Skip to main content

Advertisement

SpringerLink
Log in

Predicting survival in patients with glioblastoma using MRI radiomic features extracted from radiation planning volumes

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Background

Quantitative image analysis using pre-operative magnetic resonance imaging (MRI) has been able to predict survival in patients with glioblastoma (GBM). The study explored the role of postoperative radiation (RT) planning MRI-based radiomics to predict the outcomes, with features extracted from the gross tumor volume (GTV) and clinical target volume (CTV).

Methods

Patients with IDH-wildtype GBM treated with adjuvant RT having MRI as a part of RT planning process were included in the study. 546 features were extracted from each GTV and CTV. A LASSO Cox model was applied, and internal validation was performed using leave-one-out cross-validation with overall survival as endpoint. Cross-validated time-dependent area under curve (AUC) was constructed to test the efficacy of the radiomics model, and clinical features were used to generate a combined model. Analysis was done for the entire group and in individual surgical groups-gross total excision (GTR), subtotal resection (STR), and biopsy.

Results

235 patients were included in the study with 57, 118, and 60 in the GTR, STR, and biopsy subgroup, respectively. Using the radiomics model, binary risk groups were feasible in the entire cohort (p < 0.01) and biopsy group (p = 0.04), but not in the other two surgical groups individually. The integrated AUC (iAUC) was 0.613 for radiomics-based classification in the biopsy subgroup, which improved to 0.632 with the inclusion of clinical features.

Conclusion

Imaging features extracted from the GTV and CTV regions can lead to risk-stratification of GBM undergoing biopsy, while the utility in other individual subgroups needs to be further explored.

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

Availability of data and material

Data are stored in an institutional repository and will be made available on request to the corresponding author following institutional ethics committee protocols.

Code availability

The radiomic feature extraction was performed using freely available Pyradiomics software (http://www.pyradiomics.io/pyradiomics.html).

References

  1. Lambin P, Rios-Velazquez E, Leijenaar R et al (1990) (2012) Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer Oxf Engl 48:441–446. https://doi.org/10.1016/j.ejca.2011.11.036

    Article  Google Scholar 

  2. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  Google Scholar 

  3. Villanueva-Meyer JE, Mabray MC, Cha S (2017) Current Clinical Brain Tumor Imaging. Neurosurgery 81:397–415. https://doi.org/10.1093/neuros/nyx103

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chaddad A, Kucharczyk MJ, Daniel P et al (2019) Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol 9:374. https://doi.org/10.3389/fonc.2019.00374

    Article  PubMed  PubMed Central  Google Scholar 

  5. Singh G, Manjila S, Sakla N et al (2021) Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 125:641–657. https://doi.org/10.1038/s41416-021-01387-w

    Article  PubMed  PubMed Central  Google Scholar 

  6. Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996. https://doi.org/10.1056/NEJMoa043330

    Article  CAS  PubMed  Google Scholar 

  7. Tseng C-L, Stewart J, Whitfield G et al (2020) Glioma consensus contouring recommendations from a MR-Linac International Consortium Research Group and evaluation of a CT-MRI and MRI-only workflow. J Neurooncol 149:305–314. https://doi.org/10.1007/s11060-020-03605-6

    Article  PubMed  PubMed Central  Google Scholar 

  8. Perry JR, Laperriere N, O’Callaghan CJ et al (2017) Short-course radiation plus temozolomide in elderly patients with glioblastoma. N Engl J Med 376:1027–1037. https://doi.org/10.1056/NEJMoa1611977

    Article  CAS  PubMed  Google Scholar 

  9. Isensee F, Schell M, Pflueger I et al (2019) Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 40:4952–4964. https://doi.org/10.1002/hbm.24750

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156. https://doi.org/10.1016/S1361-8415(01)00036-6

    Article  CAS  PubMed  Google Scholar 

  11. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841. https://doi.org/10.1006/nimg.2002.1132

    Article  PubMed  Google Scholar 

  12. Greve DN, Fischl B (2009) Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48:63–72. https://doi.org/10.1016/j.neuroimage.2009.06.060

