Advances in Imaging Biomarkers for Oncology: A Comprehensive Review of Techniques, Clinical Applications and Future Perspectives

Abstract

Imaging biomarkers are important for modern oncology as it provide quantitative ways to assess tumour functioning, monitoring, progression, and evaluate response to treatment. There are several methods are available for diagnosis of tumour such as CT and MRI based techniques; biopsies are the traditional methods. However, cancer cases are increases and led to more burden on healthcare settings. Tumour imaging biomarkers (IBs) can play an important role in early diagnosis of cancer. IBs analysis includes structural characteristics such as the size of the tumour as well as functional (e.g., diffusion-weighted MRI, perfusion, and dynamic contrast-enhanced) as well as metabolic characteristics based on standardized uptake values from a Positron Emission Tomography (PET) scan. Radiomics (the creation of large volumes of image data) and artificial intelligence (AI)-based imaging biomarkers will also be discussed as potential sources of data that can be used to predict patient outcomes based on advanced IBs. This narrative review will describe in detail the range of structural, functional, and molecular & metabolic IBs that are being utilized in oncology today. Also, highlight the development of radiomics and artificial intelligence-based IBs. Tumour detection, classification, creating treatment plans, determining the outcome of a treatment, and monitoring for any signs of a tumour returning are just some of the many clinical applications of imaging and metabolic biomarkers. Functional and metabolic imaging can detect therapeutic responses at an earlier point in time than will show on an anatomic image because functional and metabolic imaging captures biological changes before any change occurs in anatomy. The clinical translation of these biomarkers is significantly affected by issues regarding reproducibility, standardization, and multi-centre validation. The integration of IBs into everyday practice is further complicated by variations in imaging processes and analytical techniques. The future of IBs lies in the integration of advanced imaging technologies, computational analytics, and molecular data, which together may enable earlier detection, more accurate prognostication, and personalized cancer therapy.