Role of Radiomics in the Diagnosis of Cancer

Radiomics has become a game changer in the field of cancer imaging. Because ordinary medical images can be transformed into measurable mineable information, they thus provide both diagnostic accuracy and precision to treatment planning. Radiomics automatically identifies hundreds to thousands of quantitative features that include tumor shape, texture, intensity, and spatial relationships. AI, especially machine learning and deep learning algorithms, allows automated feature selection, pattern recognition, and predictive modeling that have reliably shown better performance when compared to conventional diagnostics methods. Lung, breast, brain, liver, and pancreatic cancer case studies show remarkable diagnostic performance, with area under the curve values that commonly range above 0.90. Multi-modality radiomics methods have been proven better than single-modality methods. The clinical advantages relate to precision oncology enables, minimized invasive biopsy, reducing the cost of utilizing. However, there are issues such as variability and non-standardization in imaging protocol.