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Background This study delves into the demographics and clinical characteristics of oral cavity tumors in the context of the United Arab Emirates. It further investigates the efficacy of four different treatment modalities in impacting patient survival rates. It aims to understand if any treatments significantly improve survival compared to others. Methodology To assess the survival outcomes across the different treatment groups, the study employed the log-rank test, a non-parametric statistical test widely used in survival analysis. The sample consisted of patients from the electronic medical records assigned to one of the following four treatment groups: radiotherapy only (RT), radiotherapy with surgery and chemotherapy (RT+S+C), radiotherapy with surgery (RT+S), and, finally, radiotherapy with chemotherapy including immunotherapy (RT+C). Data collection involved tracking survival times from the initiation of treatment until the last follow-up period or the occurrence of an event (e.g., death). The statistical analysis was conducted using the chi-squared statistic to determine the distribution of survival times across the groups, providing a quantitative measure of the difference between the observed and expected survival. The Kaplan-Meier curve was plotted for the cohort divided into four groups. Results The log-rank test yielded a p-value of 0.321019, suggesting no statistically significant difference in survival among the treatment groups at the 5% significance level. The chi-squared statistic was 3.498018, within the 95% acceptance region, further corroborating the null hypothesis of no significant survival difference across the groups. Despite this, an observed medium effect size of 0.59 indicates a moderate difference in survival between the groups. Conclusions The findings illustrate that while there is no statistically significant difference in survival rates among the four treatment groups, the medium effect size observed suggests a moderate difference in survival. This emphasizes the need to consider the statistical significance and effect size in clinical research, as they provide different insights into treatment efficacy.
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Objective This study aims to thoroughly assess the radiation dose distribution to critical organs in patients with nasopharyngeal carcinoma, focusing on the correlation between the radiation dosages for the various organs at risk (OARs) in nasopharyngeal cancer patients. Methods We meticulously analysed a dataset comprising 38 nasopharyngeal carcinoma patients, focusing on radiation dosages measured in Gray (Gy) and volumetric data in cubic centimetres (cc) of critical organs, including the lens, brainstem, spinal cord, optic nerve, optic chiasm, and cochlea. A detailed exploratory data analysis approach encompassed univariate, bivariate, and multivariate techniques. Results Our analysis revealed several key findings. The mean and median values across various dose measurements were closely aligned, indicating symmetrical distributions with minimal skewness. The histograms further corroborated this, showing evenly distributed dose values across different anatomical regions. The correlation matrix highlighted varying degrees of interrelationships between the doses, with some showing strong correlations while others exhibited minimal or no correlation. The 3D scatter plot provided a view of the multi-dimensional dose relationships, with a specific focus on the spinal cord, lens, and brainstem doses. The bivariate scatter plots revealed symmetrical distributions between the right and left lens doses and more complex relationships involving the brainstem and spinal cord, illustrating the intricacies of dose distribution in radiation therapy. Conclusion Our findings reveal distinct radiation exposure patterns to OARs of nasopharyngeal carcinoma. This research emphasises the need for tailored radiation therapy planning to achieve optimal clinical outcomes while safeguarding vital organs.
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Introduction Artificial intelligence (AI) is transforming healthcare, particularly in radiation oncology. AI-based contouring tools like Limbus are designed to delineate Organs at Risk (OAR) and Target Volumes quickly. This study evaluates the accuracy and efficiency of AI contouring compared to human radiation oncologists and the ability of professionals to differentiate between AI-generated and human-generated contours. Methods At a recent AI conference in Abu Dhabi, a blind comparative analysis was performed to assess AI's performance in radiation oncology. Participants included four human radiation oncologists and the Limbus® AI software. They contoured specific regions from CT scans of a breast cancer patient. The audience, consisting of healthcare professionals and AI experts, was challenged to identify the AI-generated contours. The exercise was repeated twice to observe any learning effects. Time taken for contouring and audience identification accuracy were recorded. Results Initially, only 28% of the audience correctly identified the AI contours, which slightly increased to 31% in the second attempt. This indicated a difficulty in distinguishing between AI and human expertise. The AI completed contouring in up to 60 seconds, significantly faster than the human average of 8 minutes. Discussion The results indicate that AI can perform radiation contouring comparably to human oncologists but much faster. The challenge faced by professionals in identifying AI versus human contours highlights AI's advanced capabilities in medical tasks. Conclusion AI shows promise in enhancing radiation oncology workflow by reducing contouring time without quality compromise. Further research is needed to confirm AI contouring's clinical efficacy and its integration into routine practice.
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Background Endometrial carcinoma (EC) remains a pressing global health issue, with a discernible upsurge in incidence, especially in developed countries. Notably, the United Arab Emirates (UAE) has witnessed a surge in EC cases, demanding an in-depth, region-specific exploration into the disease's clinical, treatment, and prognostic facets against the backdrop of its unique socio-genetic and environmental contours. Aim This study aimed to profess a comprehensive understanding of EC by examining clinical parameters, treatment modalities, and prognostic outcomes in the UAE context, thereby seeking to delineate potential correlations between varied therapeutic combinations, patient demographics, and tumor characteristics in affecting prognostic outcomes. Materials and methods A retrospective cohort study involving 93 patients diagnosed with EC from January 2011 to March 2023 at a leading oncology center in the UAE was conducted. Data, including demographic information, clinical presentation, treatment modalities, and prognostic outcomes, were meticulously extracted and analyzed. The R software (version 4.2.2) facilitated exhaustive statistical analyses, involving descriptive statistics, correlation analyses with the polycor package, and survival analyses utilizing the Kaplan-Meier method and Cox regression analysis via the survival and survminer packages, respectively. Results Although the correlation matrix revealed a noticeable relationship between "Family history" and "Age," most parameters displayed independence, offering a robust platform for ensuing multivariate analyses. Kaplan-Meier survival curves, stratified by therapeutic modalities, exhibited no statistically significant survival differences across therapeutic cohorts (p-values: 0.44, 0.86, and 0.83). Conversely, the composite Cox regression model underscored "non-national" demographic, Diabetes Mellitus II, and stromal invasion as pivotal prognostic factors, indicating the multifactorial nature of survival in EC patients and emphasizing demographic and tumor characteristics over therapeutic modalities as influential prognostic determinants. Conclusion In conclusion, while therapy types were not directly correlated with survival, demographic and tumor traits prominently impacted prognostic outcomes, advocating for an intricate, multidimensional approach to managing EC in the UAE. This study hopes to sow seeds for subsequent research, shaping clinically and culturally apt practices and policies in the region's healthcare landscape.
