14
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients ( n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 ( IDH1), O 6-methylguanine–DNA methyltransferase, epidermal growth factor receptor variant III ( EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival ( p < 0.001), thereby highlighting the synergistic consideration of molecular and imaging measures for prognostication. Moreover, the imaging characteristics suggest that subtype-specific treatment of peritumoral infiltrated brain tissue might be more effective than current uniform standard-of-care. Finally, our analysis found subtype-specific radiogenomic signatures of EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: found
          • Article: not found

          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Multiple Comparisons among Means

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

              Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.
                Bookmark

                Author and article information

                Contributors
                christos.davatzikos@uphs.upenn.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 March 2018
                23 March 2018
                2018
                : 8
                : 5087
                Affiliations
                [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiology, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Neurosurgery, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [4 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [5 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Department of Biomedical Informatics, , Northwestern University Feinberg School of Medicine, ; Chicago, IL USA
                [6 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiation Oncology, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                Author information
                http://orcid.org/0000-0001-9786-3707
                Article
                22739
                10.1038/s41598-018-22739-2
                5865162
                29572492
                cc4e6bde-13a5-4360-92c1-29da174676d1
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 November 2017
                : 23 February 2018
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Uncategorized
                Uncategorized

                Comments

                Comment on this article