    Article  PubMed  Google Scholar 

  13. Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908

    Article  PubMed  PubMed Central  Google Scholar 

  14. Beare R, Lowekamp B, Yaniv Z (2018) Image segmentation, registration and characterization in R with SimpleITK. J Stat Softw. https://doi.org/10.18637/jss.v086.i08

    Article  PubMed  PubMed Central  Google Scholar 

  15. Nyúl LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42:1072–1081. https://doi.org/10.1002/(sici)1522-2594(199912)42:6%3c1072::aid-mrm11%3e3.0.co;2-m

    Article  PubMed  Google Scholar 

  16. Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19:143–150. https://doi.org/10.1109/42.836373

    Article  CAS  PubMed  Google Scholar 

  17. Knight J, Taylor GW, Khademi A (2017) Equivalence of histogram equalization, histogram matching and the Nyul algorithm for intensity standardization in MRI. J Comput Vis Imaging Syst. https://doi.org/10.15353/vsnl.v3i1.170

    Article  Google Scholar 

  18. Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128. https://doi.org/10.1016/j.neuroimage.2006.01.015

    Article  PubMed  Google Scholar 

  19. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Team RC (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

  21. Max Kuhn (2020) caret: classification and regression training

  22. Friedman JH, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw. https://doi.org/10.18637/jss.v033.i01

    Article  PubMed  PubMed Central  Google Scholar 

  23. Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1–13. https://doi.org/10.18637/jss.v039.i05

    Article  PubMed  PubMed Central  Google Scholar 

  24. Therneau TM (2020) A package for survival analysis in R

  25. Therneau TM, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, New York

    Book  Google Scholar 

  26. Simon RM, Subramanian J, Li M-C, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12:203–214. https://doi.org/10.1093/bib/bbr001

    Article  PubMed  PubMed Central  Google Scholar 

  27. Heagerty PJ, Saha-Chaudhuri P (2012) risksetROC: riskset ROC curve estimation from censored survival data

  28. Kuo MD, Jamshidi N (2014) Behind the numbers: Decoding molecular phenotypes with radiogenomics–guiding principles and technical considerations. Radiology 270:320–325. https://doi.org/10.1148/radiol.13132195

    Article  PubMed  Google Scholar 

  29. Bagher-Ebadian H, Siddiqui F, Liu C et al (2017) On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys 44:1755–1770. https://doi.org/10.1002/mp.12188

    Article  PubMed  Google Scholar 

  30. Qin Q, Shi A, Zhang R et al (2020) Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 11:964–972. https://doi.org/10.1111/1759-7714.13349

    Article  PubMed  PubMed Central  Google Scholar 

  31. Prasanna P, Patel J, Partovi S et al (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol 27:4188–4197. https://doi.org/10.1007/s00330-016-4637-3

    Article  PubMed  Google Scholar 

  32. Dasgupta A, Geraghty B, Maralani PJ et al (2021) Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema. J Neurooncol 153:251–261. https://doi.org/10.1007/s11060-021-03762-2

    Article  PubMed  Google Scholar 

  33. Malik N, Geraghty B, Dasgupta A et al (2021) MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region. J Neurooncol 155:181–191. https://doi.org/10.1007/s11060-021-03866-9

    Article  PubMed  Google Scholar 

  34. Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806. https://doi.org/10.1148/radiol.2018180200

    Article  PubMed  Google Scholar 

  35. Azoulay M, Chang SD, Gibbs IC et al (2020) A phase I/II trial of 5-fraction stereotactic radiosurgery with 5-mm margins with concurrent temozolomide in newly diagnosed glioblastoma: primary outcomes. Neuro-Oncol 22:1182–1189. https://doi.org/10.1093/neuonc/noaa019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We express our sincere gratitude to the patients and their caregivers involved in the study. We would like to thank the Terry Fox Foundation Program Project Grant from the Hecht Foundation for the funding support associated with the study

Funding

Terry Fox Foundation Program Project Grant from the Hecht Foundation (1083) awarded to Gregory J. Czarnota. The funding bodies had no influence on the study design, data collection, analysis, interpretation of data, or the manuscript's writing.

