Radiomics in immuno-oncology

Abstract

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

Keywords: artificial intelligence; imaging markers; immunotherapy; precision medicine; radiogenomics; radiomics.

Figure 1

A schematic representation of different semantic features that can be extracted from medical images. The different features can be denoted in a feature vector. Feature vectors between the two lesions can differ due to changes in tumour properties.

Figure 2

An overview of the two main radiomic pipelines. The top path shows classical radiomics, with tumour delineation and manual feature engineering and extraction. After feature extraction, statistical models or machine learning can be used to link the features to the endpoint. The lower path shows deep learning radiomics, where the deep learning network extracts features without human interference to link them to the desired endpoint.

Figure 3

A schematic representation of multimodal artificial intelligence (AI) models. With the increase in available data and knowledge, a combination of different diagnostic data into one integrated diagnostic system can improve AI model outcomes. The outcomes of the integrated diagnostic models can be discussed in multidisciplinary tumour boards to optimize the treatment management plan for patients.