The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM...
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doaj-2fb3cdeab3c442fbaa0137b96807e5582021-01-29T00:04:57ZengMDPI AGJournal of Imaging2313-433X2021-01-017171710.3390/jimaging7020017The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma PatientsMichael Baine0Justin Burr1Qian Du2Chi Zhang3Xiaoying Liang4Luke Krajewski5Laura Zima6Gerard Rux7Chi Zhang8Dandan Zheng9Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USADepartment of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USADepartment of Radiation Oncology, University of Florida Proton Institute, Jacksonville, FL 32206, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USADepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USAGlioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.https://www.mdpi.com/2313-433X/7/2/17radiomicsglioblastomaGBMpseudoprogressionradiation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michael Baine Justin Burr Qian Du Chi Zhang Xiaoying Liang Luke Krajewski Laura Zima Gerard Rux Chi Zhang Dandan Zheng |
spellingShingle |
Michael Baine Justin Burr Qian Du Chi Zhang Xiaoying Liang Luke Krajewski Laura Zima Gerard Rux Chi Zhang Dandan Zheng The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients Journal of Imaging radiomics glioblastoma GBM pseudoprogression radiation |
author_facet |
Michael Baine Justin Burr Qian Du Chi Zhang Xiaoying Liang Luke Krajewski Laura Zima Gerard Rux Chi Zhang Dandan Zheng |
author_sort |
Michael Baine |
title |
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients |
title_short |
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients |
title_full |
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients |
title_fullStr |
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients |
title_full_unstemmed |
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients |
title_sort |
potential use of radiomics with pre-radiation therapy mr imaging in predicting risk of pseudoprogression in glioblastoma patients |
publisher |
MDPI AG |
series |
Journal of Imaging |
issn |
2313-433X |
publishDate |
2021-01-01 |
description |
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs. |
topic |
radiomics glioblastoma GBM pseudoprogression radiation |
url |
https://www.mdpi.com/2313-433X/7/2/17 |
work_keys_str_mv |
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