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European Radiology

, Volume 28, Issue 2, pp 468–477 | Cite as

Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study

  • Catharina S. Lisson
  • Christoph G. Lisson
  • Kerstin Flosdorf
  • Regine Mayer-Steinacker
  • Markus Schultheiss
  • Alexandra von Baer
  • Thomas F. E. Barth
  • Ambros J. Beer
  • Matthias Baumhauer
  • Reinhard Meier
  • Meinrad Beer
  • Stefan A. Schmidt
Musculoskeletal

Abstract

Objectives

To explore the diagnostic value of MRI-based 3D texture analysis to identify texture features that can be used for discrimination of low-grade chondrosarcoma from enchondroma.

Methods

Eleven patients with low-grade chondrosarcoma and 11 patients with enchondroma were retrospectively evaluated. Texture analysis was performed using mint Lesion: Kurtosis, entropy, skewness, mean of positive pixels (MPP) and uniformity of positive pixel distribution (UPP) were obtained in four MRI sequences and correlated with histopathology. The Mann-Whitney U-test and receiver operating characteristic (ROC) analysis were performed to identify most discriminative texture features. Sensitivity, specificity, accuracy and optimal cut-off values were calculated.

Results

Significant differences were found in four of 20 texture parameters with regard to the different MRI sequences (p<0.01). The area under the ROC curve values to discriminate chondrosarcoma from enchondroma were 0.876 and 0.826 for kurtosis and skewness in contrast-enhanced T1 (ceT1w), respectively; in non-contrast T1, values were 0.851 and 0.822 for entropy and UPP, respectively. The highest discriminatory power had kurtosis in ceT1w with a cut-off ≥3.15 to identify low-grade chondrosarcoma (82 % sensitivity, 91 % specificity, accuracy 86 %).

Conclusion

MRI-based 3D texture analysis might be able to discriminate low-grade chondrosarcoma from enchondroma by a variety of texture parameters.

Key Points

MRI texture analysis may assist in differentiating low-grade chondrosarcoma from enchondroma.

Kurtosis in the contrast-enhanced T1w has the highest power of discrimination.

Tools provide insight into tumour characterisation as a non-invasive imaging biomarker.

Keywords

Magnetic resonance imaging Texture analysis Tissue characterisation Chondrosarcoma Enchondroma 

Abbreviations

AUC

Area under the curve

CE

Contrast-enhanced

MPP

Mean of positive pixels

ROC

Receiver operating characteristic

STIR

Short tau inversion–recovery

TSE

Turbo spin echo

UPP

Uniformity of distribution of positive pixels

Notes

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Lisson, CS.

Conflict of interest

M. Baumhauer is CEO of Mint Medical. The company distributes the software used in our study, but there was no financial support/benefit for our department.

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

Funding

The authors state that this work has not received any funding.

Statistics and biometry

Two of the authors (CGL, SAS) have 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

