Innovative Radiology and Nuclear Medicine Approaches for Mapping and Exploring Tumor Landscapes

Abstract

Author(s): Pranjali Verma, Monika Barsagade, Birendra Kumar Sahu

In the field of cancer research and treatment, innovative radiology and nuclear medicine techniques have emerged as effective tools for deciphering complex tumor landscapes. Advanced imaging techniques are required due to the complexity of tumor microenvironments and the various biological processes occurring there. Through the integration of radiology and nuclear medicine, clinicians can gain insight into tumor form, metabolism, and molecular indicators. Tackling technical challenges, data fusion issues, and interpretation challenges is essential for putting these novel methods into practice. Harmonization of Acquisition Techniques (HAT) and computational methodologies is required when combining multiple imaging modalities. Furthermore, it is a significant problem to properly translate the gathered information into relevant therapeutic insights. To achieve accurate tumor mapping, a method called Hybrid Multimodal IMAGING Machine Learning-based Integration Platform (HMIML-IP) is developed to synchronize multimodal data. The HMIML-IP combines modern imaging approaches from radiology and nuclear medicine, such as dynamic contrast-enhanced MRI and functional diffusion-weighted MRI, with Positron Emission Tomography (PET) of specific molecular markers. HMIML-IP has many potential uses in the field of oncology. It is useful for determining the heterogeneity of tumors, deciding where to take biopsies, and monitoring where therapeutic targets are located. Clinical decision-making can be affected by HMIML-IP ability to improve early detection of treatment resistance and to facilitate the identification of potential metastatic locations. The capabilities of HMIML-IP are demonstrated through simulation analyses using simulated tumor scenarios. HMIML-IP has been shown to improve diagnostic precision and prognostic insights in comparison to traditional single-modal imaging for a variety of tumor types

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