The Role of Radiomics in Precision Oncology: A Comprehensive Review
DOI:
https://doi.org/10.63278/jicrcr.vi.502Keywords:
Radiomics, Precision Oncology, Imaging, Artificial Intelligence, Cancer Treatment, Machine Learning, Deep Learning, Imaging Biomarkers, Tumor Response.Abstract
Background: Cancer, a genetic disorder caused by uncontrolled cell proliferation, is a leading cause of death globally. Advances in precision oncology aim to tailor treatments based on individual tumor characteristics, enhancing treatment efficacy and minimizing side effects. Radiomics, the extraction of quantitative data from medical imaging, has emerged as a valuable tool in cancer diagnosis, treatment planning, and monitoring. The integration of radiomics with artificial intelligence (AI) promises to further personalize cancer treatment strategies.
Aim: This review aims to explore the role of radiomics in precision oncology, highlighting its applications, methodologies, and potential to improve cancer management.
Methods: The review examines the role of various imaging techniques in oncology, including traditional methods like X-rays and advanced technologies such as MRI, CT, PET/CT, and MRI. It discusses how radiomics builds on these techniques to extract quantifiable features for predictive modeling, emphasizing the potential of AI and big data in analyzing these features. The review also addresses the integration of AI in radiomics for enhancing cancer treatment decisions.
Results: Radiomics provides a more comprehensive analysis than traditional imaging by extracting multiple quantitative features, such as shape, texture, and intensity. AI-driven models using these features can predict treatment outcomes, assess tumor response, and identify potential recurrence. The review discusses AI techniques, including machine learning (ML) and deep learning (DL), and their applications in improving cancer treatment precision.
Conclusion: The integration of radiomics with AI in precision oncology holds significant promise for improving cancer diagnosis, treatment, and monitoring. However, challenges such as variability in imaging protocols and reproducibility need to be addressed for radiomics to be more widely applicable in clinical settings.