• cross-sectional study

• performed at one institution

References

  1. 1.
    Murphey MD, Walker EA, Wilson AJ, Kransdorf MJ, Temple HT, Gannon FH (2003) From the archives of the AFIP: imaging of primary chondrosarcoma: radiologic-pathologic correlation. RadioGraphics 23:1245–1278CrossRefPubMedGoogle Scholar
  2. 2.
    Walden MJ, Murphey MD, Vidal JA (2008) Incidental enchondromas of the knee. AJR Am J Roentgenol 190:1611–1615CrossRefPubMedGoogle Scholar
  3. 3.
    Hogendoorn PC, Group EEW, Athanasou N et al (2010) Bone sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 21:v204–v213CrossRefPubMedGoogle Scholar
  4. 4.
    Stomp W, Reijnierse M, Kloppenburg M et al (2015) Prevalence of cartilaginous tumours as an incidental finding on MRI of the knee. Eur Radiol 25:3480–3487CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Hudson TM, Manaster BJ, Springfield DS, Spanier SS, Enneking WF, Hawkins IF Jr (1983) Radiology of medullary chondrosarcoma: preoperative treatment planning. Skelet Radiol 10:69–78CrossRefGoogle Scholar
  6. 6.
    Lodwick GS, Wilson AJ, Farrell C, Virtama P, Dittrich F (1980) Determining growth rates of focal lesions of bone from radiographs. Radiology 134:577–583CrossRefPubMedGoogle Scholar
  7. 7.
    Geirnaerdt MJ, Hogendoorn PC, Bloem JL, Taminiau AH, van der Woude HJ (2000) Cartilaginous tumors: fast contrast-enhanced MR imaging. Radiology 214:539–546CrossRefPubMedGoogle Scholar
  8. 8.
    Crim J, Schmidt R, Layfield L, Hanrahan C, Manaster BJ (2015) Can imaging criteria distinguish enchondroma from grade 1 chondrosarcoma? Eur J Radiol 84:2222–2230CrossRefPubMedGoogle Scholar
  9. 9.
    Evans HL, Ayala AG, Romsdahl MM (1977) Prognostic factors in chondrosarcoma of bone: a clinicopathologic analysis with emphasis on histologic grading. Cancer 40:818–831CrossRefPubMedGoogle Scholar
  10. 10.
    Garrison RC, Unni KK, McLeod RA, Pritchard DJ, Dahlin DC (1982) Chondrosarcoma arising in osteochondroma. Cancer 49:1890–1897CrossRefPubMedGoogle Scholar
  11. 11.
    Mirra JM, Gold R, Downs J, Eckardt JJ (1985) A new histologic approach to the differentiation of enchondroma and chondrosarcoma of the bones. A clinicopathologic analysis of 51 cases. Clin Orthop Relat Res 201:214–237Google Scholar
  12. 12.
    Sanerkin NG (1980) The diagnosis and grading of chondrosarcoma of bone: a combined cytologic and histologic approach. Cancer 45:582–594CrossRefPubMedGoogle Scholar
  13. 13.
    Stoker DJ, Cobb JP, Pringle JA (1991) Needle biopsy of musculoskeletal lesions. A review of 208 procedures. J Bone Joint Surg (Br) 73:498–500Google Scholar
  14. 14.
    Brien EW, Mirra JM, Kerr R (1997) Benign and malignant cartilage tumors of bone and joint: their anatomic and theoretical basis with an emphasis on radiology, pathology and clinical biology. I. The intramedullary cartilage tumors. Skelet Radiol 26:325–353CrossRefGoogle Scholar
  15. 15.
    Jennings R, Riley N, Rose B et al (2010) An evaluation of the diagnostic accuracy of the grade of preoperative biopsy compared to surgical excision in chondrosarcoma of the long bones. Int J Surg Oncol 2010:270195PubMedPubMedCentralGoogle Scholar
  16. 16.
    Roitman PD, Farfalli GL, Ayerza MA, Muscolo DL, Milano FE, Aponte-Tinao LA (2017) Is Needle Biopsy Clinically Useful in Preoperative Grading of Central Chondrosarcoma of the Pelvis and Long Bones? Clin Orthop Relat Res 475:808–814CrossRefPubMedGoogle Scholar
  17. 17.
    Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18:2095–2107CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Iglesias-Rozas JR, Hopf N (2005) Histological heterogeneity of human glioblastomas investigated with an unsupervised neural network (SOM). Histol Histopathol 20:351–356PubMedGoogle Scholar
  19. 19.
    Yang X, Knopp MV (2011) Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol 2011:732848PubMedPubMedCentralGoogle Scholar
  20. 20.
    Mayerhoefer ME, Schima W, Trattnig S, Pinker K, Berger-Kulemann V, Ba-Ssalamah A (2010) Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging 32:352–359CrossRefPubMedGoogle Scholar
  21. 21.
    Parsa AT, Wachhorst S, Lamborn KR et al (2005) Prognostic significance of intracranial dissemination of glioblastoma multiforme in adults. J Neurosurg 102:622–628CrossRefPubMedGoogle Scholar
  22. 22.
    Chuthapisith S, Eremin J, El-Sheemey M, Eremin O (2010) Breast cancer chemoresistance: emerging importance of cancer stem cells. Surg Oncol 19:27–32CrossRefPubMedGoogle Scholar
  23. 23.
    Sneddon JB, Werb Z (2007) Location, location, location: the cancer stem cell niche. Cell Stem Cell 1:607–611CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Materka A (2004) Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neurosci 6:243–250PubMedPubMedCentralGoogle Scholar
  26. 26.
    Eliat PA, Olivie D, Saikali S, Carsin B, Saint-Jalmes H, de Certaines JD (2012) Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma? Neurol Res Int 2012:195176CrossRefPubMedGoogle Scholar
  27. 27.
    Lopes R, Ayache A, Makni N et al (2011) Prostate cancer characterization on MR images using fractal features. Med Phys 38:83–95CrossRefPubMedGoogle Scholar
  28. 28.
    Holli K, Laaperi AL, Harrison L et al (2010) Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol 17:135–141CrossRefPubMedGoogle Scholar
  29. 29.
