Nomograms Based on MRI Radiomics for Differential Diagnosis and Predicting BRAFV600E Expression in Pleomorphic Xanthoastrocytoma and Ganglioglioma.

RATIONALE AND OBJECTIVES: This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression. MATERIALS AND METHODS: This study included 265 patients histologically diagnosed with PXA (n = 113) and GG (n = 152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms. RESULTS: Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms. CONCLUSION: Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.

Development of a nomogram based on radiomics and semantic features for predicting chromosome 7 gain/chromosome 10 loss in IDH wild-type histologically low-grade gliomas.

Purpose: To predict chromosome 7 gain and chromosome 10 loss (+7/-10) in IDH wild-type (IDH-wt) histologically low-grade gliomas (LGG) by machine learning models based on MRI radiomics and semantic features. Methods: A total of 122 patients diagnosed as IDH-wt histologically LGG were retrospectively included in this study. The patients were randomly divided into a training group and a test group in a ratio of 7:3. The radiomics features were extracted from axial T1WI, T2WI, FLAIR and CET1 sequences, respectively. The distance correlation (DC) and least absolute shrinkage and selection operator (LASSO) were used to select the radiomics signatures. Three machine learning algorithms including neural network (NN), support vector machine (SVM), and linear discriminant analysis (LDA) were used to construct radiomics models. In addition, a nomogram was developed by combining the optimal radiomics signature with clinical risk factors, and the potential clinical utility of the nomogram was evaluated using decision curve analysis. Results: The LDA+DC model was identified as the optimal classifier among the six radiomics models. Necrosis was determined as a risk factor for +7/-10 in IDH-wt histologically LGG. The nomogram achieved the best performance, with an AUC of 0.854 and an accuracy of 0.778 in the independent test group. The decision curve of the nomogram confirmed its clinical usefulness in a wide range of thresholds. Conclusion: The nomogram combining radiomics and semantic features can predict the +7/-10 status effectively, which may contribute to the risk stratification and individualized treatment planning of patients with IDH-wt histologically LGG.

Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification.

BACKGROUND: The 2021 World Health Organization (WHO) classification considers a histological low grade glioma with specific molecular characteristics as molecular glioblastoma (mGBM). Accurate identification of mGBM will aid in risk stratification of glioma patients. PURPOSE: To explore the value of machine learning models based on magnetic resonance imaging (MRI) radiomics features in predicting mGBM. MATERIAL AND METHODS: In total, 166 patients histologically diagnosed as low-grade diffuse glioma (WHO II and III) were included in the study. Fifty-three cases were reclassified as mGBM based on molecular status. Four dimensionality reduction methods including distance correlation (DC), gradient boosted decision tree (GBDT), least absolute shrinkage and selection operator (LASSO) and minimal redundancy maximal relevance (MRMR) were used to select the optimal signatures. Six machine learning algorithms including support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), logistic regression (LR), K-nearest neighbour (KNN) and decision tree (DT) were used to develop the classifiers. The relative SD was used to evaluate the stability of the models, and the area under the curve values in the independent test group were used to evaluate their performances. RESULTS: NN_DC was determined as the optimal classifier due to the highest area under the curve of 0.891 in the test group. The classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value of NN_DC were 0.915, 0.842, 0.950, 0.889 and 0.927, respectively. CONCLUSION: Machine learning models can predict mGBM non-invasively, which may help to develop personalized treatment strategies for neurosurgeons and provide an effective tool for accurate stratification in clinical trials.

A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients.

OBJECTIVE: To develop an effective machine learning model to preoperatively predict the occurrence of futile recanalization (FR) of acute basilar artery occlusion (ABAO) patients with endovascular treatment (EVT). MATERIALS AND METHODS: Data from 132 ABAO patients (109 male [82.6 %]; mean age ± standard deviation, 59.1 ± 12.5 years) were randomly divided into the training (n = 106) and test cohort (n = 26) with a ratio of 8:2. FR is defined as a poor outcome [modified Rankin Scale (mRS) 4-6] despite a successful recanalization [modified Thrombolysis in Cerebral Infarction (mTICI) ≥ 2b]. A total of 1130 radiomics features were extracted from diffusion-weighted imaging (DWI) images. The least absolute shrinkage and selection operator (LASSO) regression method was applicated to select features. Support vector machine (SVM) was applicated to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve. RESULTS: The area under the receiver operating characteristic (ROC) curve (AUC) of the radiomics-clinical model was 0.897 (95 % confidence interval, 0.837-0.958) in the training cohort and 0.935 (0.833-1.000) in the test cohort. The AUC of the radiomics model was 0.887 (0.824-0.951) in the training cohort and 0.840 (0.680-1.000) in the test cohort. The AUC of the clinical model was 0.746 (0.652-0.840) in the training cohort and 0.766 (0.569-0.964) in the test cohort. The AUC of the radiomics-clinical model was significantly larger than the clinical model (p = 0.016). A radiomics-clinical nomogram was developed. The decision curve analysis indicated its clinical usefulness. CONCLUSION: The DWI-based radiomics-clinical machine learning model achieved satisfactory performance in predicting the FR of ABAO patients preoperatively.

Lipoxin A4 promotes autophagy and inhibits overactivation of macrophage inflammasome activity induced by Pg LPS.

OBJECTIVE: To explore the role of lipoxin A4 (LXA4) on inflammasome and inflammatory activity in macrophages activated by Porphyromonas gingivalis lipopolysaccharide (PgLPS) one of the major causative agents of chronic periodontitis. METHODS: The mouse macrophage cell line RAW264.7 was used to produce an activated inflammation model. Markers of inflammasome and inflammatory activity and autophagy were assessed by ELISA, reverse transcription polymerase chain reaction (RT-PCR), and Western blot assay. RESULTS: Markers of inflammasome activity, inflammation and autophagy increased with Pg LPS concentration. They also increased with increasing exposure to Pg LPS up to 12h but decreased at 24h. However, markers of autophagy increased. Phosphorylated NF-κBp65 decreased with LXA4, which was similar to results obtained with the autophagy inducer, rapamycin. CONCLUSIONS: LXA4 promoted autophagy and inhibited activation of inflammasomes and inflammation markers in macrophage inflammation induced by PgLPS and this action was linked to the phosphorylation of NF-κB.