Radiomics strategy for glioma grading using texture features from multiparametric MRI

Tian, Qiang; 林燕,; Zhang, Xi; Hu, Yu Chuan; Han, Yu; Liu, Zhi Cheng; Nan, Haiyan; Sun, Qian; Sun, Ying; Yang, Yang; Yu, Ying; Zhang, Jin; Hu, Bo; Xiao, Gang; Chen, Ping; Tian, Sufang; Xu, Jie; Wang, Wen; Cui, Guang Bin

doi:10.1002/jmri.26010

Cited by 177 publications

(150 citation statements)

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“…LGGs and HGGs of 94.4% and 94% respectively when T1-weighted before and 453 after contrast-enhanced images were studied, and 96.5% and 97% when they studied 454 T2-weighted and FLAIR images. Therefore, in this work conventional MRI (T 1Gd and 455 T 2 contrasts) was studied, while others have analyzed advanced MRI or a combination 456 of both [5,[21][22][23][24][51][52][53][54]. The model was created from a simple mathematical method (a 457 multiple linear regression), in comparison to others in which mathematical tools of 458 higher complexity were utilized [7,[52][53][54]].…”

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confidence: 99%

“…The model was created from a simple mathematical method (a 457 multiple linear regression), in comparison to others in which mathematical tools of 458 higher complexity were utilized [7,[52][53][54]]. The best model was found to use only 3 459 variables of a single type (quantitative, being also only texture features), instead of a 460 combination of different classes and types of variables [21,24,51,53]. A texture analysis 461 was performed (which is easy to implement for any type of MRI) and a single texture 462 matrix was used instead of different matrices [24], being the chosen one (GLSZM) a 463 suitable texture matrix when heterogeneity is a predominat characteristic of the object 464 of study.…”

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confidence: 99%

“…The best model was found to use only 3 459 variables of a single type (quantitative, being also only texture features), instead of a 460 combination of different classes and types of variables [21,24,51,53]. A texture analysis 461 was performed (which is easy to implement for any type of MRI) and a single texture 462 matrix was used instead of different matrices [24], being the chosen one (GLSZM) a 463 suitable texture matrix when heterogeneity is a predominat characteristic of the object 464 of study. In addition, since the studied database is publicly available and the 465 mathematical expression of the best model was explicitly reported, the reproducibility 466 of the presented methodology and the corroboration of the results by other independent 467 studies is feasible.…”

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confidence: 99%

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A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Suárez-García

Hernández-López

Moreno-Barbosa

et al. 2020

Preprint

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11Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and 12 prognosis of patients. The purpose of this work was to develop a low cost and easy to 13 implement classification model which distinguishes low grade gliomas (LGGs) from high 14 grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. 15Different combinations between MRI contrasts (T 1Gd and T 2 ) and one segmented 16 glioma region (necrotic and non-enhancing tumor core (NCR/NET)) were studied. 17 Texture features obtained from the Gray Level Size Zone Matrix (GLSZM) were 18 calculated. An under-samplig method was proposed to divide the data into different 19 training subsets and subsequently extract complementary information for the creation 20 of distinct classification models. The sensitivity, specificity and accuracy of the models 21 were calculated. The best model was explicitly reported. The best model included only 22 three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 23 88.24% and 91.18% respectively. According to the features of the model, when the 24 NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in 25 the T 1Gd images and LGGs had a more heterogeneous texture than HGGs in the T 2 26 images. These novel results partially contrast with results from literature. The best 27 model proved to be useful for the classification of gliomas. Complementary results 28 showed that heterogeneity of gliomas depended on the studied MRI contrast. The 29 model presented stands out as a simple, low cost, easy to implement, reproducible and 30 highly accurate glioma classifier. What is more important, it should be accessible to 31 populations with reduced economic and scientific resources.32

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A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Suárez-García

Hernández-López

Moreno-Barbosa

et al. 2020

Preprint

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“…False classification could directly cause delayed surgery in high‐grade glioma, as well as inappropriate surgery in low‐grade glioma patients, which significantly affects patient outcomes. Accurate classification of gliomas is still crucial for prescribing therapy and assessing the prognosis of patients …”

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confidence: 99%

“…Accurate classification of gliomas is still crucial for prescribing therapy and assessing the prognosis of patients. 4 Magnetic resonance imaging (MRI) is widely used as a noninvasive technique in the preoperative work-up of glioma, and it plays an important role in the classification of gliomas. MRI provides clinicians and researchers with information regarding tumor location, size, and relationship with critical areas in the brain.…”

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Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

Wang

et al. 2018

Magnetic Resonance Imaging

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Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Alizadeh

Mahloojifar

2020

Int J Imaging Syst Tech

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Melanoma is one of the most dangerous types of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this study, a system is proposed to detect melanoma automatically using an ensemble approach, including convolutional neural networks (CNNs) and image texture feature extraction. Two CNN models, a proposed network and the VGG‐19, were employed to classify images in the CNN phase. Furthermore, texture features were extracted, and their dimension was reduced using kernel principal component analysis (kPCA) to improve the classification performance in the feature extraction‐based phase. The results of each step were then combined to obtain the final diagnosis. The proposed method was evaluated on three databases, that is, ISIC 2016, ISIC 2019, and PH2. The accuracy, average precision, sensitivity, and specificity of the proposed method on the ISIC 2016 dataset were 85.2%, 66%, 52%, and 93.4%, respectively. These evaluation metrics for the ISIC 2019 database were obtained equal to 96.7%, 95.1%, 96.3%, and 97.1%, respectively. Furthermore, the accuracy, sensitivity, and specificity of the proposed method on the PH2 dataset were 97.5%, 100%, and 96.88%, respectively. According to the experimental results, the ensemble method improves the evaluation metrics compared to each phase separately. Besides, the proposed approach can increase the performance of melanoma detection, compared to previous studies.

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Radiomics strategy for glioma grading using texture features from multiparametric MRI

Abstract: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

Cited by 177 publications

References 41 publications

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

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