Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.

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OBJECTIVEEvaluation of living tissue elasticity has wide applications in disease characterization and prognosis prediction. Few previous ex vivo attempts have been made to characterize spinal cord elasticity (SCE). Recently, tissue elasticity assessment has been clinically feasible using ultrasound shear wave elastography (SWE). The current study aims to characterize SCE in healthy dogs, in vivo, utilizing SWE, and to address SCE changes during compression.METHODSTen Greyhound dogs (mean age 14 months; mean weight 14.3 kg) were anesthetized and tracheally intubated, with hemodynamic and neurological monitoring. A 3-level, midcervical laminectomy was performed. SCE was assessed at baseline. Next, 8- and 13-mm balloon compressions were sequentially applied ventral to the spinal cord.RESULTSThe mean SCE was 18.5 ± 7 kPa. Elasticity of the central canal, pia mater, and dura mater were 21.7 ± 9.6 kPa, 26.1 ± 14.8 kPa, and 63.2 ± 11.5 kPa, respectively. As expected, the spinal cord demonstrated less elasticity than the dura mater (p < 0.0001) and pia mater (trend toward significance p = 0.08). Notably, the 13-mm balloon compression resulted in a stiffer spinal cord than at baseline (233 ± 73 kPa versus 18.5 ± 7 kPa, p < 0.0001) and 8-mm balloon compression (233 ± 73 kPa versus 185 ± 68 kPa, p < 0.048).CONCLUSIONSIn vivo SCE evaluation using SWE is feasible and comparable to earlier reports, as demonstrated by physical sectioning of the spinal cord. The compressed spinal cord is stiffer than a free spinal cord, with a linear increase in SCE with increasing mechanical compression. Knowledge of the biomechanical properties of the spinal cord including SCE has potential implications for disease management and prognosis.