Skip to main content

Advertisement

SpringerLink
Log in

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 14 March 2018

This article has been updated

Abstract

Objectives

To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery.

Methods

We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions.

Results

A combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation.

Conclusions

Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases.

Key points

• Some radiomic features showed better reproducibility for progressive lesions than necrotic ones

• Delta radiomic features can help to distinguish radiation necrosis from tumour progression

• Delta radiomic features had better predictive value than did traditional radiomic features

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

Similar content being viewed by others

Change history

  • 14 March 2018

    The original version of this article, published on 24 November 2017, unfortunately contained a mistake.

Abbreviations

AUC:

Area under the curve

CCC:

Concordance correlation coefficient

COM:

Co-occurrence matrix

FLAIR:

Fluid-attenuated inversion recovery

HOG:

Histogram of oriented gradients

IBEX:

Imaging Biomarker Explorer

MRI:

Magnetic resonance imaging

NGTDM:

Neighbourhood grey-tone difference matrix

RLM:

Run length matrix

ROC:

Receiver operating characteristic

T1c:

T1 weighted post-contrast

References

  1. Nayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Current Oncology Reports 14:48–54

    Article  PubMed  Google Scholar 

  2. Kondziolka D, Martin JJ, Flickinger JC et al (2005) Long-term survivors after gamma knife radiosurgery for brain metastases. Cancer 104:2784–2791

    Article  PubMed  Google Scholar 

  3. Elaimy AL, Mackay AR, Lamoreaux WT et al (2011) Clinical outcomes of stereotactic radiosurgery in the treatment of patients with metastatic brain tumors. World Neurosurg 75:673–683

    Article  PubMed  Google Scholar 

  4. Minniti G, Clarke E, Lanzetta G et al (2011) Stereotactic radiosurgery for brain metastases: analysis of outcome and risk of brain radionecrosis. Radiation Oncology 6:48

    Article  PubMed  PubMed Central  Google Scholar 

  5. Shaw E, Scott C, Souhami L et al (2000) Single dose radiosurgical treatment of recurrent previously irradiated primary brain tumors and brain metastases: Final report of RTOG protocol 90-05. International Journal of Radiation Oncology Biology Physics 47:291–298

    Article  CAS  Google Scholar 

  6. Chuang MT, Liu YS, Tsai YS, Chen YC, Wang CK (2016) Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis. PLoS ONE 11

  7. Lai G, Mahadevan A, Hackney D et al (2015) Diagnostic Accuracy of PET, SPECT, and Arterial Spin-Labeling in Differentiating Tumor Recurrence from Necrosis in Cerebral Metastasis after Stereotactic Radiosurgery. American Journal of Neuroradiology 36:2250–2255

    Article  CAS  PubMed  Google Scholar 

  8. Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5

  9. Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proceedings of the National Academy of Sciences 112:E6265–E6273

    Article  CAS  Google Scholar 

  10. Gevaert O, Mitchell LA, Achrol AS et al (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273:168–174

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  PubMed Central  Google Scholar 

  12. Itakura H, Achrol AS, Mitchell LA et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science translational medicine 7:303ra138–303ra138

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yamamoto S, Han W, Kim Y et al (2015) Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 275:384–392

    Article  PubMed  Google Scholar 

  14. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Transactions On Medical Imaging 22:120–128

    Article  PubMed  Google Scholar 

  15. Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE (2015) IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics. Medical Physics 42:1341–1353

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yang J, Zhang L, Fave XJ et al (2016) Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 48:1–8

    Article  CAS  PubMed  Google Scholar 

  17. Burger W, Burge MJ (2013) Edge-Preserving Smoothing FiltersPrinciples of Digital Image Processing. Springer, pp 119-167

  18. Cunliffe AR, Armato Iii SG, Fei XM, Tuohy RE, Al-Hallaq HA (2013) Lung texture in serial thoracic CT scans: Registration-based methods to compare anatomically matched regionsa. Medical Physics 40:061906

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fried DV, Tucker SL, Zhou S et al (2014) Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 90:834–842

    Article  PubMed  PubMed Central  Google Scholar 

  20. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE T. Syst. Man Cyb. 3:610–621

    Article  Google Scholar 

  21. Tang X (1998) Texture information in run-length matrices. IEEE Transactions on Image Processing 7:1602–1609

    Article  CAS  PubMed  Google Scholar 

  22. Legland D, Kiêu K, Devaux M-F (2007) Computation of Minkowski measures on 2D and 3D binary images. Image Anal Stereol 26:83–92

    Article  Google Scholar 

  23. Basu S (2012) Developing predictive models for lung tumor analysis.

  24. Amadasun M, King R (1989) Textural Features Corresponding to Textural Properties. Ieee Transactions on Systems Man and Cybernetics 19:1264–1274

    Article  Google Scholar 

  25. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) pp 886-893

  26. Tiwari P, Prasanna P, Wolansky L et al (2016) Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. AJNR Am J Neuroradiol. https://doi.org/10.3174/ajnr.A4931

  27. Galizia MS, Töre HG, Chalian H, McCarthy R, Salem R, Yaghmai V (2012) MDCT necrosis quantification in the assessment of hepatocellular carcinoma response to yttrium 90 radioembolization therapy: comparison of two-dimensional and volumetric techniques. Academic Radiology 19:48–54

    Article  PubMed  Google Scholar 

  28. Lin LIK (1989) A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 45:255–268

    Article  CAS  PubMed  Google Scholar 

  29. Duda RO, Hart PE, Stork DG (2001) Pattern Classification. John Wiley and Sons, Inc.

  30. Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans 40:185-197

  31. Barajas RF, Chang JS, Segal MR et al (2009) Differentiation of Recurrent Glioblastoma Multiforme from Radiation Necrosis after External Beam Radiation Therapy with Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging. Radiology 253:486–496

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kickingereder P, Burth S, Wick A et al (2016) Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology 280:880–889

    Article  PubMed  Google Scholar 

  33. Li H, Zhu Y, Burnside ES et al (2016) MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology:152110

  34. Larroza A, Moratal D, Paredes-Sánchez A et al (2015) Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging 42:1362–1368

    Article  PubMed  Google Scholar 

  35. Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. NeuroImage: Clinical 6:9–19

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Christine Wogan for reviewing this manuscript.

This work was presented in part at the 2016 American Association of Physicists in Medicine Annual Meeting.

Author information

Authors and Affiliations

  1. Central South University Xiangya Hospital, Changsha, Hunan, China

    Zijian Zhang

  2. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA

    Zijian Zhang, Jinzhong Yang, Angela Ho, Xin Wang, Xenia Fave, Lifei Zhang, Dennis Mackin & Laurence E. Court

  3. University of Houston, Houston, TX, USA

    Angela Ho

  4. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA

    Wen Jiang, Jennifer Logan, Paul D. Brown, Susan L. McGovern & Jing Li

  5. Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA

    Nandita Guha-Thakurta

  6. Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA

    Sherise D. Ferguson

Authors
  1. Zijian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinzhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Angela Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jennifer Logan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paul D. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Susan L. McGovern
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nandita Guha-Thakurta
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sherise D. Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xenia Fave
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lifei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dennis Mackin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Laurence E. Court
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinzhong Yang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jing Li.

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.

Funding

This study has received funding by National Institutes of Health through Cancer Center Support Grant P30CA016672.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• experimental

• performed at one institution

Additional information

The original version of this article was revised: The presentation of Table 2 was incorrect.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Yang, J., Ho, A. et al. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28, 2255–2263 (2018). https://doi.org/10.1007/s00330-017-5154-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-017-5154-8

Keywords

Search

Navigation