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Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images

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Abstract

Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, inclusion of shape features improves OS prediction in GBM patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of GBM OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups.

Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients.

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Acknowledgments

This work is supported by the NMRC Bedside Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren. This research is also supported by the Singapore Ministry of Health’s National Medical Research Council under its Translational and Clinical Research Flagship Program-Tier 1 (Project No: NMRC/ TCR/ 016-NNI/ 2016).

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Authors and Affiliations

  1. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Parita Sanghani & Hongliang Ren

  2. Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore

    Beng Ti Ang & Nicolas Kon Kam King

  3. Faculty of Engineering, National University of Singapore, EA-05-30, 9 Engineering Drive 1, Singapore, 117575, Singapore

    Hongliang Ren

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  1. Parita Sanghani
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  2. Beng Ti Ang
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Correspondence to Hongliang Ren.

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Sanghani, P., Ang, B.T., King, N.K.K. et al. Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images. Med Biol Eng Comput 57, 1683–1691 (2019). https://doi.org/10.1007/s11517-019-01986-z

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  • DOI: https://doi.org/10.1007/s11517-019-01986-z

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