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      A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

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          Abstract

          Objective

          The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.

          Methods

          In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS.

          Results

          There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training ( P < 0.001, multivariable Cox regression) and validation ( P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts.

          Conclusions

          PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

          Highlights

          • •

            We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas.

          • •

            We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis.

          • •

            PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.

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          Most cited references15

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

          Zelan Ma, Cuishan Liang, Jie Tian … (2016)
          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.
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            Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.

            Prateek Prasanna, Jay Patel, Sasan Partovi … (2017)
            Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM.
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              ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization.

              Shota Yamamoto, Ronald Korn, Rahmi Oklu … (2014)
              To present a radiogenomic computed tomographic (CT) characterization of anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) (ALK+).
              Article 0 comments Cited 56 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Tao Jiang
                Yinyan Wang
                Journal
                Journal ID (nlm-ta): Neuroimage Clin
                Journal ID (iso-abbrev): Neuroimage Clin
                Title: NeuroImage : Clinical
                Publisher: Elsevier
                ISSN (Electronic): 2213-1582
                Publication date PMC-release: 16 October 2018
                Publication date Collection: 2018
                Publication date (Electronic): 16 October 2018
                Volume: 20
                Pages: 1070-1077
                Affiliations
                [a ]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
                [b ]Chinese Academy of Sciences, Institute of Automation, Beijing, China
                [c ]Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,
                [d ]Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
                [e ]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
                [f ]Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
                [g ]China National Clinical Research Center for Neurological Diseases, Beijing, China
                Author notes
                [* ]Corresponding author at: Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing 100050, China. taojiang1964@ 123456163.com
                [** ]Corresponding author at: Beijing Tiantan Hospital, Department of Neurosurgery, Capital Medical University, 6 Tiantanxili, Beijing 100050, China. tiantanyinyan@ 123456126.com
                [1]

                Co-first authors

                [2]

                Co-corresponding authors

                Article
                Publisher ID: S2213-1582(18)30324-3
                DOI: 10.1016/j.nicl.2018.10.014
                PMC ID: 6202688
                PubMed ID: 30366279
                SO-VID: 13adc5f8-fb10-41d0-a88f-a65390addc63
                Copyright © © 2018 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 3 March 2018
                Date revision received : 16 August 2018
                Date accepted : 15 October 2018
                Categories
                Subject: Regular Article

                Keywords: radiomic analysis,lower-grade gliomas,progression-free survival,radiogenomics
                Data availability:
                Keywords: radiomic analysis, lower-grade gliomas, progression-free survival, radiogenomics

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