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


Magnetic resonance imaging (MRI) is a potent imaging technique for determining the cause of a stroke and for brain imaging (MRI7). Another form of MRI is ultrahigh frequency-based MRI, which uses a 7 Tesla magnet and is being developed by SIEMENS for improved human imaging. These MRIs are the subject of this study. This article describes an alternative strategy called “interval monitoring,” which aims to identify tumour malignancy changes more quickly. The suggested method’s theoretical underpinnings and computer implementation are discussed, and the American Cancer Registry is used as an empirical example to illustrate how it might be used in image-based photo science. This approach is frequently used by cancer registries, which is beneficial because patient survival is the first step of cancer therapy. This is a crucial component of its care. On the other hand, conventional techniques of estimating cumulative survival only reveal changes in prognosis after a significant period of time has elapsed. After filtering and skeletonizing a series of MRI images, the GMPLS algorithm finds cancer there. This study reduces the amount of time and money needed to calculate the cancer equation. The desired matrix is generated statistically, and the matrix inverse provides us with a real-time mathematical equation that is specific to each patient. Further survivor analysis is done if the person is injured or dies. The goal of this study is to create a unique mathematical model of a cancer patient, as well as a live cancer health graph and a survivor function that forecasts death.

Author(s) Details:

Adnan Alam Khan,
DHA Suffa University, Sindh Madressatul Islam University Karachi, Pakistan.

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Keywords: Magnetic resonance imaging, American Cancer Registry, cancer patient, ultrahigh-field.

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