    Zacharaki EI, Wang S, Chawla S et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Karahaliou A, Vassiou K, Arikidis NS, Skiadopoulos S, Kanavou T, Costaridou L (2010) Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol 83:296–309CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    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
  32. 32.
    Lerski RA, Schad LR (1998) The use of reticulated foam in texture test objects for magnetic resonance imaging. Magn Reson Imaging 16:1139–1144CrossRefPubMedGoogle Scholar
  33. 33.
    Lerski RA, Schad LR, Luypaert R et al (1999) Multicentre magnetic resonance texture analysis trial using reticulated foam test objects. Magn Reson Imaging 17:1025–1031CrossRefPubMedGoogle Scholar
  34. 34.
    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
  35. 35.
    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
  36. 36.
    Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol 57:289–300Google Scholar
  38. 38.
    Chalkidou A, O'Doherty MJ, Marsden PK (2015) False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PLoS One 10:e0124165CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    McDonald JH (2014) Handbook of Biological Statistics (3rd Edition). Sparky House Publishing, Baltimore http://www.biostathandbook.com/benjaminihochberg.xls. Accessed 08.04.2017Google Scholar
  40. 40.
    Skeletal Lesions Interobserver Correlation among Expert Diagnosticians Study Group (2007) Reliability of histopathologic and radiologic grading of cartilaginous neoplasms in long bones. J Bone Joint Surg Am 89:2113–2123CrossRefGoogle Scholar
  41. 41.
    Flemming DJ, Murphey MD (2000) Enchondroma and chondrosarcoma. Semin Musculoskelet Radiol 4:59–71CrossRefPubMedGoogle Scholar
  42. 42.
    Berquist TH, Dalinka MK, Alazraki N et al (2000) Bone tumors. American College of Radiology ACR Appropriateness Criteria Radiology 215:261–264PubMedGoogle Scholar
  43. 43.
    De Beuckeleer LH, De Schepper AM, Ramon F, Somville J (1995) Magnetic resonance imaging of cartilaginous tumors: a retrospective study of 79 patients. Eur J Radiol 21:34–40CrossRefPubMedGoogle Scholar
  44. 44.
    Parlier-Cuau C, Bousson V, Ogilvie CM, Lackman RD, Laredo JD (2011) When should we biopsy a solitary central cartilaginous tumor of long bones? Literature review and management proposal. Eur J Radiol 77:6–12CrossRefPubMedGoogle Scholar
  45. 45.
    Hayes CW, Conway WF, Sundaram M (1992) Misleading aggressive MR imaging appearance of some benign musculoskeletal lesions. RadioGraphics 12:1119–1134 discussion 1135-1116CrossRefPubMedGoogle Scholar
  46. 46.
    Ma LD, Frassica FJ, Scott WW Jr, Fishman EK, Zerbouni EA (1995) Differentiation of benign and malignant musculoskeletal tumors: potential pitfalls with MR imaging. RadioGraphics 15:349–366CrossRefPubMedGoogle Scholar
  47. 47.
    Ferrer-Santacreu EM, Ortiz-Cruz EJ, Gonzalez-Lopez JM, Perez Fernandez E (2012) Enchondroma versus Low-Grade Chondrosarcoma in Appendicular Skeleton: Clinical and Radiological Criteria. J Oncol 2012:437958CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Varma DG, Ayala AG, Carrasco CH, Guo SQ, Kumar R, Edeiken J (1992) Chondrosarcoma: MR imaging with pathologic correlation. RadioGraphics 12:687–704CrossRefPubMedGoogle Scholar
  49. 49.
    Vanel D, Kreshak J, Larousserie F et al (2013) Enchondroma vs. chondrosarcoma: a simple, easy-to-use, new magnetic resonance sign. Eur J Radiol 82:2154–2160CrossRefPubMedGoogle Scholar
  50. 50.
    Geirnaerdt MJ, Hermans J, Bloem JL et al (1997) Usefulness of radiography in differentiating enchondroma from central grade 1 chondrosarcoma. AJR Am J Roentgenol 169:1097–1104CrossRefPubMedGoogle Scholar
  51. 51.
    Kendell SD, Collins MS, Adkins MC, Sundaram M, Unni KK (2004) Radiographic differentiation of enchondroma from low-grade chondrosarcoma in the fibula. Skelet Radiol 33:458–466CrossRefGoogle Scholar
  52. 52.
    De Coninck T, Jans L, Sys G et al (2013) Dynamic contrast-enhanced MR imaging for differentiation between enchondroma and chondrosarcoma. Eur Radiol 23:3140–3152CrossRefPubMedGoogle Scholar
  53. 53.
    Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345CrossRefPubMedGoogle Scholar
  54. 54.
    Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Kronenberg HM (2003) Developmental regulation of the growth plate. Nature 423:332–336CrossRefPubMedGoogle Scholar
  57. 57.
    Schipani E, Provot S (2003) PTHrP, PTH, and the PTH/PTHrP receptor in endochondral bone development. Birth Defects Res C Embryo Today 69:352–362CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Catharina S. Lisson
    • 1
  • Christoph G. Lisson
    • 1
  • Kerstin Flosdorf
    • 1
  • Regine Mayer-Steinacker
    • 2
  • Markus Schultheiss
    • 3
  • Alexandra von Baer
    • 3
  • Thomas F. E. Barth
    • 4
  • Ambros J. Beer
    • 5
  • Matthias Baumhauer
    • 6
  • Reinhard Meier
    • 1
  • Meinrad Beer
    • 1
  • Stefan A. Schmidt
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyUniversity Hospital of UlmUlmGermany
  2. 2.Department of Internal Medicine IIIUniversity Hospital of UlmUlmGermany
  3. 3.Department of Trauma SurgeryUniversity Hospital of UlmUlmGermany
  4. 4.Institute of PathologyUniversity of UlmUlmGermany
  5. 5.Department of Nuclear MedicineUniversity Hospital of UlmUlmGermany
  6. 6.Mint MedicalDossenheimGermany

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