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Prediction of survival with multi-scale radiomic analysis in glioblastoma patients

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Abstract

We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman’s rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients.

Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.

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Funding

The study was supported by grant from Varian Medical System.

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Authors and Affiliations

  1. Division of Radiation Oncology, McGill University Health Centre, Montreal, Canada

    Ahmad Chaddad, Tamim Niazi & Bassam Abdulkarim

  2. Ecole de Technologie Supérieure, Montréal, Canada

    Ahmad Chaddad

  3. Department of Pathology, McGill University & Research Institute of McGill University Health Centre, Montreal, Canada

    Siham Sabri

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  1. Ahmad Chaddad
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Contributions

A.C. designed the study, analyzed the data and wrote the paper. A.C., S.S., T.N., and B.A., reviewed and gave final approval for publication.

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Correspondence to Ahmad Chaddad.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Chaddad, A., Sabri, S., Niazi, T. et al. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Med Biol Eng Comput 56, 2287–2300 (2018). https://doi.org/10.1007/s11517-018-1858-4

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