Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

Abstract

BackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.MethodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2–5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.ResultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.ConclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

“…As shown by the bar diagram, the Gray-Level Co-Occurrence Matrix (GLCM) is the most used one, followed by the Gray-Level Run Length Matrix (GLRLM) and the First Order. Interestingly, since 2021 deep learning features [93] were introduced, e.g., Hiremath et al [22] and Liu et al [94] .

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The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion

“…believed that the tumor-brain interface was not correlated with brain invasion ( 14 ), while Joo et al. believed that the tumor-brain interface was important for the diagnosis of brain invasion ( 10 , 12 , 13 ). This study concluded that the tumor-brain interface was the most meaningful MRI feature, an independent risk factor for WHO grade and brain invasion, with the best diagnostic efficacy among all single variables ( Figure 4 ).…”

Role of the Neutrophil to Lymphocyte Ratio in Guillain Barré Syndrome: A Systematic Review and Meta-Analysis

“…Stress and severe illness can activate the hypothalamic-pituitary-adrenal (HPA) axis leading to elevations in cortisol that stimulate neutrophil demargination and maturation, as well as lymphocyte apoptosis [35][36][37][38][39]. Immunologically, severe illness increases the production of neutrophils from the bone marrow and can lead to lymphopenia via various proposed mechanisms [40][41][42]. Ultimately, a relative neutrophilia and lymphopenia can result, leading to an elevated NLR.…”