28
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      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

          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.

          Related collections

          Most cited references4

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

          CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009.

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

            Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

            Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (©) RSNA, 2016 Online supplemental material is available for this article.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Recent advances in the molecular understanding of glioblastoma

              Glioblastoma is the most common and most aggressive primary brain tumor. Despite maximum treatment, patients only have a median survival time of 15 months, because of the tumor’s resistance to current therapeutic approaches. Thus far, methylation of the O 6-methylguanine-DNA methyltransferase (MGMT) promoter has been the only confirmed molecular predictive factor in glioblastoma. Novel “genome-wide” techniques have identified additional important molecular alterations as mutations in isocitrate dehydrogenase 1 (IDH1) and its prognostic importance. This review summarizes findings and techniques of genetic, epigenetic, transcriptional, and proteomic studies of glioblastoma. It provides the clinician with an up-to-date overview of current identified molecular alterations that should ultimately lead to new therapeutic targets and more individualized treatment approaches in glioblastoma.
                Bookmark

                Author and article information

                Contributors
                zc.li@siat.ac.cn
                zhaiguangtao@sjtu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 September 2017
                4 September 2017
                2017
                : 7
                : 10353
                Affiliations
                [1 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, ; Shanghai, China
                [2 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, Department of Neurosurgery/Neuro-oncology, , Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, ; Guangzhou, China
                [3 ]ISNI 0000 0001 0483 7922, GRID grid.458489.c, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ; Shenzhen, China
                [4 ]ISNI 0000 0004 1761 2484, GRID grid.33763.32, School of Electrical and Information Engineering, Tianjin University, ; Tianjin, China
                Author information
                http://orcid.org/0000-0003-4140-0580
                http://orcid.org/0000-0001-8165-9322
                Article
                10649
                10.1038/s41598-017-10649-8
                5583361
                28871110
                36772d04-2d8c-4a0f-a425-bb23bd3c76eb
                © The Author(s) 2017

                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
                : 24 May 2017
                : 11 August 2017
                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Uncategorized
                Uncategorized

                Comments

                Comment on this article