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Prognostic factors in adult brainstem gliomas: a multicenter, retrospective analysis of 101 cases

  • Clinical-Patient Studies
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Abstract

Background Adult brainstem gliomas (BSG) are uncommon and poorly understood with respect to prognostic factors. We retrospectively evaluated the clinical, radiographic, histologic, and treatment features from 101 adults with presumed or biopsy proven BSG to determine prognostic factors. Patients and Methods We reviewed the records of patients diagnosed from 1987–2005. We used Cox proportional hazard models to determine prognostic factors. Results These 50 male and 51 female patients ranged in age from 18 to 79 years at diagnosis (median 36 years) with follow-ups from 1 to 261 months (median 47 months). The overall survival for all patients at 5 and 10 years was 58% and 41%, respectively, with a median survival of 85 months (range 1–228). Out of 24 candidate prognosis factors, we selected seven covariates for proportional hazards model by Lasso procedure: age of diagnosis, ethnicity, need for corticosteroids, tumor grade, dysphagia, tumor location, and karnofsky performance status (KPS). Univariate analysis showed that these seven factors are significantly associated with survival. Multivariate analysis showed that four covariates significantly increased hazard for survival: ethnicity, tumor location, age of diagnosis, and tumor grade. Conclusions In this study, we identified four prognostic factors that were significantly associated with survival in adults with BSGs. Overall, these patients have a better prognosis than children with BSGs reported in the literature. These results call for larger prospective studies to fully assess the importance of these factors in the clinical setting and to help stratify patients in future clinical studies.

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Acknowledgments

We gratefully acknowledge the support of our patients and their families who have allowed us to perform this study.

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Authors and Affiliations

  1. Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, SW430, 44 Binney Street, Boston, MA, 02115, USA

    Santosh Kesari, Vassilios Markos, Jan Drappatz & Patrick Y. Wen

  2. Department of Neurology, Division of Cancer Neurology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Santosh Kesari, Jan Drappatz & Patrick Y. Wen

  3. Harvard Medical School, Boston, MA, USA

    Santosh Kesari, Jan Drappatz & Patrick Y. Wen

  4. Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01602, USA

    Ryung S. Kim

  5. Department of Medical Oncology, University General Hospital of Heraklion, Crete, Greece

    Vassilios Markos

  6. Department of Neurology, Hospital of the University of Pennsylvania, University of Pennsylvania Medical Center, 3400 Spruce Street, Philadelphia, PA, 19104, USA

    Amy A. Pruitt

Authors
  1. Santosh Kesari
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  2. Ryung S. Kim
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  3. Vassilios Markos
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  4. Jan Drappatz
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  5. Patrick Y. Wen
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  6. Amy A. Pruitt
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Corresponding author

Correspondence to Santosh Kesari.

Electronic supplementary material

(DOC 38 kb)

Figure S1:

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Lasso yields more accurate model than stepwise algorithm in our data. Figure shows cross-validated prediction errors according to different L1 norms: lower value corresponds to better performance. The blue horizontal line indicates the prediction error from traditional stepwise algorithm. The figure clearly demonstrates that Lasso performs better than stepwise procedure in our data and is the rationale for using this technique for this dataset.

Figure S2A:

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Univariate analysis of survival. Kaplan-Meier survival curves and score (log-rank) tests of age and eight factors that were found to be significant are shown. 2A: age, race, KPS, tumor grade, steroid use, and dysphagia. 2B MRI-hydrocephalus, motor weakness, and tumor location. Two continuous variables, KPS and the age of diagnosis, were divided into patients with values higher than third quartile and those with values less than first quartile.

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Figure S3A:

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Imaging of BSGs. Examples of MRI of clinical-imaging subgroups. 4A. a, b- Exophytic/cystic: axial FLAIR (a) and T1-post gad (b) sequences showing an enhancing exophytic mass with mass effect on brainstem; c,d- axial FLAIR (c) and T1-post gad (d) sequences showing an cystic branstem glioma. 4B. Diffuse pontine: axial FLAIR (a) and T1-post gad (b) of a diffuse BSGs in a patient who had a prior PNET as a child. 4C. Tectal: axial T2 weighted (a) and T1-post gad (b) sequences of a patient with a tectal glioma with some enhancement (arrow) 4D. CMJxn: sagital T1-post gad sequence showing an cervicomedullary junction mass (arrow). 4E. Multifocal /gliomatosis: a, b- coronal (a) and axial T1-post gad (b) sequences showing a multifocal BSG (arrows); c, d- axial FLAIR weighted sequences showing diffuse involvement of brainstem, temporal lobes and thalami.

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Figure S3E:

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Kesari, S., Kim, R.S., Markos, V. et al. Prognostic factors in adult brainstem gliomas: a multicenter, retrospective analysis of 101 cases. J Neurooncol 88, 175–183 (2008). https://doi.org/10.1007/s11060-008-9545-1

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  • DOI: https://doi.org/10.1007/s11060-008-9545-1

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