Abstract
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous ‘manual’ clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research – mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.
Highlights
Precision medicine has become a key focus of modern bioscience and medicine, and involves “prevention and treatment strategies that take individual variability into account”, through the use of “large-scale biologic databases ... powerful methods for characterizing patients ... and computational tools for analysing large sets of data”[1]
These biomarkers should be and cheaply obtained, objective and reproducible. Such biomarkers will be increasingly important for so-called “phenome-wide association studies”[30]. With these goals in mind, we propose that images derived from routine radiological testing have been largely ignored in the context of precision medicine, and motivate the use of powerful new machine learning techniques applied to radiological images as the basis for novel and useful biomarker discovery
We note that the human-defined feature method is currently synonymous with the term “radiomics” in some of the relevant literature, we suggest that a broader definition of the field is more useful than a narrow focus on a single method
Summary
Precision medicine has become a key focus of modern bioscience and medicine, and involves “prevention and treatment strategies that take individual variability into account”, through the use of “large-scale biologic databases ... powerful methods for characterizing patients ... and computational tools for analysing large sets of data”[1]. It has become clear that physical biomarkers more proximal to the outcome of ultimate interest (usually clinical morbidity or mortality) are needed in addition to genomic markers. To date, such biomarkers have proven difficult to capture and analyse for precision medicine purposes. Environmental exposures are difficult to quantify because they occur outside of the medical context, accumulating and changing throughout life[10]. Aggregated, large-scale clinical data can be analysed to identify factors (biomarkers) that correspond to important variations in health in a similar way to that which already occurs in genomics[14]. Current national and international efforts seek to expand these databases to include other “-omic” information, such as proteomic and microbiomic data[1, 15,16,17,18,19,20,21] (the high-throughput analysis of endogenous body proteins and commensal organisms respectively)
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