Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma
ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study...
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doaj-6b83b0f222a64ad28d120de07813308f2021-10-04T06:40:12ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-10-011110.3389/fonc.2021.734433734433Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With GliomaHongyu Chen0Fuhua Lin1Jinming Zhang2Xiaofei Lv3Jian Zhou4Zhi-Cheng Li5Yinsheng Chen6Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaDepartment of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaInstitute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.https://www.frontiersin.org/articles/10.3389/fonc.2021.734433/fullgliomadeep learningradiomicsmagnetic resonance imagingPTEN |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongyu Chen Fuhua Lin Jinming Zhang Xiaofei Lv Jian Zhou Zhi-Cheng Li Yinsheng Chen |
spellingShingle |
Hongyu Chen Fuhua Lin Jinming Zhang Xiaofei Lv Jian Zhou Zhi-Cheng Li Yinsheng Chen Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma Frontiers in Oncology glioma deep learning radiomics magnetic resonance imaging PTEN |
author_facet |
Hongyu Chen Fuhua Lin Jinming Zhang Xiaofei Lv Jian Zhou Zhi-Cheng Li Yinsheng Chen |
author_sort |
Hongyu Chen |
title |
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma |
title_short |
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma |
title_full |
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma |
title_fullStr |
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma |
title_full_unstemmed |
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma |
title_sort |
deep learning radiomics to predict pten mutation status from magnetic resonance imaging in patients with glioma |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-10-01 |
description |
ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone. |
topic |
glioma deep learning radiomics magnetic resonance imaging PTEN |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2021.734433/full |
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