Bayesian design and analysis of two-arm cluster randomized trials using assurance.

We consider the design of a two-arm superiority cluster randomized controlled trial (RCT) with a continuous outcome. We detail Bayesian inference for the analysis of the trial using a linear mixed-effects model. The treatment is compared to control using the posterior distribution for the treatment effect. We develop the form of the assurance to choose the sample size based on this analysis, and its evaluation using a two loop Monte Carlo sampling scheme. We assess the proposed approach, considering the effect of different forms of prior distribution, and the number of Monte Carlo samples needed in both loops for accurate determination of the assurance and sample size. Based on this assessment, we provide general advice on each of these choices. We apply the approach to the choice of sample size for a cluster RCT into poststroke incontinence, and compare the resulting sample size to that from assurance based on a Wald test for the treatment effect. The Bayesian approach to design and analysis developed in this article can offer advantages in terms of an increase in the robustness of the chosen sample size to parameter mis-specification and reduced sample sizes if prior information indicates the treatment effect is likely to be larger than the minimal clinically important difference.

Sample size determination for point-of-care COVID-19 diagnostic tests: a Bayesian approach.

BACKGROUND: In a pandemic setting, it is critical to evaluate and deploy accurate diagnostic tests rapidly. This relies heavily on the sample size chosen to assess the test accuracy (e.g. sensitivity and specificity) during the diagnostic accuracy study. Too small a sample size will lead to imprecise estimates of the accuracy measures, whereas too large a sample size may delay the development process unnecessarily. This study considers use of a Bayesian method to guide sample size determination for diagnostic accuracy studies, with application to COVID-19 rapid viral detection tests. Specifically, we investigate whether utilising existing information (e.g. from preceding laboratory studies) within a Bayesian framework can reduce the required sample size, whilst maintaining test accuracy to the desired precision. METHODS: The method presented is based on the Bayesian concept of assurance which, in this context, represents the unconditional probability that a diagnostic accuracy study yields sensitivity and/or specificity intervals with the desired precision. We conduct a simulation study to evaluate the performance of this approach in a variety of COVID-19 settings, and compare it to commonly used power-based methods. An accompanying interactive web application is available, which can be used by researchers to perform the sample size calculations. RESULTS: Results show that the Bayesian assurance method can reduce the required sample size for COVID-19 diagnostic accuracy studies, compared to standard methods, by making better use of laboratory data, without loss of performance. Increasing the size of the laboratory study can further reduce the required sample size in the diagnostic accuracy study. CONCLUSIONS: The method considered in this paper is an important advancement for increasing the efficiency of the evidence development pathway. It has highlighted that the trade-off between lab study sample size and diagnostic accuracy study sample size should be carefully considered, since establishing an adequate lab sample size can bring longer-term gains. Although emphasis is on its use in the COVID-19 pandemic setting, where we envisage it will have the most impact, it can be usefully applied in other clinical areas.

Bayesian sample size determination for diagnostic accuracy studies.

The development of a new diagnostic test ideally follows a sequence of stages which, among other aims, evaluate technical performance. This includes an analytical validity study, a diagnostic accuracy study, and an interventional clinical utility study. In this article, we propose a novel Bayesian approach to sample size determination for the diagnostic accuracy study, which takes advantage of information available from the analytical validity stage. We utilize assurance to calculate the required sample size based on the target width of a posterior probability interval and can choose to use or disregard the data from the analytical validity study when subsequently inferring measures of test accuracy. Sensitivity analyses are performed to assess the robustness of the proposed sample size to the choice of prior, and prior-data conflict is evaluated by comparing the data to the prior predictive distributions. We illustrate the proposed approach using a motivating real-life application involving a diagnostic test for ventilator associated pneumonia. Finally, we compare the properties of the approach against commonly used alternatives. The results show that, when suitable prior information is available, the assurance-based approach can reduce the required sample size when compared to alternative approaches.

Comparative diagnostic accuracy studies with an imperfect reference standard - a comparison of correction methods.

