Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade

Duan, Chongfeng; Li, N; Li., Yang; Liu, F; Wang, J C; Liu, X J; Xu, Wenjian

doi:10.1016/j.crad.2022.01.039

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Either 2D or 3D features were used in the previous studies. Duan CF et al (11) used 2D features to compare different models for predicting meningioma grade. In their study, seven models constructed by 2D features performed well with a high AUC (all>0.80), and SVM and KNN performed better than the other models with an AUC of 0.88 and a larger net benefit in the DCA curve.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics has been used widely in recent years (7)(8)(9). Many researchers have applied radiomics in the study of meningioma, especially the prediction of meningioma grade (10)(11)(12)(13)(14)(15) (Table 1). In previous studies, there were two different methods applied to feature extraction: two-dimensional (2D) radiomics features (10,11) and three-dimensional (3D) radiomics features (12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have applied radiomics in the study of meningioma, especially the prediction of meningioma grade (10)(11)(12)(13)(14)(15) (Table 1). In previous studies, there were two different methods applied to feature extraction: two-dimensional (2D) radiomics features (10,11) and three-dimensional (3D) radiomics features (12)(13)(14)(15). To the best of our knowledge, no study has directly compared the performance of 2D and 3D radiomics features in predicting meningioma grade.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study

Duan

Li²,

Liu³

et al. 2023

Front. Oncol.

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ObjectivesThe objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images.MethodsThere were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve.ResultsThere were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models.ConclusionsThe 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma.Key pointsThere was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study

Duan

Li²,

Liu³

et al. 2023

Front. Oncol.

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“…Radiomics has the capability to predict outcomes through modeling based on high-throughput extraction of texture parameters from images (Kumar et al 2012, Lambin et al 2012, Gillies et al 2016, Duan et al 2022. In meningioma grading, when combined with machine learning, radiomics has demonstrated promising results.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al (2020) combined conventional MRI (cMRI) with radiomics features extracted from ADC and SWI modalities, employing random forests, to attain high grading accuracy. Duan et al (2022) compared the performance of seven different machine learning models in meningioma grading and showed that various radiomics features derived from enhanced T1-weighted images can predict meningioma grading. Chen et al (2019) extracted 40 texture parameters from T1-weighted images to assess their utility in grading meningiomas.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions

Zhang,

Miao,

et al. 2024

Phys. Med. Biol.

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Background:Radiomics and deep learning techniques have become integral in meningioma grading. The combination of these approaches holds the potential to enhance classification accuracy. Given the frequent occurrence of peritumoral edema (PTE) in meningiomas, investigating the potential value of PTE requires further research.Objectives:To address the challenge of meningioma grading, this study introduces a unique approach that integrates radiomics and deep learning techniques. The primary focus is on the development of a Transfer Learning-based Meningioma Feature Extraction Model (MFEM), leveraging both Vision Transformer (ViT) and Convolutional Neural Network (CNN) architectures. Furthermore, the study explores the potential significance of the peritumoral edema (PTE) region in enhancing the grading process.Materials and Methods:A retrospective study was conducted involving 98 meningioma patients, with 60 classified as low-grade meningiomas and 38 as high-grade meningiomas. PTE was observed in 51.02% of low-grade meningiomas patients and 89.47% of high-grade meningiomas patients. Magnetic resonance images were acquired using a GE Signa HDxt 1.5T MRI scanner, incorporating T2-weighted Fluid-Attenuated Inversion Recovery (T2 Flair) sequences. A Transfer Learning-based Meningioma Feature Extraction Model (MFEM) was constructed by combining ViT and CNN models to extract deep features from the Transformer layer, alongside radiomics features. These were then utilized as input for a machine learning classifier to accurately grade meningiomas.Results:The proposed method demonstrated excellent grading accuracy and robustness on the meningioma dataset, offering valuable guidance for treatment decisions. The approach achieved 92.86% accuracy, 93.44% precision, 95% sensitivity, and 89.47% specificity.Conclusion:The radiomics and deep learning-based approach presented in this study offers a reliable method for preoperative meningioma grading. This innovative method not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making.

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An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

Duan,

Hao,

Cui

et al. 2024

J Digit Imaging. Inform. med.

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Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade

Cited by 9 publications

References 26 publications

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions

An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

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