Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging

Takahashi, Satoshi; Takahashi, Wataru; Tanaka, Shota; Haga, Akihiro; Nakamoto, Takahiro; Suzuki, Yuichi; Mukasa, Akitake; Takayanagi, Shunsaku; Kitagawa, Yosuke; Hana, Taijun; Nejo, Takahide; Nomura, Masashi; Nakagawa, Keiichi; Saito, Nobuhito

doi:10.1016/j.ijrobp.2019.07.011

Cited by 30 publications

(36 citation statements)

References 26 publications

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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%

“…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]]. 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].…”

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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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confidence: 99%

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confidence: 99%

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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“…Radiomics is an emerging field that treats images as data rather than pictures and analyzes a large number of features extracted from 1 image in relation to clinical variables of interest. A few studies on radiomics analyses of glioma have been published over the last years and advocated for machine learning models in predicting tumor histology and grade [25]. Radiomics has been suggested as a robust strategy to noninvasively classify lesions [14,26].…”

Section: Discussionmentioning

confidence: 99%

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Zhao

Feng

et al. 2020

BMC Neurol

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Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy.Methods: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. Results: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

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“…It might indicate that the distinguishment between such highly imaging overlapped pneumonia may need the emphasized features in the spatial or frequency domains or the relatively higher stability of these higher-order features. In clinical cancer research, radiomics features have been shown to re ect tumor invasiveness, malignancy, and lymph node metastasis potential and other biological characteristics [27][28][29] . However, we speculate that the cause of CT image heterogeneity between COVID-19 and Non-COVID-19 may be different from the tumor.…”

Section: Discussionmentioning

confidence: 99%

CT-based Radiomics Combined with Signs: A Valuable Tool to help Physician Discriminate COVID-19 and Other Viral Pneumonia

Huang

Zhang

et al. 2020

Preprint

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Background: In this COVID-19 pandemic, the differential diagnosis of different viral types of pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 pneumonia and other viral pneumonia.Methods: A total of 181 patients with confirmed viral pneumonia (COVID-19: 89 cases, Non-COVID-19: 92 cases; training cohort: 126 cases; test cohort: 55 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model and the combined model were evaluated using an intra-cross validation cohort. Diagnostic performance of two models was assessed via receiver operating characteristic (ROC) analysis.Results: The combined models consisted of 3 significant CT signs and 14 selected features and demonstrated better discrimination performance between COVID-19 and Non-COVID-19 pneumonia than the single radiomics model. For the radiomics model along, the area under the ROC curve (AUC) were 0.904 (sensitivity, 85.5%; specificity, 84.4%; accuracy, 84.9%) in the training cohort and 0.866 (sensitivity, 77.8%; specificity, 78.6%; accuracy, 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity, 91.9%; specificity, 85.9%; accuracy, 88.9%), while that for the test cohort was 0.943 (sensitivity, 88.9%; specificity, 85.7%; accuracy, 87.3%).Conclusion: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and other viral pneumonia with satisfactory performance.

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Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging

Cited by 30 publications

References 26 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

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

CT-based Radiomics Combined with Signs: A Valuable Tool to help Physician Discriminate COVID-19 and Other Viral Pneumonia

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