The advent of radiomics has opened a brand new avenue in cancer research and presents a unique opportunity to data scientists and radiologists alike. Broadly speaking, two prominent potentials have emerged for radiomics—tumour characterization and therapeutic response prediction (Fig. 1).
The search has begun to identify imaging markers to be used to assess biological parameters (i.e. genetic mutations or surface expression of particular molecules) in the tumour. Normally, such biological assessment of a tumour is achieved by biopsy—a process that is highly invasive, carries potential risk for patient morbidity, and can only elucidate information for lesions in sites easily accessible to surgeons. Radiomics provides the opportunity to non-invasively assess the biological profile (i.e. surface marker expression, genetic mutational status, blood markers, etc.) of all the lesions simultaneously and instantaneously. With the increased use of computer models to diagnose conditions and predict response to therapy, this new field where biological parameters can non-invasively be assessed using quantitative features and computer models can be termed computer-aided biological profiling (CABP).
One of the earlier studies to leverage radiomic features in the assessment of genetic mutational status (i.e. radiogenomics) was the work of Segal et al. in human liver cancer where combinations of 28 imaging traits were shown to be capable of reconstructing 78% of the global gene expression profile (i.e. mRNA levels) of these tumours . Further research ensued on a number of tumour types—with varying degrees of success.
With the rise of deep-learning-based image analysis, computer algorithms can be used to extract radiomic features on a large scale that could then be linked to predictive and prognostic biomarkers in cancer (that would otherwise be obtained surgically).
Unlike more traditional radiomics approaches where feature extraction and data analysis consisted of two separate steps, deep learning fuses these processes together and iteratively optimizes one with respect to the other. In other words, deep learning provides radiomics models with optimal features and optimal data analysis for a specific clinical problem. This advanced form of computer-aided biological profiling (where a neural network can extract features and link them together on a massive scale) can be termed deep learning mediated tumour profiling (DL-TP).
The next application of radiomic features in cancer research was prediction of response to different forms of treatment [i.e. computer-aided response prediction (CARP)]. In non-small-cell lung cancer (NSCLC), Coroller et al. identified seven features that were predictive for pathological gross residual disease and one feature for pathological complete response . Further studies later identified other radiomic features that would predict the response to conventional treatment (i.e. chemo-/radiotherapy) in bladder cancer  and locally advanced rectal cancer .