Bimali Sanjeevani Weerakoon

This study aimed to assess the normal values of the distance and angle between the superior mesenteric artery (SMA) and abdominal aorta (AA) in an asymptomatic Sri Lankan cohort using computed tomography (CT) images. A cross-sectional descriptive study was conducted at three selected government hospitals, involving 100 patients (50 males and 50 females) who underwent contrast-enhanced (CE) CT exams for gastrointestinal reasons, aged 18 to 80 years. Demographic parameters such as age, gender, height (m), and weight (kg) were recorded prior to the scanning. The mean aorto-mesenteric distance (AMD) was 14.49 mm, and the mean aorto-mesenteric angle (AMA) was 57.14°. Significant associations were observed between gender and AMD (p = 0.014) as well as gender and AMA (p = 0.018), with males exhibiting higher values. Differences in AMD (p = 0.01) and AMA (p = 0.026) were also significant across BMI groups, with higher values seen in individuals with higher BMI. No significant correlation was found between age and AMD (p = 0.827) or age and AMA (p = 0.307). This study highlights significant gender and BMI-related differences in the anatomical relationship between the SMA and AA. Age, however, does not appear to significantly impact these measurements within the studied range. Future research should explore additional demographic factors to further understand these variations.

One of the most important factors to be addressed regarding breast cancer (BC) mortality and morbidity is the identification of BC at a late stage due to a lack of awareness of early detection methods of BC. Mammography can be emphasized as a foremost vital facet in the early detection of breast cancer as a screening tool. A mini review of the literature was performed to explore the concepts of awareness of screening mammography for breast cancer under three main aspects. There, the knowledge, attitude, and practice of women in mammographic screening were focused meanwhile identifying the common barriers for women to refrain from undergoing mammographic screening for breast cancer. The studies included various categories of participants especially subcategorized under their personal history or demographic characteristics. Therefore, the correlation of these factors with their awareness level has also been focused. Further, the barriers to screening mammography encountered by women were included. Therefore, this mini-review highlights the different levels of awareness of mammographic screening among females and the common reasons for avoiding screening. It concludes the benefit of bringing the knowledge of females to a considerably positive juncture by utilizing escalating awareness programs, hence minimizing the common causes of refraining from screening mammography.

Background: 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. Methods: 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 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. Results: 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 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. Conclusion: 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.