A roadmap for the clinical implementation of optical-imaging biomarkers

Abstract

Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrett's oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical imaging-technologies. Optical-imaging biomarkers (OIBs), which rely on the interactions of tissue and non-ionizing optical radiation (with typical wavelengths in the range of 400-1,000 nm), can be used for the non-invasive detection and characterization of disease states. OIBs enable the real-time analysis of tissue biochemistry and the use of compact point-of-care and low-cost imaging devices (when compared to radiological imaging), and can operate across ranges of resolutions and depths spanning over four orders of magnitude 1. Across the visible and near-infrared spectrum, light undergoes a range of complex interactions with tissue (Fig. 1). Conventional photographic methods that aim at replicating human vision 2 discard most of the information obtained from these interactions and only capture reflected light across three channels (red, green and blue). Over the past decade, a wide range of promising OIBs that extract in-depth information provided by the different light-tissue interactions have emerged. However, for any new imaging biomarker to be deployed in a clinical setting, detailed validation is required. Technical validation defines the precision and accuracy with which the biomarker can be measured, whereas biological validation establishes the association between the biomarker and the underlying physiological, anatomical or pathological process. Clinical validation can then establish whether the biomarker does indeed identify, measure or predict the clinical outcome of interest. To achieve clinical validation, the imaging device needs to conform to clinical performance and safety specifications, and be approved for use in patients. With standard radiological imaging-such as computed tomography (CT) or magnetic resonance imaging (MRI)-the imaging device required to measure a novel imaging biomarker is already clinically approved for use in humans and widely available across radiology departments 3. In contrast, for OIBs it is uncommon that a clinically approved imaging device (alongside its associated specialist data-acquisition and data-interpretation methods) is available for clinical validation. Therefore, biological validation may be restricted to testing ex vivo samples such as histopathological sections. Compared to the in vivo setting, these can be prone to bias, and generate a different range of optic...

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

“…Machine (ML) and deep learning (DL) algorithms are used to automatically extract and analyze histogram, texture, and shape information from imaging data which may not be evident to the naked eye. Given the limitations of conventional medical imaging, there is increased interest to apply radiomics in oncological imaging as a tool to obtain diagnostic, predictive, and prognostic information from routine clinical imaging [16]. However, despite its extensive use in research and favorable results linking CT/MRI texture features to renal mass characterization, the routine use of radiomics in clinical practice is yet to be seen.…”

Photoacoustic imaging as a tool to probe the tumour microenvironment

“…The studies reviewed here indicate that PAI can inform on TME features in preclinical models and holds promise for clinical translation. Currently, further technical and biological validation of PAI biomarkers is needed to increase uptake of the modality in studies of TME biology or clinical evaluation of TME features (Waterhouse et al, 2019). In terms of technical validation, some studies have reported on standardisation of data acquisition and analysis (Abeyakoon et al, 2018; Bohndiek et al, 2013; Joseph et al, 2017; Martinho Costa et al, 2018; Neuschmelting et al, 2016).…”

3D microarchitecture of the human tuberculous granuloma

“…Secondly, scouting for pathological features may help guide and expedite 323 histopathological follow-up studies. Thirdly, digitized 3D image libraries of tissue and organs from 324 TB patients could be used to identify novel imaging biomarkers based on patterns of differential 325 radio-opacities 42 (Table 1). 347 348…”