Skip to main content
SpringerLink
Log in
Book cover

International MICCAI Brainlesion Workshop

BrainLes 2017: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 358–368Cite as

Glioblastoma and Survival Prediction

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10670))

Abstract

Glioblastoma is a stage IV highly invasive astrocytoma tumor. Its heterogeneous appearance in MRI poses a critical challenge in diagnosis, prognosis and survival prediction. This work proposes an automated survival prediction method by utilizing different types of texture and other features. The method tests feature significance and prognostic values, and then utilizes the most significant features with a Random Forest regression model to perform survival prediction. We use 163 cases from BraTS17 training dataset for evaluation of the proposed model. A 10-fold cross validation offers normalized root mean square error of 30% for the training dataset and the cross-validated accuracy of 67%, respectively. Finally, the proposed model ranked first in the Survival Prediction task for global Brain Tumor Segmentation Challenge (BraTS) 2017 and an accuracy of 57.9% is achieved.

L. Vidyaratne and M. Alam—The two authors have similar contributions.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007)

    Article  Google Scholar 

  2. Kliehues, P., Burger, P.C., Collins, V.P., Newcombe, E.W., Ohgaki, H., Cavenee, W.K.: Glioblastoma. In: WHO Classification of Tumours: Pathology and Genetics of Tumours of the Nervous System, Lyon, France, pp. 29–39. International Agency for Research on Cancer (2000)

    Google Scholar 

  3. Claes, A., Idema, A.J., Wesseling, P.: Diffuse glioma growth: a guerilla war. Acta Neuropathol. 114(5), 443–458 (2007)

    Article  Google Scholar 

  4. Stupp, R., Mason, W.P., van den Bent, M.J., Weller, M., Fisher, B., Taphoorn, M.J.B., Belanger, K., Brandes, A.A., Marosi, C., Bogdahn, U., Curschmann, J., Janzer, R.C., Ludwin, S.K., Gorlia, T., Allgeier, A., Lacombe, D., Cairncross, J.G., Eisenhauer, E., Mirimanoff, R.O.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352(10), 987–996 (2005)

    Article  Google Scholar 

  5. Soeda, A., Hara, A., Kunisada, T., Yoshimura, S., Iwama, T., Park, D.M.: The evidence of glioblastoma heterogeneity. Sci. Rep. 5, 9630 (2015)

    Article  Google Scholar 

  6. Pope, W.B., Sayre, J., Perlina, A., Villablanca, J.P., Mischel, P.S., Cloughesy, T.F.: MR imaging correlates of survival in patients with high-grade gliomas. Am. J. Neuroradiol. 26(10), 2466–2474 (2005)

    Google Scholar 

  7. Gutman, D.A., Cooper, L.A., Hwang, S.N., Holder, C.A., Gao, J., Aurora, T.D., Dunn Jr., W.D., Scarpace, L., Mikkelsen, T., Jain, R., Wintermark, M., Jilwan, M., Raghavan, P., Huang, E., Clifford, R.J., Mongkolwat, P., Kleper, V., Freymann, J., Kirby, J., Zinn, P.O., Moreno, C.S., Jaffe, C., Colen, R., Rubin, D.L., Saltz, J., Flanders, A., Brat, D.J.: MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267(2), 560–569 (2013)

    Article  Google Scholar 

  8. VASARI Research Project - Cancer Imaging Archive Wiki. https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project

  9. Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M.M., Leemans, C.R., Dekker, A., Quackenbush, J., Gillies, R.J., Lambin, P.: Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014)

    Google Scholar 

  10. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016)

    Article  Google Scholar 

  11. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jordoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2016)

    Article  Google Scholar 

  12. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, Ç., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R., Reza, S.M., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  13. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)

    Article  Google Scholar 

  14. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017). https://doi.org/10.7937/k9/tcia.2017.klxwjj1q

  15. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive (2017). https://doi.org/10.7937/k9/tcia.2017.gjq7r0ef

  16. Ahmed, S., Iftekharuddin, K.M., Vossough, A.: Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans. Inf. Technol. Biomed. 15, 206–213 (2011)

    Article  Google Scholar 

  17. Reza, S., Iftekharuddin, K.M.: Multi-fractal texture features for brain tumor and edema segmentation. In: SPIE, Medical Imaging 2014: Computer-Aided Diagnosis, San Diego, California (2014)

    Google Scholar 

  18. Iftekharuddin, K.M., Jia, W., Marsh, R.: Fractal analysis of tumor in brain images. Mach. Vis. Appl. 13, 352–362 (2003)

    Article  Google Scholar 

  19. Islam, A., Reza, S.M.S., Iftekharuddin, K.M.: Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans. Biomed. Eng. 60(11), 3204–3215 (2013)

    Article  Google Scholar 

  20. Islam, A., Iftekharuddin, K.M., Ogg, R., Laningham, F.H., Sivakumar, B.: Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors. In: SPIE Medical Imaging (2008)

    Google Scholar 

  21. Ahmed, S., Iftekharuddin, K.M., Vossough, A.: Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans. Inf Technol. Biomed. 15(2), 206–213 (2011)

    Article  Google Scholar 

  22. Ayache, A., Vehel, J.L.: On the identification of the pointwise holder exponent of the generalized multifractional Brownian motion. Stoch. Process Appl. 111, 119–156 (2004)

    Article  MATH  Google Scholar 

  23. Ayache, A., Véhel, J.L.: Generalized multifractional Brownian motion: definition and preliminary results. In: Dekking, M., Véhel, J.L., Lutton, E., Tricot, C. (eds.) Fractals: Theory and Applications in Engineering, pp. 17–32. Springer, London (1999). https://doi.org/10.1007/978-1-4471-0873-3_2

  24. Leung, T., Jitendra, M.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43, 29–44 (2001)

    Article  MATH  Google Scholar 

  25. Kleesiek, J., Biller, A., Urban, G., Köthe, U., Bendszus, M., Hamprecht, F.A.: Ilastik for multi-modal brain tumor segmentation. In: MICCAI BraTS (Brain Tumor Segmentation Challenge) (2014)

    Google Scholar 

  26. Vallières, M., Freeman, C.R., Skamene, S.R., El Naqa, I.: A radiomics model from Joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys. Med. Biol. 60(14), 5471–5496 (2015)

    Google Scholar 

  27. Caret: classification and regression training. https://CRAN.R-project.org/package=caret

Download references

Acknowledgements

This work was funded by NIBIB/NIH grant# R01 EB020683.

Author information

Authors and Affiliations

  1. Vision Lab, Electrical & Computer Engineering, Old Dominion University, Norfolk, USA

    Zeina A. Shboul, Lasitha Vidyaratne, Mahbubul Alam & Khan M. Iftekharuddin

Authors
  1. Zeina A. Shboul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lasitha Vidyaratne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahbubul Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khan M. Iftekharuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeina A. Shboul .

Editor information

Editors and Affiliations

  1. African Institute of Mathematical Sciences, Cape Town, Ghana

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. Technical University Munich, Munich, Germany

    Bjoern Menze

  5. Universität Bern, Bern, Switzerland

    Mauricio Reyes

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shboul, Z.A., Vidyaratne, L., Alam, M., Iftekharuddin, K.M. (2018). Glioblastoma and Survival Prediction. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75238-9_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75237-2

  • Online ISBN: 978-3-319-75238-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation