Multidisciplinary Cancer Investigation

en Discriminating Malignant and Benign Brain Tumors Using Texture Features Of MRI-ADC Images Original/Research Article <div style="text-align: justify;">Introduction: The diagnosis of brain tumors often involves the use of Magnetic Resonance Imaging (MRI), with the Apparent Diffusion Coefficient (ADC) being a commonly employed technique in current clinical practice. This study seeks to investigate the potential of using statistical texture analysis of MRI-ADC images to distinguish between malignant and benign brain tumors. Methods: The research utilized 980 MRI brain ADC image slices labeled as malignant and 805 labeled as benign from 252 subjects. The clinical diagnosis of each participant was verified by histopathological and radiological reports. The region of interest (ROI) was defined to extract ADC values within the tumor areas. From each ROI, statistical features including higher-order moments of ADC, mean pixel value, and texture features of Grey Level Co-occurrence Matrix (GLCM) were extracted along with patient demographic information. The mean feature values for each category were computed and analyzed using a one-tailed P value test at a 95% confidence level. Results: The average pixel value of ADC, as well as the GLCM texture features (Variance 1, Variance 2, Mean 1, Mean 2, Contrast, and Energy), were found to be significantly higher (P<0.05) for benign tumors. In Contrast, malignant tumors exhibited significantly higher values for kurtosis of ADC and GLCM texture features (Entropy, Homogeneity, and Correlation). The patient's age and other features (skewness of ADC, GLCM texture features such as Shade, Entropy, and Prominence) did not provide sufficient evidence to reject the null hypothesis (P>0.05). Conclusions: In conclusion, the aforementioned features, with the exception of the patient's age, skewness, and GLCM features such as Entropy, Shade, and Prominence can be used as potential biomarkers for distinguishing between benign and malignant brain tumors.</div> Magnetic Resonance Imaging, Malignant Brain Neoplasm, Benign Brain Neoplasm, Diffusion Weighted MRI 17 26 http://mcijournal.com/browse.php?a_code=A-10-630-1&slc_lang=en&sid=1 Sahan Vijithananda sahan.vijithananda@gmail.com 10031947532846003754 10031947532846003754 No Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka Mohan Jayatilake jayatiml@pdn.ac.lk 10031947532846003755 10031947532846003755 Yes Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka Teresa Goncalves tcg@uevora.pt 10031947532846003756 10031947532846003756 No Department of Informatics, School of Science and Technology, University of Evora, Evora, Portugal Luis Rato imr@uevora.pt 10031947532846003757 10031947532846003757 No Department of Informatics, School of Science and Technology, University of Evora, Evora, Portugal Bimali Weerakoon bsw888@ahs.pdn.ac.lk 10031947532846003758 10031947532846003758 No Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka Tharindu D Kalupahana deshankalupahana@sjp.ac.lk 10031947532846003759 10031947532846003759 No Department of Computer Engineering, Faculty of Engineering, University of Sri Jayewardenepura, Ratmalana, Sri Lanka Anil De Silva desilva.y@gmail.com 10031947532846003760 10031947532846003760 No Department of Radiology, National Hospital of Sri Lanka, Colombo, Sri Lanka Karuna Dissanayake karunadissanayake@gmail.com 10031947532846003761 10031947532846003761 No Department of Histopathology, National Hospital of Sri Lanka, Colombo, Sri Lanka Badra Hewavithana badhra.hewavithana@med.pdn.ac.lk 10031947532846003762 10031947532846003762 No Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka