HJNO Jul/Aug 2024

HEALTHCARE JOURNAL OF NEW ORLEANS I  JUL / AUG 2024 63 Sotirios Stathakis, PhD, DABR, FAAPM Dr. Charles M. Smith Chief of Physics Mary Bird Perkins Cancer Center include: • Elimination of CT imaging: MRI sCT application eliminates the need for separate CT imaging, simplifying the treatment planning process and reducing the burden on patients. • Superior soft tissue visualization. • Enhanced accuracy as the synthetic images more accurately represent the patient’s anatomy compared to using CT images alone. • Uses in adaptive radiation therapy when combined with MRI linear accelerators for treatment delivery. In conclusion, AI has shown great potential in improving the accuracy and efficiency of radiation therapy. With the help of AI, physicians can achieve higher sensitivity for lesion detection, improved contouring accuracy, and increased inter- reader agreement. Additionally, AI can significantly reduce the time required for the clinical workflow, allowing for more efficient treatment and better-quality treatment plans. Overall, the integration of AI into radiation therapy has the potential to greatly improve patient outcomes. n Sotirios Stathakis, PhD, DABR, FAAPM serves as Mary Bird Perkins’ Dr. Charles M. Smith chief of physics. In this role, he oversees the overall management and oversight of the cancer center’s physics and dosimetry teams, in support of clinical, research, and educational activities. Stathakis obtained a Bachelor of Science in honors physics from the University ofWaterloo in Canada,aMaster of Science inmedical physics from theMedical Physics and Bio-Engineering Department at the University of Aberdeen, Scotland, U.K., and a PhD in medical physics from the University of Patras,Hellas Greece. imaging technology and artificial intelligence (AI) can accurately diagnose brain tumors in fewer than three minutes. This approach is able to accurately distinguish tumor tissue from healthy tissue. Artificial Intelligence has shown great potential in improving the accuracy and efficiency of brain tumor identification for stereotactic radiosurgery (SRS). With AI assistance, the inter-reader agreement can significantly increase, and algorithm- assisted physicians demonstrate a higher sensitivity for lesion detection than unassisted physicians. AI assistance can improve contouring accuracy, especially for physicians with less SRS experience, and also improved efficiency. Several studies show results that suggest that deep learning neural networks can be optimally utilized to improve accuracy and efficiency for the clinical workflow in brain tumor SRS. One of the latest applications of AI in radiation therapy is the creation of synthetic images sets. The most common example is the creation of a synthetic CT (sCT) from MRI images. MRI is a powerful imaging modality used in radiation therapy for tumor visualization and treatment planning. However, traditional treatment planning requires CT images due to its better tissue density information for dose calculations. MRI sCT application involves the generation of synthetic CT images from MRI data, allowing treatment planning to be performed directly on MRI scans. Advantages of MRI sCT application STATE OF THE ART technological advances in radiation therapy delivery systems and in computer sciences are available and utilized to precisely and accurately treat patients.The latest of these is the application of artificial intelligence (AI) as a cancer treatment. Some of the applications of AI include automated organ segmentation, tumor detection, and synthetic image generation. AIisbeingusedinorgansegmentationfor radiation therapy to improve the efficiency and accuracy of the process. Organ segmentation is a crucial, labor-intensive step in radiation oncology that can often turn into a clinical workflow bottleneck. It involves identifying the organs and tissues in diagnostic images that must be targeted or protected during radiation therapy and can take hours per patient. AI models for organ segmentation are being developed to automate this process andmake it faster and more accurate. Studies show that AI- based contouring significantly reduces the time required for organ segmentation compared to manual contouring and has a high level of accuracy. AI-based contouring can also reduce inter-observer variability in organ segmentation and improve the consistency of target volumes among radiation oncologists. The speed, accuracy, and standardization of AI organ segmentation can lead to a higher patient throughput and higher-quality treatment plans, hence better patient outcomes. Another application of AI is that it is used to identify tumors in the brain. For example, a process combining advanced