Integrating Oncology, Radiology, and Nuclear Medicine into Theranostics

Abstract

Author(s): Yogendra Sahu, Naina Bhoyar, Krishna Sahu

Theranostics is a combination of therapy and diagnostics, with the goal of individualizing cancer treatment depending on the features of each patient. Combining the fields of oncology, radiology, and nuclear medicine allows for more accurate diagnoses and quicker access to tailored treatments. This strategy improves patient outcomes by increasing therapy efficacy and decreasing adverse effects. Data interoperability, image standards, multidisciplinary cooperation, and legal considerations are a few of the obstacles to the successful integration of various fields. Solving these problems is crucial for theranostics to reach its full potential. This research proposes the creation of an integrated platform that may incorporate Oncology, Radiology, and Nuclear Medicine data, and this framework is called the Multi-Modal Radiogenomics Imaging Integration Framework (M-MRIIF). Using sophisticated machine learning and artificial intelligence techniques, this system can glean useful information from medical photos and patient files. Moreover, the accuracy of treatment is improved by the use of radiolabeled tracers for tumor targeting (RT-TT). Theranostics' incorporation of oncology, radiology, and nuclear medicine has numerous practical applications. Among these include the ability to diagnose cancer at an early stage, track a patient's reaction to treatment, personalize therapy, and forecast the patient's prognosis. Patient outcomes, resource allocation, and cost-effectiveness can all be assessed by running a series of simulations. Informed judgments about theranostics' incorporation into clinical practice can be made by healthcare providers and policymakers with the help of these simulations

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