Comparative analytical performance of multiple plasma Aß42 and Aß40 assays and their ability to predict positron emission tomography amyloid positivity.

INTRODUCTION: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aß) peptides in plasma and the extent to which they improve the prediction of amyloid positivity. METHODS: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aß assays. Statistical tests were performed to determine whether the plasma Aß measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype. RESULTS: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons). DISCUSSION: Measurement of Aß in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study. HIGHLIGHTS: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aß) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aß42/40 predicted amyloid positron emission tomography status better than Aß42 or Aß40 alone.

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.

Head-to-head comparison of leading blood tests for Alzheimer's disease pathology.

INTRODUCTION: Blood tests have the potential to improve the accuracy of Alzheimer disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. METHODS: Plasma samples from the Alzheimers Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. RESULTS: Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. DISCUSSION: This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements.

The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.

Radiologists and Clinical Trials: Part 2: Practical Statistical Methods for Understanding and Monitoring Independent Reader Performance.

Though many clinical trials rely on medical image evaluations for primary or key secondary endpoints, the methods to monitor reader performance are all too often mired in the legacy use of adjudication rates. If misused, this simple metric can be misleading and sometimes entirely contradictory. Furthermore, attempts to overcome the limitations of adjudication rates using de novo or ad hoc methods often ignore well-established research conducted over the last half-century and can lead to inaccurate conclusions or variable interpretations. Underperforming readers can be missed, expert readers retrained, or worse, replaced. This paper aims to standardize reader performance evaluations using proven statistical methods. Additionally, these methods will describe how to discriminate between scenarios of concern and normal medical interpretation variability. Statistical methods are provided for inter-reader and intra-reader variability and bias, including the adjudicator's bias. Finally, we have compiled guidelines for calculating correct sample sizes, considerations for intra-reader memory recall, and applying alternative designs for independent readers.

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.

Pixel compounding: resolution-enhanced ultrasound imaging for quantitative analysis.

Accurate measurement of structural features represented in medical images is important in clinical trials and patient diagnosis. A key factor for precision is spatial resolution, which in ultrasonic imaging is limited by transducer array arrangements, transmitting frequency, and data acquisition firmware. In this paper, a variation of pixel compounding is proposed to enhance ultrasound resolution using acquired cine loops. The technique operates on a sequence of ultrasound B-scan images acquired with random motion. Subpixel registration is estimated and a maximum a posteriori (MAP) approach with the shift information is used to reconstruct a high-resolution single image. A nonhomogeneous anisotropic diffusion algorithm follows from the estimation process and is implemented to enhance the high-resolution edges. Preliminary tests using simulations and phantom studies show promising results. Pixel compounding can be a powerful preprocessing tool to assure accurate segmentation, measurement, and analysis of ultrasound images.

Comparisons between electromagnetic and X-beam transit-time flow measurements for evaluating drug actions on cardiac output in the conscious dog.

INTRODUCTION: Cardiac output remains an important preclinical measurement for evaluating the cardiovascular effects of drugs. We evaluated the performance of the Triton Active Redirection Transit-Time, ART(2), which represents a new class of X-beam flow systems and compared it in vivo and in vitro to an electromagnetic flow (EMF) system for measuring large vessel flow. METHODS: In vivo, simultaneous aortic flow measurements were obtained during alpha- and beta-adrenergic receptor stimulation in 5 conscious dogs instrumented with both ART(2) and EMF probes on their ascending aortas. In vitro, simultaneous measurements of volume flow using the ART(2), EMF, and timed-volume collection were made using a novel benchtop flow apparatus that ensured probe alignment and precise timed-volume flow measurements. Accuracy and sensitivity of both systems were assessed by recording flow measurements while varying rates, temperature and hematocrit. RESULTS: In vivo aortic flow measurements between ART(2) and EMF were closely correlated (linear regression r(2) values ranged from 0.84 to 0.99), with the ART(2) system recording lower flow values than the EMF. In vitro ART(2) flow rates were in excellent agreement with timed-volume flow, while EMF flow rates were lower (p<0.05) and exhibited more variation and dependency upon temperature or hematocrit than the ART(2). Saline flows measured by ART(2) and EMF averaged 97+/-2% and 91+/-5% accuracy, respectively, over the temperature range 32 degrees C to 42 degrees C. For blood hematocrit values between 35% and 45%, ART(2) accuracy averaged 98+/-2%, compared to 89+/-5% accuracy with the EMF. DISCUSSION: The ART(2) flow measurements in conscious dogs correlated closely to concurrent measurements obtained with the EMF over a wide range of flow rates, even though the absolute aortic flow values differed. Since it accurately measured flow in vitro, the ART(2) system is an appropriate alternative for evaluating cardiovascular effects of disease progression or drug administration in experimental animals.

Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord.

Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques, it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion and its directionality, both techniques are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption neglects pathological processes that are not Gaussian therefore reducing the amount of potentially clinically relevant information. Additions to the Gaussian distribution measured by the excess kurtosis, or peakedness, of the probabilistic model provide a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can offer additional information about the micromolecular environment of the pediatric spinal cord. This is accomplished by a more thorough characterization of the nature of random water displacement within the cord. A novel DKI imaging sequence based on a tilted 2D spatially selective radio frequency pulse providing reduced field of view (FOV) imaging was developed, implemented, and optimized on a 3 Tesla MRI scanner, and tested on pediatric subjects (healthy subjects: 15; patients with spinal cord injury (SCI):5). Software was developed and validated for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in mean kurtosis (p < 0.01) and radial kurtosis (p < 0.01) between healthy subjects and subjects with SCI. DKI provides incremental and novel information over conventional diffusion acquisitions when coupled with higher order estimation algorithms.

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.

Differential effect of HERG blocking agents on cardiac electrical alternans in the guinea pig.

Beat-to-beat alternations of the cardiac monophasic action potential, known as electrical alternans, were studied at drug concentrations that have known arrhythmogenic outcomes. Electrical alternans were elicited from the heart of anesthetized guinea pigs, both in the absence and presence of drugs that inhibit the delayed rectifier K(+) channel encoded by the human ether a-go-go related-gene (HERG), and are associated with the fatal arrhythmia, Torsade de Pointes. Two other HERG inhibiting drugs not associated with Torsade de Pointes were also studied. At concentrations known to be proarrhythmic, E-4031 and bepridil increased mean alternans 10 and 40 ms at pacing frequencies

Dynamic beat-to-beat modeling of the QT-RR interval relationship: analysis of QT prolongation during alterations of autonomic state versus human ether a-go-go-related gene inhibition.

Methods to correct the QT interval for heart rate are often in disagreement and may be further confounded by changes in autonomic state. This can be problematic when trying to distinguish the changes in QT interval by either drug-induced delayed repolarization or from autonomic-mediated physiological responses. Assessment of the canine dynamic QT-RR interval relationship was visualized by novel programming of the dynamic beat-to-beat confluence of data or "clouds". To represent the nonuniformity of the clouds, a bootstrap sampling method that computes the mathematical center of the uncorrected beat-to-beat QT value (QTbtb) with upper 95% confidence bounds was adopted and compared with corrected QT (QTc) using standard correction factors. Nitroprusside-induced reflex tachycardia reduced QTbtb by 43 ms, whereas an increase of 55 and 16 ms was obtained using the Bazett (QTcB) and Fridericia (QTcF) formulae, respectively. Phenylephrine-induced reflex bradycardia increased QTbtb by 3 ms but decreased QTcB by 20 ms and QTcF by 12 ms. Delayed repolarization with E-4031 (1-[2-(6-methyl-2-pyridyl)ethyl]-4-methylsulfonylaminobenzoyl)-piperidine), an inhibitor of rectifier potassium current, increased QTbtb by 26 ms but QT prolongation calculations using QTcF and QTcB were between 12 and 52% less, respectively, when small decreases in heart rate (5-8 beats per minute) were apparent. Dynamic assessment of beat-to-beat data, using the bootstrap method, allows quantification of QT interval changes under varying conditions of heart rate, autonomic tone, and direct repolarization that may not be distinguishable with use of standard correction factors.

The relationship of clinical QT prolongation to outcome in the conscious dog using a beat-to-beat QT-RR interval assessment.

QT interval prolongation of the electrocardiogram has been associated with the occurrence of life-threatening fatal ventricular arrhythmias. To understand the relationship between preclinical cardiac conduction assessment to clinical outcome, comparisons of free (unbound)-plasma drug concentrations and their associated effects in the conscious mongrel dog were made to the free plasma concentrations in humans reported to produce QT prolongation. E-4031 (an experimental class III antiarrhythmic), cisapride, terfenadine, terodiline, and verapamil all affect cardiac repolarization and can produce QT prolongation in humans. In the conscious dog, the QT interval was assessed on a beat-to-beat basis in relation to each preceding RR interval at concentrations approximating the same unbound human concentrations. E-4031, cisapride and terodiline statistically increased the QT(RR1000) interval [the QT interval at a 60 beats/min (bpm) heart rate] 23, 8, and 9 ms, respectively, at concentrations 0.3 to 15.8 times their relevant clinical level. Increases were not observed for terfenadine or verapamil (p > 0.05 at all doses). Inspection of individual dog QT versus RR interval relationships showed clear QT interval responses specific to each treatment but not readily apparent when data are averaged at a heart rate of 60 bpm. For specific rectifier K(+) current (IKr) blockers, robust effects on mean QT prolongation can be detected. However, for drugs that affect repolarization through multiple channels, the effect on the mean QT interval may be more difficult to detect. Inspection of the beat-to-beat QT-RR interval relationship in an individual animal can increase the sensitivity for more accurate clinical prediction.