Skip to main content

Advertisement

SpringerLink
Log in

Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI

  • Head and Neck
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI).

Methods

Fifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training (n = 42) and testing (n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student’s t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis.

Results

In the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725–0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features (p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560–0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident (p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis.

Conclusions

The BOF-based radiomics may be helpful for the differentiation between OAL and IOI.

Key Points

• It is challenging to differentiate OAL from IOI due to the similar clinical and image features.

• Radiomics has great potential for the noninvasive diagnosis of orbital diseases.

• The BOF representation from patch to image may help the differentiation of OAL and IOI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AUC:

Area under the curve

BOF:

Bag-of-features

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

IOI:

Idiopathic orbital inflammation

MRI:

Magnetic resonance imaging

OAL:

Ocular adnexal lymphoma

ROC:

Receiver operating characteristic

SVM:

Support vector machine

References

  1. McKelvie PA (2010) Ocular adnexal lymphomas: a review. Adv Anat Pathol 17:251–261

    Article  Google Scholar 

  2. Decaudin D, de Cremoux P, Vincent-Salomon A, Dendale R, Rouic L (2006) Ocular adnexal lymphoma: a review of clinicopathologic features and treatment options. Blood 108:1451–1460

  3. Kharod SM, Herman MP, Morris CG, Lightsey J, Mendenhall WM, Mendenhall NP (2018) Radiotherapy in the management of orbital lymphoma: a single institution’s experience over 4 decades. Am J Clin Oncol 41:100–106

  4. Uno T, Isobe K, Shikama N et al (2003) Radiotherapy for extranodal, marginal zone, B-cell lymphoma of mucosa-associated lymphoid tissue originating in the ocular adnexa: a multiinstitutional, retrospective review of 50 patients. Cancer 98:865–871

    Article  Google Scholar 

  5. Espinoza G (2010) Orbital inflammatory pseudotumors: etiology, differential diagnosis, and management. Curr Rheumatol Rep 12:443–447

    Article  Google Scholar 

  6. Swamy N, McCluskey P, Nemet A et al (2007) Idiopathic orbital inflammatory syndrome: clinical features and treatment outcomes. Br J Ophthalmol 91:1667–1670

    Article  CAS  Google Scholar 

  7. Rubin P, Foster C (2004) Etiology and management of idiopathic orbital inflammation. Am J Ophthalmol 138:1041–1043

    Article  Google Scholar 

  8. Brannan P (2007) A review of sclerosing idiopathic orbital inflammation. Curr Opin Ophthalmol 18:402–404

    Article  Google Scholar 

  9. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  10. Woolf DK, Ahmed M, Plowman PN (2012) Primary lymphoma of the ocular adnexa (orbital lymphoma) and primary intraocular lymphoma. Clin Oncol (R Coll Radiol) 24:339–344

    Article  CAS  Google Scholar 

  11. Haradome K, Haradome H, Usui Y et al (2014) Orbital lymphoproliferative disorders (OLPDs): value of MR imaging for differentiating orbital lymphoma from benign OPLDs. AJNR Am J Neuroradiol 35:1976–1982

  12. Cytryn AS, Putterman AM, Schneck GL, Beckman E, Valvassori GE (1997) Predictability of magnetic resonance imaging in differentiation of orbital lymphoma from orbital inflammatory syndrome. Ophthal Plast Reconstr Surg 13:129–134

  13. Xian J, Zhang Z, Wang Z et al (2010) Value of MR imaging in the differentiation of benign and malignant orbital tumors in adults. Eur Radiol 20:1692–1702

    Article  Google Scholar 

  14. Ben Simon GJ, Annunziata CC, Fink J, Villablanca P, McCann JD, Goldberg RA (2005) Rethinking orbital imaging establishing guidelines for interpreting orbital imaging studies and evaluating their predictive value in patients with orbital tumors. Ophthalmology 112:2196–2207

  15. Oses P, Renault M-A, Chauvel R et al (2009) Mapping 3-dimensional neovessel organization steps using micro-computed tomography in a murine model of hindlimb ischemia-brief report. Arterioscler Thromb Vasc Biol 29:2090–2092

    Article  CAS  Google Scholar 

  16. Kapur R, Sepahdari A, Mafee M et al (2009) MR imaging of orbital inflammatory syndrome, orbital cellulitis, and orbital lymphoid lesions: the role of diffusion-weighted imaging. AJNR Am J Neuroradiol 30:64–70

    Article  CAS  Google Scholar 

  17. Ro S-R, Asbach P, Siebert E, Bertelmann E, Hamm B, Erb-Eigne (2016) Characterization of orbital masses by multiparametric MRI. Eur J Radiol 85:324–336