BACKGROUND: Staquet et al. and Brenner both developed correction methods to estimate the sensitivity and specificity of a binary-response index test when the reference standard is imperfect and its sensitivity and specificity are known. However, to our knowledge, no study has compared the statistical properties of these methods, despite their long application in diagnostic accuracy studies. AIM: To compare the correction methods developed by Staquet et al. and Brenner. METHODS: Simulations techniques were employed to compare the methods under assumptions that the new test and the reference standard are conditionally independent or dependent given the true disease status of an individual. Three clinical datasets were analysed to understand the impact of using each method to inform clinical decision-making. RESULTS: Under the assumption of conditional independence, the Staquet et al. correction method outperforms the Brenner correction method irrespective of the prevalence of disease and whether the performance of the reference standard is better or worse than the index test. However, when the prevalence of the disease is high (> 0.9) or low (< 0.1), the Staquet et al. correction method can produce illogical results (i.e. results outside [0,1]). Under the assumption of conditional dependence; both methods failed to estimate the sensitivity and specificity of the index test especially when the covariance terms between the index test and the reference standard is not close to zero. CONCLUSION: When the new test and the imperfect reference standard are conditionally independent, and the sensitivity and specificity of the imperfect reference standard are known, the Staquet et al. correction method outperforms the Brenner method. However, where the prevalence of the target condition is very high or low or the two tests are conditionally dependent, other statistical methods such as latent class approaches should be considered.

Precision Targeted Therapy with BLU-667 for RET-Driven Cancers.

The receptor tyrosine kinase rearranged during transfection (RET) is an oncogenic driver activated in multiple cancers, including non-small cell lung cancer (NSCLC), medullary thyroid cancer (MTC), and papillary thyroid cancer. No approved therapies have been designed to target RET; treatment has been limited to multikinase inhibitors (MKI), which can have significant off-target toxicities and limited efficacy. BLU-667 is a highly potent and selective RET inhibitor designed to overcome these limitations. In vitro, BLU-667 demonstrated ≥10-fold increased potency over approved MKIs against oncogenic RET variants and resistance mutants. In vivo, BLU-667 potently inhibited growth of NSCLC and thyroid cancer xenografts driven by various RET mutations and fusions without inhibiting VEGFR2. In first-in-human testing, BLU-667 significantly inhibited RET signaling and induced durable clinical responses in patients with RET-altered NSCLC and MTC without notable off-target toxicity, providing clinical validation for selective RET targeting.Significance: Patients with RET-driven cancers derive limited benefit from available MKIs. BLU-667 is a potent and selective RET inhibitor that induces tumor regression in cancer models with RET mutations and fusions. BLU-667 attenuated RET signaling and produced durable clinical responses in patients with RET-altered tumors, clinically validating selective RET targeting. Cancer Discov; 8(7); 836-49. ©2018 AACR.See related commentary by Iams and Lovly, p. 797This article is highlighted in the In This Issue feature, p. 781.

Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions.

Diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires preexisting knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multimodality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue in mice. These data provide an unbiased, three-dimensional analysis of how disease affects the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.

Evaluation of a novel fluorescent nanobeacon for targeted imaging of Thomsen-Friedenreich associated colorectal cancer.

The Thomsen-Friedenreich (TF) antigen represents a prognostic biomarker of colorectal carcinoma. Here, using a nanobeacon, the surface of which was fabricated with peanut agglutinin as TF-binding molecules, we demonstrate that the nanobeacon is able to detect TF antigen in frozen and freshly biopsied polyps using fluorescence microscopy. Our results provide important clues about how to detect aberrant colonic tissues in the most timely fashion. Given the versatile application method for this topical nanobeacon, the protocol used in this work is amenable to clinical colonoscopy. Moreover, the prospects of clinical translation of this technology are evident.

Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas.

Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets.

Co-registration of multi-modality imaging allows for comprehensive analysis of tumor-induced bone disease.

