Radiomics: a critical step towards integrated healthcare

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).

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 [3]. 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 [4]. Further studies later identified other radiomic features that would predict the response to conventional treatment (i.e. chemo-/radiotherapy) in bladder cancer [5] and locally advanced rectal cancer [6].

While the suffix “-omics” has come to denote the idea of extracting valuable information from data sets, radiomics is only the latest addition to the ever-growing list of new fields of study within the fusion of advanced technology and modern medicine. Images derived from tissue (e.g. general microscopy, immunohistochemistry, etc.) have also been subject to quantitative analysis and new information is being generated beyond what would be observed by a pathologist, i.e. pathomics. Genomics, the branch of molecular biology concerned with the mapping of the human genome, has helped to identify many genetic mutations and pathways that have been used for prognostication or as novel targets for modern therapeutics and has heavily relied on computer models developed by skilled bioinformaticians.

As it currently stands, different medical disciplines have developed different stratification methods, primarily based on their own field (i.e. radiological classifications, pathological and chemical laboratory classifications, clinical checklists used for prognostication, etc.)—quite often to the exclusion of other departments. As these traditional scoring systems were often based on subjective interpretations of analogue readouts, combining these disparate outputs is quite challenging. The rise of the quantitative aspects of various medical disciples (i.e. the “-omics”) presents a remarkably unique opportunity wherein information from different diagnostic modalities can be objectively integrated.

The long-term vision for precision medicine should focus on the development of integration strategies, wherein data derived from the patients themselves can be used to guide the treating physician. Through intricate analyses that integrate clinical data, blood markers, pathomics, radiomics, and genomics, we envision that a patient can be provisionally diagnosed (via computer-aided diagnosis), stratified into a molecular subtype of their tumour (via computer-aided biological profiling), and have a recommended treatment formulated (via computer-aided response prediction). This aggregation of sub-systems cooperating with the aim of achieving an overarching functionality (in this case‚ better CAD, CARP, and CABP) is termed the integration system (see Fig. 2). This will require hand-in-hand multidisciplinary collaboration between the biomedical field (i.e. clinicians, geneticists, radiologists, pathologists, clinical chemists) and the technical field (i.e. computer scientists, physicists, engineers, statisticians, and mathematicians) as well as an organizational structure wherein the departments will operate not in isolation but in successful integration.