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Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment., (© 2023. Springer Nature Limited.)

The observation of molecular diffusion by means of magnetic resonance imaging (MRI) is significant in the evaluation of the metabolic activity of living tissues. Series of MRI examinations were conducted on a diffusion model to study the behaviour of the diffusion process of different-molecular-weight (MW) paramagnetic MRI contrast agents in an isotropic agar hydrogel medium. The model consisted of a solidified 1 % agar gel with an initial concentration of 0.5 mmol/L contrast solution layered on top of the gel. The diffusion process was monitored at pre-determined time intervals of immediately, 1, 6, 9, 23, and 48 h after introduction of the contrast agents onto the agar gel with a T1-weighted spin-echo (SE) pulse sequence. Three types of paramagnetic contrast agents, Gd-DTPA with a MW of 547.57 g/mol, Prohance with a MW of 558.69 g/mol and MnCl 2 with a MW of 125.84 g/mol, resulted in an approximate average diffusional displacement ratio of 1:1:2 per hour, respectively, within 48 h of the experiment. Therefore, the results of this study supported the hypothesis that the rate of the diffusion process of MRI contrast agents in the agar hydrogel medium is inversely related to their MWs. However, more repetitions are necessary under various types of experimental conditions and also with various types of contrast media of different MWs for further confirmation and validation of these results.