Survival Analysis of Tumor using 7 Tesla Magnetic Resonance Imaging (MRI): A Statistical Approach

A powerful imaging approach for detecting the origin of a stroke and brain imaging is magnetic resonance imaging (MRI7). Ultrahigh frequency-based MRI, which employs a 7 Tesla magnet and is being developed by SIEMENS for enhanced human imaging, is another type of MRI. This research looks into these MRIs. This article presents a different approach known as "interval monitoring," which tries to detect tumor malignancy changes faster. The conceptual background and computer implementation of the proposed method are presented, and its application is demonstrated using an empirical example from image-based photo science, the American Cancer Registry. Because patient survival is the initial stage in cancer treatment, many cancer registries employ this strategy on a regular basis, which is a good thing. This is an important aspect of its treatment. Traditional methods of calculating cumulative survival, on the other hand, indicate changes in prognosis only after a large amount of time has passed. The GMPLS algorithm locates cancer in a sequence of MRI images after filtering and skeletonization. When calculating the cancer equation, this research saves time and money. A statistical approach is used to produce the desired matrix, and the matrix inverse offers us a real-time mathematical equation that is unique for each patient. If the person has an injury or died, further survivor analysis is performed. This research aims to develop a one-of-a-kind mathematical model of a cancer patient, as well as a real-time graph of cancer health and a survivor function that predicts death.