Bone metastases are a clinically significant problem that arises in approximately 70% of metastatic breast cancer patients. Once established in the bone, tumor cells induce changes in the bone microenvironment that lead to bone destruction, pain, and significant morbidity. While much is known about the later stages of bone disease, less is known about the earlier stages or the changes in protein expression in the tumor micro-environment. Due to promising results of combining magnetic resonance imaging (MRI) and Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry (MALDI IMS) ion images in the brain, we developed methods for applying these modalities to models of tumor-induced bone disease in order to better understand the changes in protein expression that occur within the tumor-bone microenvironment. Specifically, we integrated 3-dimensional-volume reconstructions of spatially resolved MALDI IMS with high-resolution anatomical and diffusion weighted MRI data and histology in an intratibial model of breast tumor-induced bone disease. This approach enables us to analyze proteomic profiles from MALDI IMS data with corresponding in vivo imaging and ex vivo histology data. To the best of our knowledge, this is the first time that these three modalities have been rigorously registered in the bone. The MALDI mass-to-charge ratio peaks indicate differential expression of calcyclin, ubiquitin, and other proteins within the tumor cells, while peaks corresponding to hemoglobin A and calgranulin A provided molecular information that aided in the identification of areas rich in red and white blood cells, respectively. This multi-modality approach will allow us to comprehensively understand the bone-tumor microenvironment and thus may allow us to better develop and test approaches for inhibiting bone metastases.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.

PURPOSE: To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR). RESULTS: All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR. CONCLUSION: Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.

Imaging analysis of hepatoblastoma resectability across neoadjuvant chemotherapy.

PURPOSE: Hepatoblastomas often require neoadjuvant chemotherapy to facilitate partial hepatectomy, which necessitates freedom of tumor borders from the confluence of hepatic veins (COHV), portal vein bifurcation (PVB), and retrohepatic inferior vena cava (IVC). This study aimed to clarify the effect of incremental neoadjuvant cycles on the AHEP0731 protocol criteria of hepatoblastoma resectability. METHODS: Hepatoblastoma responses to neoadjuvant chemotherapy were analyzed among patients (n=23) treated at two children's hospitals between 1996 and 2010. Using digital imaging data, ellipsoid and point-based models were created to measure tumor volume regression and respective distances from tumor borders nearest to the COHV, PVB, and IVC. RESULTS: Hepatoblastoma volumes regressed with incremental neoadjuvant chemotherapy cycles (p<0.001). Although tumor borders regressed away from the COHV (p=0.008), on average only 1.1mm was gained. No change from tumor borders to the PVB was detected (p=0.102). Distances from tumor borders to the IVC remained stable at one hospital (p=0.612), but increased only 0.15 mm every 10 days of therapy at the other (p=0.002). Neoadjuvant chemotherapy induced slightly more tumors to meet the threshold vascular margin of 1cm (baseline to completion): COHV, 11 (47.8%) to 17 (73.9%; p=0.058); PVB, 11 (47.8%) to 15 (65.2%; p=0.157); and IVC, 4 (17.4%) to 10 (43.5%; p=0.034). No differences were detected in demographic or disease-specific characteristics between patients who did or did not achieve this 1-cm margin after conclusion of chemotherapy. CONCLUSION: Hepatoblastoma volumes regress significantly with increasing neoadjuvant chemotherapy cycles. However, tumors often remain anchored to the major hepatic vasculature, showing marginal improvement in resectability criteria.

A Bayes linear Bayes method for estimation of correlated event rates.

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates.

The radial diffusivity and magnetization transfer pool size ratio are sensitive markers for demyelination in a rat model of type III multiple sclerosis (MS) lesions.

Determining biophysical sensitivity and specificity of quantitative magnetic resonance imaging is essential to develop effective imaging metrics of neurodegeneration. Among these metrics, apparent pool size ratio (PSR) from quantitative magnetization transfer (qMT) imaging and radial diffusivity (RD) from diffusion tensor imaging (DTI) are both known to relate to histological measure of myelin density and integrity. However their relative sensitivities towards quantitative myelin detection are unknown. In this study, we correlated high-resolution quantitative magnetic resonance imaging measures of subvoxel tissue structures with corresponding quantitative myelin histology in a lipopolysaccharide (LPS) mediated animal model of MS. Specifically, we acquired quantitative magnetization transfer (qMT) and diffusion tensor imaging (DTI) metrics (on the same tissue sample) in an animal model system of type III oligodendrogliopathy which lacked prominent lymphocytic infiltration, a system that had not been previously examined with quantitative MRI. We find that the qMT measured apparent pool size ratio (PSR) showed the strongest correlation with a histological measure of myelin content. DTI measured RD showed the next strongest correlation, and other DTI and relaxation parameters (such as the longitudinal relaxation rate (R1f) or fractional anisotropy (FA)) showed considerably weaker correlations with myelin content.

