Quantitative Imaging Methods for Individualized Cancer Treatment

Abstract

Author(s): Bhuneshwari Dewangan, Hemlata Dewangan, Ragini Patel

Quantitative Imaging Methods (QIM) have emerged as a foundation of personalised cancer treatment, bringing in a new era in oncology. These approaches are of critical value in cancer care because they yield accurate, data-driven insights into the specific disease profile of each individual patient. QIM's value comes in the fact that it can shed light on cancer's complicated environment in a way that is both precise and grounded in statistics. Accurate tumor characterisation is made possible by QIM through the use of innovative imaging and computational methods. Standardization, data integration, and computing complexity are few of the hurdles that must be overcome before QIM may be widely used in clinical practice. This research proposes a framework called the Dynamic Functional Radiogenomics Integration Framework (DFRIF) to improve treatment planning by more precisely describing tumor morphology, heterogeneity, and response to therapy. QIM paves the way for individualized treatment plans, which reduce the likelihood of unwanted side effects while increasing the therapeutic benefit. Additionally, QIM can be used for a comprehensive method of cancer management, including prognostic modeling, non-invasive monitoring, and early cancer identification. The uses of QIM are many, ranging from prognostic modeling and non-invasive monitoring to early cancer diagnosis. QIM's effect on customer satisfaction, scheduling of resources, and affordability can be assessed using simulation analyses, which can then be used to inform healthcare practitioners and policymakers. By leveraging the potential of data integration and computer analysis, these strategies help physicians gain a better understanding of cancer, which in turn allows them to give patients with more targeted, efficient treatment.

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