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

Wang, Qiuyu; Li, Qingneng; Mi, Rui; Hai, Yang; Zhang, Heye; Chen, Baodong; Li, Ye; Huang, Guodong; Xia, Jun

doi:10.1002/jmri.26265

Cited by 68 publications

(64 citation statements)

References 40 publications

(87 reference statements)

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“…single approach is usually not sufficient to provide all the information necessary for the 48 understanding of a disease and the accuracy of its diagnosis [5]. On the other hand, the 49 latest advances in disease diagnosis are not always accessible to the entire population, as 50 is the case in developing countries.…”

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confidence: 99%

“…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%

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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%

mentioning

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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“…Similar researches suggested that radiomics combined with machinelearning algorithms displayed promising potential in various fields, including differential diagnosis of glioblastoma, presurgical grading of glioma, and prediction of patient survival outcomes (8,(26)(27)(28). It is worth noting that previous studies primarily focused the value of radiomics in distinguishing low-grade glioma vs. high-grade glioma, whereas the possible different characteristics among the histological subtypes of glioma were not taken into consideration (29)(30)(31). However, the heterogeneity of different glioma subtypes might interfere with the accuracy of the models.…”

Section: Discussionmentioning

confidence: 95%

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

et al. 2019

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Objectives: To investigate the ability of radiomics features from MRI in differentiating anaplastic oligodendroglioma (AO) from atypical low-grade oligodendroglioma using machine-learning algorithms. Methods: A total number of 101 qualified patients (50 participants with AO and 51 with atypical low-grade oligodendroglioma) were enrolled in this retrospective, single-center study. Forty radiomics features of tumor images derived from six matrices were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images. Three selection methods were performed to select the optimal features for classifiers, including distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT). Then three machine-learning classifiers were adopted to generate discriminative models, including linear discriminant analysis, support vector machine, and random forest (RF). Receiver operating characteristic analysis was conducted to evaluate the discriminative performance of each model. Results: Nine predictive models were established based on radiomics features from T1C images and FLAIR images. All of the classifiers represented feasible ability in differentiation, with AUC more than 0.840 when combined with suitable selection method. For models based on T1C images, the combination of LASSO and RF classifier represented the highest AUC of 0.904 in the validation group. For models based on FLAIR images, the combination of GBDT and RF classifier showed the highest AUC of 0.861 in the validation group. Conclusion: Radiomics-based machine-learning approach could potentially serve as a feasible method in distinguishing AO from atypical low-grade oligodendroglioma.

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“…Invasive biopsy is the "gold standard" for pretreatment evaluation of histologic grade, but is often limited due to sample bias [17]. As a non-invasive approach for characterizing tumors comprehensively, ADC-based radiomics has demonstrated potential for prediction of the histologic grade of giloma [18], cervical cancer [19], and bladder cancer [20]. Therefore, we investigated the predictive ability of radiomics features from ADC maps for the histologic grade of SCCs of the tongue and MF.…”

Section: Discussionmentioning

confidence: 99%

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the tongue and mouth floor: A preliminary study

Ren

Shi

et al. 2019

Preprint

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Background The assessment of histologic grade plays an important part in prognosis evaluation of squamous cell carcinoma (SCC) of the tongue and mouth floor (MF). This study was performed to ascertain if apparent diffusion coefficient (ADC)-based radiomics can predict histologic grade in SCC of the tongue and MF. Methods This study involved 95 patients (training cohort: n = 63; testing cohort: n = 32), who were categorized as low- and high-grade SCCs according to the pathologic findings. A total of 526 radiomics features were extracted from ADC maps. Radiomics signature was constructed with least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and testing cohorts. Results Six features were selected to construct the radiomics signature for predicting histologic grade. The ADC-based radiomics signature performed well in discriminating between low- and high-grade tumors, with AUCs of 0.82 and 0.78 in the training and testing cohorts, respectively. Based on the cutoff value of the training cohort, the radiomics signature achieved accuracies of 0.75 and 0.78, sensitivities of 0.71 and 0.86, and specificities of 0.83 and 0.64 in the training and testing cohorts, respectively. Conclusions The ADC-based radiomics signature could identify histologic grade in SCC of the tongue and MF.

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

Abstract: 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.

Cited by 68 publications

References 40 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

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the tongue and mouth floor: A preliminary study

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