  18. Hiwatashi A, Togao O, Yamashita K et al (2018) Diffusivity of intraorbital lymphoma vs. inflammation: comparison of single shot turbo spin echo and multishot echo planar imaging techniques. Eur Radiol 28:325–330

    Article  Google Scholar 

  19. Sepahdari AR, Aakalu VK, Setabutr P, Shiehmorteza M, Naheedy JH, Mafee MF (2010) Indeterminate orbital masses: restricted diffusion at MR imaging with echo-planar diffusion-weighted imaging predicts malignancy. Radiology 256:554–564

  20. Sun B, Song L, Wang X et al (2017) Lymphoma and inflammation in the orbit: diagnostic performance with diffusion-weighted imaging and dynamic contrast-enhanced MRI. J Magn Reson Imaging 45:1438–1445

    Article  Google Scholar 

  21. Roshdy N, Shahin M, Kishk H et al (2010) MRI in diagnosis of orbital masses. Curr Eye Res 35:986–991

    Article  Google Scholar 

  22. Xu X, Hu H, Liu H et al (2017) Benign and malignant orbital lymphoproliferative disorders: differentiating using multiparametric MRI at 3.0T. J Magn Reson Imaging 45:167–176

    Article  Google Scholar 

  23. Lambin P, Leijenaar R, Deist T et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  24. Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957

    Article  Google Scholar 

  25. Nie K, Chen J-H, Yu HJ, Chu Y, Nalcioglu O, Su M-Y (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 15:1513–1525

  26. Lakshmanaprabu S, Sachi-Nandan M, Shankar K, Arunkumar N, Ramirez-Gonzalez G (2019) Optimal deep learning model for classification of lung cancer on CT images. Futur Gener Comput Syst 92:374–382

  27. Juntu J, Sijbers J, De Backer S, Rajan J, Van Dyck D (2010) Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging 31:680–689

  28. Fruehwald-Pallamar J, Hesselink JR, Mafee MF, Holzer-Fruehwald L, Czerny C, Mayerhoefer ME (2016) Texture-based analysis of 100 MR examinations of head and neck tumors - is it possible to discriminate between benign and malignant masses in a multicenter trial? Rofo 188:195–202

  29. Huang Y-q, Liang C-h, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164

    Article  Google Scholar 

  30. Liu C, Ding J, Spuhler K et al (2019) Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging 49:131–140

    Article  Google Scholar 

  31. Qu J, Shen C, Qin J et al (2019) The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol 29:906–914

    Article  Google Scholar 

  32. Guo J, Liu Z, Shen C et al (2018) MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol 28:3872–3881

    Article  Google Scholar 

  33. Yu J, Qin Z, Wan T, Zhanga X (2013) Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120:355–364

  34. Dardas NH, Georganas ND (2011) Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans Instrum Meas 60:3592–3607

    Article  Google Scholar 

  35. Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. Computer Vision - ECCV, Pt 4, Proceedings 3954:490–503

  36. Luo J, Ning Z, Zhang S, Feng Q, Zhang Y(2018) Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer. Phys Med Biol 63:245014

  37. Mizener JB, Podhajsky P, Hayreh SS (1997) Ocular ischemic syndrome. Ophthalmology 104:859–864

    Article  CAS  Google Scholar 

  38. Ding ZX, Lip G, Chong V (2011) Idiopathic orbital pseudotumour. Clin Radiol 66:886–892

    Article  CAS  Google Scholar 

  39. Ren J, Yuan Y, Wu Y, Tao X (2018) Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps. BMC Med Imaging 18:6

Download references

Funding

This study has received funding from the National Natural Science Foundation of China (61971350), the China Postdoctoral Science Foundation (2019M653717), Shaanxi Key R&D Plan (2020SF-036), Xi’an Science and Technology Plan Project (GXYD18.3), and the Scientific Research Foundation of Xi’an Fourth Hospital (FZ-50).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710069, Shaanxi, China

    Yuqing Hou, Xiaoyang Xie, Jixin Chen, Xiaowei He & Fengjun Zhao

  2. Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an, 710069, Shaanxi, China

    Yuqing Hou, Xiaoyang Xie, Jixin Chen, Xiaowei He & Fengjun Zhao

  3. Department of Radiology, Xi’an Fourth Hospital, Xi’an, 710004, Shaanxi, China

    Peng Lv, Shijie Jiang & Lijuan Yang

Authors
  1. Yuqing Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jixin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shijie Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaowei He
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lijuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fengjun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lijuan Yang or Fengjun Zhao.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Fengjun Zhao.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, Y., Xie, X., Chen, J. et al. Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI. Eur Radiol 31, 24–33 (2021). https://doi.org/10.1007/s00330-020-07110-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07110-2

Keywords

Search

Navigation