David Raunig

Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort

While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimers disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimers DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.

Metrology Standards for Quantitative Imaging Biomarkers

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.

Scan‐rescan variability in perfusion assessment of tumors in MRI using both model and data‐derived arterial input functions

To evaluate the contribution to scan‐rescan coefficient of variation (CV) of patient‐specific arterial input function (AIF) measurement in dynamic contrast‐enhanced MRI (DCE‐MRI) data, and to determine whether any advantage or disadvantage to using a data‐derived arterial input function is related to the anatomical location of the target lesion.

Reproducibility of perfusion parameters in dynamic contrast-enhanced MRI of lung and liver tumors: Effect on estimates of patient sample size in clinical trials and on individual patient responses

OBJECTIVE Dynamic contrast-enhanced MRI (DCE-MRI) is a potentially useful noninvasive technique for assessing tissue perfusion, particularly in the context of solid tumors and targeted antiangiogenic and antivascular therapies. Our aim was to determine the reproducibility of perfusion parameters derived at DCE-MRI of tumors of the lung and liver, the most common sites of metastasis. SUBJECTS AND METHODS Patients with lung and liver tumors underwent two sequential DCE-MRI examinations 2-7 days apart without any intervening therapy. The volume transfer constant between blood plasma and the extravascular extracellular space (K(trans)) and blood-normalized initial area under the signal intensity-time curve (initial AUC(BN)) were computed with a two-compartment pharmacokinetic model. Differences in parameters were assessed with within-patient coefficients of variation. RESULTS Twenty-three patients had evaluable tumors (12 lung, 11 liver). The within-patient coefficients of variation for K(trans) and initial AUC(BN) for liver lesions were 8.9% and 9.9% and for lung lesions were 17.9% and 18.2%. Sample sizes for reductions in these parameters from 10% to 50% were estimated to range from two to 102 subjects. Estimates of confidence that changes observed in a given patient were due to intervening therapy rather than variability of the technique were calculated to range from 71% to 87% if a 20% reduction in a parameter was observed. CONCLUSION The rate of reproducibility of DCE-MRI parameters is in the range of 10%-20% and is influenced by lesion location, parameters being significantly more reproducible in the liver than in the lung. These findings provide the foundation for interpretation of results and design of clinical trials in which DCE-MRI studies are used to assess objective responses.

Volumetric MRI and MRS provide sensitive measures of Alzheimer's disease neuropathology in inducible Tau transgenic mice (rTg4510)

The purpose of this study was to determine if in vivo high resolution 3D MRI and localized (1)H MR spectroscopy (MRS) can detect brain findings resembling Alzheimers disease in a transgenic mouse model of Tau pathology. Seven double transgenic rTg4510 female mice and 7 age-matched wild-type (wt) female mice were evaluated at 5 months of age. To confirm the usefulness and consistency of in vivo MRI/S, we also scanned the brains of 14 male mice (7 rTg4510 and 7 age-matched wt) at 8 months of age. Mean hippocampal and cerebral cortex volumes in the female rTg4510 mice were 26.7% and 20.6% smaller than that in the wt controls (p<0.0001), respectively. Mean hippocampal and cerebral cortex volumes in the male rTg4510 mice were 18.4% and 16.9% smaller than that in the wt controls (p<0.00005), respectively. The mean volumes of the cerebellum were not statistically different between the rTg4510 and the wt groups. MRS assessment revealed that the myo-inositol to total creatine ratios (mIns/tCr), a measure of gliosis, were significantly higher in the hippocampus of rTg4510 mice relative to wt mice (p=0.03 for the females; p=0.005 for the males). Immunohistochemistry and histology in the same animals verified previously published data showing elevation of hyperphosphorylated Tau, glial activation and cortical and hippocampal neuronal loss. This study demonstrates that in vivo MRI/S can be a non-invasive biomarker to assess brain atrophy and related biochemical changes in the rTg4510 mouse model.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

Disease progression modeling using Hidden Markov Models

The development of novel treatments for many slowly progressing diseases, such as Alzheimers disease (AD), is dependent on the ability to monitor and detect changes in disease progression. In some diseases the distinct clinical stages of the disease progress far too slowly to enable a quick evaluation of the efficacy of a given proposed treatment. To help improve the assessment of disease progression, we propose using Hidden Markov Models (HMMs) to model, in a more granular fashion, disease progression as compared to the clinical stages of the disease. Unlike many other applications of Hidden Markov Models, we train our HMM in an unsupervised way and then evaluate how effective the model is at uncovering underlying statistical patterns in disease progression by considering HMM states as disease stages. In this study, we focus on AD and show that our model, when evaluated on the cross validation data, can identify more granular disease stages than the three currently accepted clinical stages of “Normal”, “MCI” (Mild Cognitive Impairment), and “AD”.

Robustness of automated hippocampal volumetry across magnetic resonance field strengths and repeat images

Low HCV has recently been qualified by the European Medicines Agency as a biomarker for enrichment of clinical trials in predementia stages of Alzheimers disease. For automated methods to meet the necessary regulatory requirements, it is essential they be standardized and their performance be well characterized.

Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease: A View of the Regulatory Science Qualification Landscape from the Coalition Against Major Diseases CSF Biomarker Team

Abstract Alzheimer’s disease (AD) drug development is burdened with the current requirement to conduct large, lengthy, and costly trials to overcome uncertainty in patient progression and effect size on treatment outcome measures. There is an urgent need for the discovery, development, and implementation of novel, objectively measured biomarkers for AD that would aid selection of the appropriate subpopulation of patients in clinical trials, and presumably, improve the likelihood of successfully evaluating innovative treatment options. Amyloid deposition and tau in the brain, which are most commonly assessed either in cerebrospinal fluid (CSF) or by molecular imaging, are consistently and widely accepted. Nonetheless, a clear gap still exists in the accurate identification of subjects that truly have the hallmarks of AD. The Coalition Against Major Diseases (CAMD), one of 12 consortia of the Critical Path Institute (C-Path), aims to streamline drug development for AD and related dementias by advancing regulatory approved drug development tools for clinical trials through precompetitive data sharing and adoption of consensus clinical data standards. This report focuses on the regulatory process for biomarker qualification, briefly comments on how it contrasts with approval or clearance of companion diagnostics, details the qualifications currently available to the field of AD, and highlights the current challenges facing the landscape of CSF biomarkers qualified as hallmarks of AD. Finally, it recommends actions to accelerate regulatory qualification of CSF biomarkers that would, in turn, improve the efficiency of AD therapeutic development.