Multifunctional nanobeacon for imaging Thomsen-Friedenreich antigen-associated colorectal cancer.

This research aimed to validate the specificity of the newly developed nanobeacon for imaging the Thomsen-Friedenreich (TF) antigen, a potential biomarker of colorectal cancer. The imaging agent is comprised of a submicron-sized polystyrene nanosphere encapsulated with a Coumarin 6 dye. The surface of the nanosphere was modified with peanut agglutinin (PNA) and poly(N-vinylacetamide (PNVA) moieties. The former binds to Gal-ß(1-3)GalNAc with high affinity while the latter enhances the specificity of PNA for the carbohydrates. The specificity of the nanobeacon was evaluated in human colorectal cancer cells and specimens, and the data were compared with immunohistochemical staining and flow cytometric analysis. Additionally, distribution of the nanobeacon in vivo was assessed using an "intestinal loop" mouse model. Quantitative analysis of the data indicated that approximately 2 µg of PNA were detected for each milligram of the nanobeacon. The nanobeacon specifically reported colorectal tumors by recognizing the tumor-specific antigen through the surface-immobilized PNA. Removal of TF from human colorectal cancer cells and tissues resulted in a loss of fluorescence signal, which suggests the specificity of the probe. Most importantly, the probe was not absorbed systematically in the large intestine upon topical application. As a result, no registered toxicity was associated with the probe. These data demonstrate the potential use of this novel nanobeacon for imaging the TF antigen as a biomarker for the early detection and prediction of the progression of colorectal cancer at the molecular level.

Monitoring the inflammatory response to infection through the integration of MALDI IMS and MRI.

Systemic bacterial infection is characterized by a robust whole-organism inflammatory response. Analysis of the immune response to infection involves technologies that typically focus on single organ systems and lack spatial information. Additionally, the analysis of individual inflammatory proteins requires antibodies specific to the protein of interest, limiting the panel of proteins that can be analyzed. Herein we describe the application of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) to mice systemically infected with Staphylococcus aureus to identify inflammatory protein masses that respond to infection throughout an entire infected animal. Integrating the resolution afforded by magnetic resonance imaging (MRI) with the sensitivity of MALDI IMS provides three-dimensional spatially resolved information regarding the distribution of innate immune proteins during systemic infection, allowing comparisons to in vivo structural information and soft-tissue contrast via MRI. Thus, integrating MALDI IMS with MRI provides a systems-biology approach to study inflammation during infection.

Structural basis for selective small molecule kinase inhibition of activated c-Met.

The receptor tyrosine kinase c-Met is implicated in oncogenesis and is the target for several small molecule and biologic agents in clinical trials for the treatment of cancer. Binding of the hepatocyte growth factor to the cell surface receptor of c-Met induces activation via autophosphorylation of the kinase domain. Here we describe the structural basis of c-Met activation upon autophosphorylation and the selective small molecule inhibiton of autophosphorylated c-Met. MK-2461 is a potent c-Met inhibitor that is selective for the phosphorylated state of the enzyme. Compound 1 is an MK-2461 analog with a 20-fold enthalpy-driven preference for the autophosphorylated over unphosphorylated c-Met kinase domain. The crystal structure of the unbound kinase domain phosphorylated at Tyr-1234 and Tyr-1235 shows that activation loop phosphorylation leads to the ejection and disorder of the activation loop and rearrangement of helix αC and the G loop to generate a viable active site. Helix αC adopts a orientation different from that seen in activation loop mutants. The crystal structure of the complex formed by the autophosphorylated c-Met kinase domain and compound 1 reveals a significant induced fit conformational change of the G loop and ordering of the activation loop, explaining the selectivity of compound 1 for the autophosphorylated state. The results highlight the role of structural plasticity within the kinase domain in imparting the specificity of ligand binding and provide the framework for structure-guided design of activated c-Met inhibitors.

Repeatability of DTI-based skeletal muscle fiber tracking.