Author information

Author notes

  1. Benjamin J. Geraghty and Archya Dasgupta have contributed equally to the study (co-first authors).

  2. Arjun Sahgal and Gregory J. Czarnota have contributed equally to the study.

Authors and Affiliations

  1. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada

    Benjamin J. Geraghty, Archya Dasgupta, Michael Sandhu, Nauman Malik, Jay Detsky, Chia-Lin Tseng, Hany Soliman, Sten Myrehaug, Zain Husain, Arjun Sahgal & Gregory J. Czarnota

  2. Department of Radiation Oncology, University of Toronto, Toronto, Canada

    Benjamin J. Geraghty, Archya Dasgupta, Michael Sandhu, Nauman Malik, Jay Detsky, Chia-Lin Tseng, Hany Soliman, Sten Myrehaug, Zain Husain, Arjun Sahgal & Gregory J. Czarnota

  3. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada

    Benjamin J. Geraghty, Archya Dasgupta, Michael Sandhu, Jay Detsky, Chia-Lin Tseng, Hany Soliman, Sten Myrehaug, Zain Husain, Angus Lau, Arjun Sahgal & Gregory J. Czarnota

  4. Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada

    Pejman Jabehdar Maralani

  5. Department of Medical Imaging, University of Toronto, Toronto, Canada

    Pejman Jabehdar Maralani

  6. Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, Canada

    James Perry

  7. Department of Medicine, University of Toronto, Toronto, Canada

    James Perry

  8. Department of Medical Biophysics, University of Toronto, Toronto, Canada

    Angus Lau & Gregory J. Czarnota

Authors
  1. Benjamin J. Geraghty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Archya Dasgupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Sandhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nauman Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pejman Jabehdar Maralani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jay Detsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chia-Lin Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hany Soliman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sten Myrehaug
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zain Husain
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. James Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Angus Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Arjun Sahgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Gregory J. Czarnota
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: GJC, BG, AD, AS; Methodology: All authors; Formal Analysis and investigation: All authors; Writing-original draft preparation: BG, AD, AS, GJC; Writing-review and editing: All authors; Project administration and supervision: AS, GJC; Funding acquisition: GJC. All the authors are in agreement and accountable for all the aspects of the work.

Corresponding author

Correspondence to Gregory J. Czarnota.

Ethics declarations

Conflict of interest

Benjamin Geraghty, Archya Dasgupta, Michael Sandhu, Nauman Malik, Pejman Jabehdar Maralani, Jay Detsky, Chia-Lin Tseng, Hany Soliman, Sten Myrehaug, Zain Husain, James Perry, Angus Lau: None. Arjun Sahgal: Research grant with Elekta AB, Varian. Past educational seminars with Elekta (Gamma Knife Icon) and Elekta AB, Accuray Inc., Varian (Medical Advisory group and CNS Teaching Faculty), BrainLAB, AstraZeneca, Medtronic Kyphon. Travel accommodations/expenses by Elekta, Varian, BrainLAB. Board Member: International Stereotactic Radiosurgery Society (ISRS). Co-chair with AO Spine Knowledge Forum Tumor. Elekta MR Linac Research Consortium, Elekta Spine, Oligometastases and Linac Based SRS Consortia. Gregory J. Czarnota: Funding received from the Terry Fox Foundation Program Project Grant.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

11060_2021_3939_MOESM1_ESM.tif

Supplementary Figure 1: Workflow for the development of classification model and validation. Supplementary file1 (TIF 235 kb)

11060_2021_3939_MOESM2_ESM.tif

Supplementary Figure 2: Kaplan-Meier plots for overall survival across the three surgery subgroups. Supplementary file2 (TIF 160 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geraghty, B.J., Dasgupta, A., Sandhu, M. et al. Predicting survival in patients with glioblastoma using MRI radiomic features extracted from radiation planning volumes. J Neurooncol 156, 579–588 (2022). https://doi.org/10.1007/s11060-021-03939-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-021-03939-9

Keywords

Search

Navigation