Feature Extraction of Apparent Diffusion Coefficient in Human Brain Lesions to Distinguish Benign and Malignant Using MRI

Abstract

Introduction: Apparent Diffusion Coefficient (ADC) is one of the most common Magnetic Resonance Imaging (MRI) techniques that is frequently used in the brain tumor identification process in the current clinical neuro-imaging setup. The ADC quantitatively measures the diffusivity of water molecules within living tissues using Diffusion-Weighted Imaging (DWI) and provides information about the net direction of the water diffusion and the boundaries that restrict the diffusion which are crucial to identify certain pathological conditions in the cellular level. Objectives: This study focused on extracting image texture features from MRI-ADC images of human brain tumors, and the patient demographics, identifying the distribution pattern of each feature, and developing robust Machine Learning (ML) models that categorize the tumors according to benign or malignant nature as well as the aggressiveness of gliomas. Materials and Methods: This prospective study was designed to be conducted in three main steps: identify the key features that correlate with tumor types through a basic statistical analysis, develop a ML model that predicts the benign and malignant nature of the tumor, and developing an ML model to classify gliomas according to the aggressiveness of tumors. The study was carried out using 1790 MRI-ADC brain image slices (980 malignant, 8 benign) from 252 human subjects including both males and females (age 2 years to 90 years) who were radiologically and histopathologically diagnosed with brain carcinoma. All MR images were acquired utilizing 3T MR systems using a head coil. ADC images of brain tumors were generated by merging b=0 and b=1000 diffusion- weighted images. Pixels within the tumor region of the generated ADC images were selected by drawing a region of interest (ROI) surrounding the tumor area. The features; i.e., mean pixel value, higher-order moments (skewness, kurtosis) of ADC, Grey Level Co-occurrence Matrix (GLCM) texture features, patient’s age, and gender corresponding to each ROI were extracted. The extracted features were tested with a one-tailed P-value hypothesis testing with a 95% confidence level by hypothesizing that there is no significant difference in mean values of extracted features among benign and malignant brain tumors. Furthermore, two ML classification models were developed using the extracted features: a classification model to differentiate benign and malignant brain tumors and a classification model to differentiate gliomas within the dataset according to the World Health Organization (WHO) glioma grading system. Development of ML model to differentiate benign and malignant image slices: The data extracted from 1549 image slices of 205 subjects with brain tumors (excluding pituitary macroadenoma and dermoid cysts from the initial dataset) was split into training (70%) and test (30%) sets. The analysis of variance (ANOVA) f-test feature selection over the train set was utilized to select the best set of features to train an ML model, and the K(10)-fold cross-validation method was utilized to find the most promising ML algorithm and corresponding hyper-parameters over the training dataset. The hyper-parameters of the algorithm were tuned using a grid search technique and the decision threshold was adjusted to have the optimum level of classification power. The performance of the tuned model for benign and malignant tumor classification was assessed using the accuracy measure over the test set. Development of ML classification model to differentiate WHO glioma grade: The glioma classification model was developed based on 1088 labeled MRI-ADC glioma brain image slices acquired from 88 human subjects. The gliomas were categorized into four groups according to the WHO glioma grading system (WHO-I, WHO-II, WHO-III, and WHO-IV). The glioma dataset was split into train and test sets with 70% to 30% proportions respectively and the best set of features to build a predictive ML model was selected by applying the ANOVA f-test over the train set. The most promising supervised learning ML algorithm for the glioma dataset was selected using the K-fold cross-validation and the hyper-parameters of the developed classification model were optimized using the grid search technique. Finally, the performance of the tuned glioma classification model was assessed using the accuracy measure over the test set. Results: According to the P-values obtained from each feature the mean pixel value of ADC and GLCM texture features i.e., mean1, mean2, variance1, variance2, energy, and contrast showed significantly (P-value < 0.05) higher feature values for benign tumors while the kurtosis and GLCM texture features i.e., entropy, homogeneity, correlation, prominence, and shade showing significantly high feature values for malignant tumors. However, facts for the features; skewness (P-value 0.05 < 0.0603), and the patient’s age (P-value 0.05 < 0.2729) were not enough to reject the null hypothesis of this study. According to the ANOVA f-test conducted over the train set of the benign and malignant dataset, the skewness expressed the minimum ANOVA f-test score (0.8731). The Random Forest Classifier (RFC) algorithm was selected to build the benign and malignant brain tumor classification model as it scored the highest mean cross-validation score (0.8899±0.0217) at the K-fold cross-validation experiment. The classification model developed in RFC was able to distinguish benign from malignant brain tumors with an accuracy of 91.16% over the test set (after the hyper-parameter tuning process). According to the ANOVA f-test, two attributes: the GLCM energy (14.21), and correlation (21.78) performed minimum scores and were excluded from the glioma dataset. Among the tested algorithms, the RFC (0.8536 ± 0.0199) obtained the highest score and was selected to build the glioma prediction model with an accuracy of 84.80% over the test set. Conclusion: The above three experiments revealed the feasibility of the utilization of texture features of MRI-ADC images for tumor classification. Therefore, this study’s outcomes enable the development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.