquesP.M. Nogueira-BarbosaM.H. Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images.Comput. Biol. Med.20167314715610.1016/j.compbiomed.2016.04.00627111110
     [Google Scholar]
  14. GollerSS ForemanSC RischewskiJF WeissingerJ DietrichAS SchinzD Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.European Sec Cervical Spine Res Soc2023321243144320
     [Google Scholar]
  15. PoullainF. ChampsaurP. PaulyV. KnoepflinP. Le CorrollerT. CrezeM. PithiouxM. BendahanD. GuenounD. Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: A preliminary study.Eur. J. Radiol.202315811064210.1016/j.ejrad.2022.11064236527774
     [Google Scholar]
  16. SollmannN. BecherucciE.A. BoehmC. HusseiniM.E. RuschkeS. BurianE. KirschkeJ.S. LinkT.M. SubburajK. KarampinosD.C. KrugR. BaumT. DieckmeyerM. Texture analysis using CT and chemical shift encoding-based water-fat mri can improve differentiation between patients with and without osteoporotic vertebral fractures.Front. Endocrinol.20221277853710.3389/fendo.2021.77853735058878
     [Google Scholar]
  17. WangY. LiaoX. XiaoF. ZhangH. LiJ. LiaoM. Magnetic resonance imaging texture analysis in differentiating benign and malignant breast lesions of breast imaging reporting and data system 4: A preliminary study.J. Comput. Assist. Tomogr.2020441838910.1097/RCT.000000000000096931939887
     [Google Scholar]
  18. ShiB. ZhangG.M.Y. XuM. JinZ.Y. SunH. Distinguishing metastases from benign adrenal masses: What can CT texture analysis do?Acta Radiol.201960111553156110.1177/028418511983029230799636
     [Google Scholar]
  19. HawkinsS WangH LiuY GarciaA StringfieldO KrewerH Predicting malignant nodules from screening CT scans.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer201611122120212810.1016/j.jtho.2016.07.002
     [Google Scholar]
  20. YanJ. XueX. GaoC. GuoY. WuL. ZhouC. ChenF. XuM. Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.Quant. Imaging Med. Surg.202212164265210.21037/qims‑20‑138534993108
     [Google Scholar]
  21. NgF. KozarskiR. GaneshanB. GohV. 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.201382234234810.1016/j.ejrad.2012.10.02323194641
     [Google Scholar]
  22. AhnS.J. KimJ.H. ParkS.J. HanJ.K. Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis.Eur. J. Radiol.201685101867187410.1016/j.ejrad.2016.08.01427666629
     [Google Scholar]
  23. NyflotM.J. YangF. ByrdD. BowenS.R. SandisonG.A. KinahanP.E. Quantitative radiomics: Impact of stochastic effects on textural feature analysis implies the need for standards.J. Med. Imaging20152404100210.1117/1.JMI.2.4.04100226251842
     [Google Scholar]
  24. OrlhacF BoughdadS PhilippeC Stalla-BourdillonH NiocheC ChampionL A postreconstruction harmonization method for multicenter radiomic studies in PET.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer20185981321132810.2967/jnumed.117.199935
     [Google Scholar]
  25. EminagaO. AbbasM. ShenJ. LaurieM. BrooksJ.D. LiaoJ.C. RubinD.L. PlexusNet: A neural network architectural concept for medical image classification.Comput. Biol. Med.202315410659410.1016/j.compbiomed.2023.10659436753979
     [Google Scholar]
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MRI-based Texture Analysis in Differentiation of Benign and Malignant Vertebral Compression Fractures
Curr. Med. Imaging 20, e15734056290762 (2024); https://doi.org/10.2174/0115734056290762240209071656
/content/journals/cmir/10.2174/0115734056290762240209071656
/content/journals/cmir/10.2174/0115734056290762240209071656
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  • Article Type:     Research Article
Keyword(s): Cases; Magnetic resonance imaging; Patients; Texture analysis; Vertebral body; Vertebral compression fracture

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