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Deciphering the glioblastoma phenotype by computed tomography radiomics.

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Title: Deciphering the glioblastoma phenotype by computed tomography radiomics.
Authors: Compter, Inge1 (AUTHOR) inge.compter@maastro.nl, Verduin, Maikel2 (AUTHOR), Shi, Zhenwei1 (AUTHOR), Woodruff, Henry C.3,4 (AUTHOR), Smeenk, Robert J.5 (AUTHOR), Rozema, Tom6 (AUTHOR), Leijenaar, Ralph T.H.7 (AUTHOR), Monshouwer, René5 (AUTHOR), Eekers, Daniëlle B.P.1 (AUTHOR), Hoeben, Ann2 (AUTHOR), Postma, Alida A.4 (AUTHOR), Dekker, Andre1 (AUTHOR), De Ruysscher, Dirk1 (AUTHOR), Lambin, Philippe3,4 (AUTHOR), Wee, Leonard1 (AUTHOR)
Source: Radiotherapy & Oncology. Jul2021, Vol. 160, p132-139. 8p.
Subjects: Computed tomography, Radiomics, Brain tumors, Phenotypes, Overall survival, Prognosis
Abstract: • A CT-derived radiomics model can predict OS in patients with a glioblastoma. • Discrimination based on the combined clinical and radiomics model was comparable to previous MRI-based models. • Qualitatively high-level datasets will support further model development. Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/− temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan–Meier curves were generated. Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59–0.71. In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models. [ABSTRACT FROM AUTHOR]
Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 151122649
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Deciphering the glioblastoma phenotype by computed tomography radiomics.
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  Data: • A CT-derived radiomics model can predict OS in patients with a glioblastoma. • Discrimination based on the combined clinical and radiomics model was comparable to previous MRI-based models. • Qualitatively high-level datasets will support further model development. Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/− temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan–Meier curves were generated. Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59–0.71. In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Radiotherapy & Oncology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.radonc.2021.05.002
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Radiomics
        Type: general
      – SubjectFull: Brain tumors
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      – SubjectFull: Phenotypes
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      – SubjectFull: Overall survival
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      – SubjectFull: Prognosis
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      – TitleFull: Deciphering the glioblastoma phenotype by computed tomography radiomics.
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              Text: Jul2021
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