Advertisement

European Radiology

, Volume 28, Issue 9, pp 3872–3881 | Cite as

MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation

  • Jian Guo
  • Zhenyu Liu
  • Chen Shen
  • Zheng Li
  • Fei Yan
  • Jie Tian
  • Junfang Xian
Head and Neck
  • 224 Downloads

Abstract

Objectives

To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).

Methods

One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test.

Results

Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05).

Conclusions

Radiomics features have the potential to differentiate OAL from IOI.

Key Points

• Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult.

• Radiomics features can potentially differentiate OAL from IOI non invasively.

• The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.

Keywords

Radiomics Inflammation Lymphoma Orbital neoplasms Magnetic resonance imaging (MRI) 

Abbreviations

ADC

Apparent diffusion coefficient

AUC

Area under the ROC curve

DCE

Dynamic contrast enhanced

DWI

Diffusion-weighted imaging

ETL

Echo train length

FS

Fat saturation

FSE

Fast spin echo

GLCM

Grey level co-occurrence matrix

GLRLM

Grey level run length matrix

ICC

Intraclass correlation coefficient

IOI

Idiopathic orbital inflammation

LASSO

Least absolute shrinkage and selection operators procedure

MRI

Magnetic resonance imaging

NEX

Number of excitations

OAL

Ocular adnexal lymphoma

ROC

Receiver-operating characteristic

SRHGE

Short-run high-grey emphasis

T1WI

T1-weighted images

T2WI

T2-weighted image

TE

Echo time

TR

Repetition time

Notes

Acknowledgements

The authors would like to express their sincere appreciation to all reviewers for their kind comments.

This work was presented in part at the 2017 International Society of Magnetic Resonance Imaging in Medicine Annual Meeting.

Funding

This study has received funding from the High Level Health Technical Personnel of Bureau of Health in Beijing under grant no. 2014-2-005; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support under grant no. ZYLX201704; Key Talent Project of Beijing under Grant no. 2014001; The Priming Scientific Research Foundation for the Senior Researcher in Beijing Tongren Hospital, Capital Medical University, under grant no. 2016-YJJ-GGL-011.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Junfang Xian.

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 obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

