Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Abstract

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

“…The quality of radiomics studies has recently improved thanks to community-wide efforts to explore and reduce variability in medical imaging and to promote the translation of quantitative imaging biomarkers into clinical practice and clinical trials ( 24 – 27 ) ( Figure 2 ). Figures 2A, B are drawn based on a research team’s recent literature search for the CT-based radiomics studies in lung cancer ( 6 ), which we supplemented with studies published as of July, 2020 as well as information about imaging parameters (slice thickness, reconstruction kernel) and segmentation (inter-/intra-variability, software, result supervised or not) ( Table 1 in Supplementary Material ).…”

Latest CT technologies in lung cancer screening: protocols and radiation dose reduction

“…Continuous efforts are made by the European Imaging Biomarker Alliance (EIBALL) and Quantitative Imaging Biomarker Alliance (QIBA) to streamline the use and validation of imaging biomarkers like lung nodule quantification in lung cancer CT screening. The goal of these subcommittees of the ESR and RSNA is international standardization and harmonization of data acquisition (CT protocol) and analysis (quantification of lung nodules) (30,48). In particular, the QIBA provides some standards for image quality assurance, rather than specified scan parameter values for LDCT.…”

Quantitative distribution of iodinated contrast media in body computed tomography: data from a large reference cohort

“…Such recommendations should be defined by professional societies in close cooperation with hardware and software providers. Similar efforts have been successfully made by the Quantitative Imaging Biomarkers Alliance and European Imaging Biomarkers Alliance with regard to different imaging techniques; however, quantitative maps from DECT have not found representation in these to date [ 33 , 34 ].…”