A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

Chekouo, Thierry; Mohammed, Shariq; Rao, Arvind

doi:10.1016/j.nicl.2020.102437

Cited by 15 publications

(7 citation statements)

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“…It provides numerical acquisition of pixel-level differences in areas that the human eye cannot distinguish on images [22]. In the literature, histogram analysis has been used in the detection and classification of brain tumors in numerous diseases such as multiple sclerosis, acute ischemia, Alzheimer's, and tinnitus [23][24][25][26][27]. In a study on multiple sclerosis patients, histogram analysis showed a difference in the structure of the optic nerve compared to the controls [28].…”

Section: Discussionmentioning

confidence: 99%

MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis

Eroğlu¹,

Baykara²

2022

Journal of Surgery and Medicine

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Background/Aim: Type 1 neurofibromatosis (NF1) is the most common neurocutaneous disease affecting numerous systems. Optic pathway glioma (OPG) is a common tumor in children with NF1 and often has variable clinical presentations. In this study, histogram analysis parameters of optic nerves were measured in the magnetic resonance images (MRI) of children with NF1 and compared with a control group. Methods: This case-control study consisted of three groups: Ten patients with NF1 without optic pathway glioma (bilateral optic nerve, n: 20), four patients with NF1 with bilateral optic pathway glioma (n: 8), and nineteen healthy controls (n: 38). ROIs were placed on bilateral pre-chiasmatic optic nerves in the images. With histogram analysis, average gray level intensity (mean), the standard deviation, minimum, median, and maximum intensity, uniformity, entropy, kurtosis, variance, skewness, size% M, size% U, size% L, and percentiles were measured. Results: Mean, median, 3%, 5%, 10%, 25%, and 75% values were higher in NF1 patients with optic pathway glioma (NF1-OPG) than in NF1 patients without optic pathway glioma (NF1-woOPG), and the control group (P<0.001). The same values were significantly higher in the NF1-woOPG group compared to the control group (P<0.001). The minimum, maximum, 1%, 90%, 95%, 97%, and 99% values were significantly higher in the NF1-OPG and NF1-woOPG groups than the control group (P<0.001). The entropy value was significantly higher in the NF1-OPG group than the NF1-woOPG and control groups (5.73, 4.93, and 5.25, respectively, P=0.016). Conclusion: MRI histogram analysis revealed significant differences between NF1-OPG, NF1-woOPG, and healthy individuals in terms of optic nerves. Thus, we think that it can be used to monitor the optic nerves of children with NF1.

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Section: Discussionmentioning

confidence: 99%

MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis

Eroğlu¹,

Baykara²

2022

Journal of Surgery and Medicine

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“…GLCM conduces to reflecting the comprehensive information about pixel distribution containing direction, distance, gray value, and the pattern of gray level arrangement (28), and Correlation represents the linear dependency of gray level values to their respective voxels in the GLCM textural features. It has been applied previously in the evaluation of breast cancer, osteosarcoma, lung cancer and gliomas in imaging modalities such as CT, MRI, and PECT (31)(32)(33)(34)(35).…”

Section: Discussionmentioning

confidence: 99%

Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma

et al. 2021

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BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.MethodWe extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients.ResultsA total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models.ConclusionIn this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.

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“…In some previous radiomics studies, the GLCM features also played an important role in predicting the IDH mutation status. Checkout et al developed a new approach to predict IDH mutation status that outperformed competing methods ( 38 ), while Park et al ( 39 ) found that GLCM was one of the strongest IDH status prediction factors. Furthermore, in a study by Chaddad et al ( 40 ), GLCM had a significant role in predicting survival in patients with glioblastoma.…”

Section: Discussionmentioning

confidence: 99%

A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

et al. 2021

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ObjectiveThis study aimed to develop a radiomics model to predict early recurrence (<1 year) in grade II glioma after the first resection.MethodsThe pathological, clinical, and magnetic resonance imaging (MRI) data of patients diagnosed with grade II glioma who underwent surgery and had a recurrence between 2017 and 2020 in our hospital were retrospectively analyzed. After a rigorous selection, 64 patients were eligible and enrolled in the study. Twenty-two cases had a pathologically confirmed recurrent glioma. The cases were randomly assigned using a ratio of 7:3 to either the training set or validation set. T1-weighted image (T1WI), T2-weighted image (T2WI), and contrast-enhanced T1-weighted image (T1CE) were acquired. The minimum-redundancy-maximum-relevancy (mRMR) method alone or in combination with univariate logistic analysis were used to identify the most optimal predictive feature from the three image sequences. Multivariate logistic regression analysis was then used to develop a predictive model using the screened features. The performance of each model in both training and validation datasets was assessed using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 396 radiomics features were initially extracted from each image sequence. After running the mRMR and univariate logistic analysis, nine predictive features were identified and used to build the multiparametric radiomics model. The model had a higher AUC when compared with the univariate models in both training and validation data sets with an AUC of 0.966 (95% confidence interval: 0.949–0.99) and 0.930 (95% confidence interval: 0.905–0.973), respectively. The calibration curves indicated a good agreement between the predictable and the actual probability of developing recurrence. The DCA demonstrated that the predictive value of the model improved when combining the three MRI sequences.ConclusionOur multiparametric radiomics model could be used as an efficient and accurate tool for predicting the recurrence of grade II glioma.

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A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

Cited by 15 publications

References 58 publications

MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis

MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis

Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma

A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

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