References

  1. 1.
    Rosado MF, Byrne GE, Ding F et al (2006) Ocular adnexal lymphoma: a clinicopathologic study of a large cohort of patients with no evidence for an association with Chlamydia psittaci. Blood 107:467–472CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Sjö LD (2009) Ophthalmic lymphoma: epidemiology and pathogenesis. Acta Ophthalmologica 87:1–20CrossRefPubMedGoogle Scholar
  3. 3.
    Shields JA, Shields CL, Scartozzi R (2004) Survey of 1264 patients with orbital tumors and simulating lesions: The 2002 Montgomery Lecture, part 1. Ophthalmology 111:997–1008CrossRefPubMedGoogle Scholar
  4. 4.
    Ferreri AJ, Dolcetti R, Du MQ et al (2008) Ocular adnexal MALT lymphoma: an intriguing model for antigen-driven lymphomagenesis and microbial-targeted therapy. Ann Oncol 19:835–846CrossRefPubMedGoogle Scholar
  5. 5.
    Woolf DK, Ahmed M, Plowman PN (2012) Primary lymphoma of the ocular adnexa (orbital lymphoma) and primary intraocular lymphoma. Clin Oncol (R Coll Radiol) 24:339–344CrossRefGoogle Scholar
  6. 6.
    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 41:100–106PubMedGoogle Scholar
  7. 7.
    Shikishima K, Kawai K, Kitahara K (2006) Pathological evaluation of orbital tumours in Japan: analysis of a large case series and 1379 cases reported in the Japanese literature. Clin Exp Ophthalmol 34:239–244CrossRefPubMedGoogle Scholar
  8. 8.
    Rubin PA, Foster CS (2004) Etiology and management of idiopathic orbital inflammation. Am J Ophthalmol 138:1041–1043CrossRefPubMedGoogle Scholar
  9. 9.
    Swamy BN, McCluskey P, Nemet A et al (2007) Idiopathic orbital inflammatory syndrome: clinical features and treatment outcomes. Br J Ophthalmol 91:1667–1670CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Dagi Glass LR, Freitag SK (2016) Orbital inflammation: Corticosteroids first. Survey of Ophthalmology 61:670–673CrossRefPubMedGoogle Scholar
  11. 11.
    Mombaerts I, Rose GE, Garrity JA (2016) Orbital Inflammation: Biopsy first. Survey of Ophthalmology 61:664-669Google Scholar
  12. 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 13:129–134CrossRefPubMedGoogle Scholar
  13. 13.
    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 35:1976–1982CrossRefPubMedGoogle Scholar
  14. 14.
    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 20:1692–1702CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Warner MA, Weber AL, Jakobiec FA (1996) Benign and malignant tumors of the orbital cavity including the lacrimal gland. Neuroimaging Clin N Am 6:123–142PubMedGoogle Scholar
  16. 16.
    Roshdy N, Shahin M, Kishk H et al (2010) MRI in diagnosis of orbital masses. Curr Eye Res 35:986–991CrossRefPubMedGoogle Scholar
  17. 17.
    Sullivan TJ, Valenzuela AA (2006) Imaging features of ocular adnexal lymphoproliferative disease. Eye 20:1189–1195CrossRefPubMedGoogle Scholar
  18. 18.
    Uehara F, Ohba N (2002) Diagnostic imaging in patients with orbital cellulitis and inflammatory pseudotumor. Int Ophthalmol Clin 42:133–142CrossRefPubMedGoogle Scholar
  19. 19.
    Politi LS, Forghani R, Godi C et al (2010) Ocular adnexal lymphoma: diffusion-weighted MR imaging for differential diagnosis and therapeutic monitoring. Radiology 256:565–574CrossRefPubMedGoogle Scholar
  20. 20.
    Fatima Z, Ichikawa T, Ishigame K et al (2014) Orbital masses: the usefulness of diffusion-weighted imaging in lesion categorization. Clin Neuroradiol 24:129–134CrossRefPubMedGoogle Scholar
  21. 21.
    Kapur R, Sepahdari AR, Mafee MF 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 30:64–70CrossRefPubMedGoogle Scholar
  22. 22.
    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 256:554–564CrossRefPubMedGoogle Scholar
  23. 23.
    Purohit BS, Vargas MI, Ailianou A et al (2016) Orbital tumours and tumour-like lesions: exploring the armamentarium of multiparametric imaging. Insights Imaging 7:43–68CrossRefPubMedGoogle Scholar
  24. 24.
    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 45:1438–1445CrossRefPubMedGoogle Scholar
  25. 25.
    Xu XQ, Hu H, Liu H et al (2017) Benign and malignant orbital lymphoproliferative disorders: differentiating using multiparametric MRI at 3.0T. J Magn Reson Imaging 45:167–176CrossRefPubMedGoogle Scholar
  26. 26.
    Lambin P, Riosvelazquez E, Leijenaar RTH et al (2012) Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    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 281:947–957CrossRefPubMedGoogle Scholar
  29. 29.
    Huang YQ, Liang CH, 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 34:2157–2164CrossRefPubMedGoogle Scholar
  30. 30.
    Parmar C, Leijenaar RT, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 5:11044CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Wang J, Kato F, Oyama-Manabe N et al (2015) Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: A pilot radiomics study. PLOS ONE 10.  https://doi.org/10.1371/journal.pone.0143308
  32. 32.
    Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264CrossRefPubMedGoogle Scholar
  33. 33.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Bayanati H, Thornhill RE, Souza CA et al (2014) Quantitative CT texture and shape analysis: Can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? Eur Radiol 25:480–487CrossRefPubMedGoogle Scholar
  35. 35.
    Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY (2008) Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 15:1513–1525CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    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 31:680–689CrossRefPubMedGoogle Scholar
  37. 37.
    Thornhill RE, Golfam M, Sheikh A et al (2014) Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis. Acad Radiol 21:1185–1194CrossRefPubMedGoogle Scholar
  38. 38.
    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 188:195–202PubMedGoogle Scholar
  39. 39.
    Ding ZX, Lip G, Chong V (2011) Idiopathic orbital pseudotumour. Clin Radiol 66:886–892CrossRefPubMedGoogle Scholar
  40. 40.
    Li Z, Mao Y, Li H, Yu G, Wan H, Li B (2015) Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR. Magne Reson Med 76:1410–1419CrossRefGoogle Scholar
  41. 41.
    Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269CrossRefPubMedGoogle Scholar
  42. 42.
    Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Ganeshan B, Miles KA, Young RC, Chatwin CR (2007) In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol 14:1058–1068CrossRefPubMedGoogle Scholar
  44. 44.
    Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157–164CrossRefPubMedGoogle Scholar
  45. 45.
    Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802CrossRefPubMedGoogle Scholar
  46. 46.
    Skogen K, Ganeshan B, Good C, Critchley G, Miles K (2013) Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade. J Neurooncol 111:213–219CrossRefPubMedGoogle Scholar
  47. 47.
    Frighetto-Pereira L, Rangayyan RM, Metzner GA (2016) Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Computers in Biology & Medicine 73:147–156CrossRefGoogle Scholar
  48. 48.
    Woods BJ, Clymer BD, Kurc T et al (2007) Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. J Magn Reson Imaging 25:495–501CrossRefPubMedGoogle Scholar
  49. 49.
    Sasaguri K, Takahashi N, Takeuchi M, Carter RE, Leibovich BC, Kawashima A (2016) Differentiation of benign from metastatic adrenal masses in patients with renal cell carcinoma on contrast-enhanced CT. AJR Am J Roentgenol 207:1031–1038CrossRefPubMedGoogle Scholar
  50. 50.
    Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L et al (2013) Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla. NMR Biomed 26:1372–1379CrossRefPubMedGoogle Scholar
  51. 51.
    Mueller-Using S, Feldt T, Sarfo FS, Eberhardt KA (2016) Factors associated with performing tuberculosis screening of HIV-positive patients in Ghana: LASSO-based predictor selection in a large public health data set. BMC Public Health 16:563CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98CrossRefPubMedGoogle Scholar
  53. 53.
    Xu R, Kido S, Suga K et al (2014) Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med 28:926–935CrossRefPubMedGoogle Scholar
  54. 54.
    Way TW, Hadjiiski LM, Sahiner B et al (2006) Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Medical Physics 33:2323–2337CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Sepahdari AR, Politi LS, Aakalu VK, Kim HJ, Razek AA (2014) Diffusion-weighted imaging of orbital masses: multi-institutional data support a 2-ADC threshold model to categorize lesions as benign, malignant, or indeterminate. AJNR Am J Neuroradiol 35:170–175CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Beijing Tongren HospitalCapital Medical UniversityBeijingChina
  2. 2.CAS Key Laboratory of Molecular ImagingInstitute of AutomationBeijingChina
  3. 3.School of Life Science and TechnologyXidian UniversityXi’anChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations