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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.
RESUMO
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.