Diffusion tensor imaging (DTI)-based muscle fiber tracking enables the measurement of muscle architectural parameters, such as pennation angle (theta) and fiber tract length (L(ft)), throughout the entire muscle. Little is known, however, about the repeatability of either the muscle architectural measures or the underlying diffusion measures. Therefore, the goal of this study was to investigate the repeatability of DTI fiber tracking-based measurements and theta and L(ft). Four DTI acquisitions were performed on two days that allowed for between acquisition, within day, and between day analyses. The eigenvalues and fractional anisotropy were calculated at the maximum cross-sectional area of, and fiber tracking was performed in, the tibialis anterior muscle of nine healthy subjects. The between acquisitions condition had the highest repeatability for the DTI indices and the architectural parameters. The overall inter class correlation coefficients (ICC's) were greater than 0.6 for both theta and L(ft) and the repeatability coefficients were theta < 10.2 degrees and L(ft) < 50 mm. In conclusion, under the experimental and data analysis conditions used, the repeatability of the diffusion measures is very good and repeatability of the architectural measurements is acceptable. Therefore, this study demonstrates the feasibility for longitudinal studies of alterations in muscle architecture using DTI-based fiber tracking, under similar noise conditions and with similar diffusion characteristics.

An automated algorithm to improve the precision of basilar artery diameter measurements before and after subarachnoid hemorrhage-induced vasospasm in an animal model.

OBJECTIVE: Quantifying vasospasm has traditionally been performed manually, a method prone to imprecision and user bias. An alternative approach is to use computerized image analysis techniques to define and quantify the diameter of a vessel. The goal of this article is to demonstrate a novel automated vessel measurement algorithm specific to the needs of vasospasm studies and to compare it with traditional manual measurements in an animal model of vasospasm. METHODS: A total of 576 arterial diameter measurements were collected by 4 independent, blinded examiners from 24 angiograms in a rabbit subarachnoid hemorrhage (SAH) model. Measurements were taken from 3 segments of the basilar artery in anteroposterior and lateral projections, both before SAH and after SAH-induced vasospasm. Means and standard deviations of 288 manual measurements were compared with 288 automated measurements. RESULTS: The precision of automated measurements was significantly improved compared with standardized manual measurements (85.7% decrease in variation; P < .001). When using automated measurements, the precision was not affected by vessel size, but when using manual measurements, smaller arteries were less precise (P = .04). There was no significant difference in precision between 2 different contrast concentrations (P = .32). CONCLUSION: Automated measurements of basilar artery diameters are more precise than manual measurements, both before and after SAH-induced vasospasm. The variability in the manual group worsens when the artery is smaller secondary to vasospasm, indicating a need for the use of this segmentation method.

Quantitative assessment of DTI-based muscle fiber tracking and optimal tracking parameters.

Diffusion tensor imaging-based fiber tracking in skeletal muscle has been used to reconstruct and quantify muscle architecture. In addition, the consistent pattern of muscle fiber geometry enables a quantitative assessment of the fiber tracking. This work describes a method to determine the accuracy of individual muscle fiber tracts based on the location at which the fibers terminate, the fiber path, and similarity to the neighboring fibers. In addition, the effect of different stop criteria settings on this quantitative assessment was investigated. Fiber tracking was performed on the tibialis anterior muscle of nine healthy subjects. Complete fiber tracts covered 89.4 +/- 9.6% and 75.0 +/- 15.2% of the aponeurosis area in the superficial and deep compartments, respectively. Applications of the method include the exclusion of erroneous fiber-tracking results, quantitative assessment of data set quality, and the assessment of fiber-tracking stop criteria.

A divergent approach to the synthesis of 3-substituted-2-pyrazolines: Suzuki cross-coupling of 3-sulfonyloxy-2-pyrazolines.

The efficient Suzuki cross-coupling of pyrazoline nonaflates with organoboron reagents was achieved to afford diverse 3-substituted-2-pyrazolines in excellent yield. The nonaflates displayed improved reactivity over the corresponding triflates and smoothly coupled to a variety of aryl- and heteroarylboronic acids. This process and its broad scope constitute a rapid, divergent strategy for the synthesis of elaborated 2-pyrazolines that are not readily obtained via conventional methods.