How consistently do physicians diagnose and manage drug-induced interstitial lung disease? Two surveys of European ILD specialist physicians

Introduction Currently there are no general guidelines for diagnosis or management of suspected drug-induced (DI) interstitial lung disease (ILD). The objective was to survey a sample of current European practice in the diagnosis and management of DI-ILD, in the context of the prescribing information approved by regulatory authorities for 28 licenced drugs with a recognised risk of DI-ILD. Methods Consultant physicians working in specialist ILD centres across Europe were emailed two surveys via a website link. Initially, opinion was sought regarding various diagnostic and management options based on seven clinical ILD case vignettes and five general questions regarding DI-ILD. The second survey involved 29 statements regarding the diagnosis and management of DI-ILD, derived from the results of the first survey. Consensus agreement was defined as 75% or greater. Results When making a diagnosis of DI-ILD, the favoured investigations used (other than computed tomography) included pulmonary function tests, bronchoscopy and blood tests. The preferred method used to decide when to stop treatment was a pulmonary function test. In the second survey, the majority of the statements were accepted by the 33 respondents, with only four of 29 statements not achieving consensus when the responses “agree” and “strongly agree” were combined as one answer. Conclusion The two surveys provide guidance for clinicians regarding an approach to the diagnosis and management of DI-ILD in which the current evidence base is severely lacking, as demonstrated by the limited information provided by the manufacturers of the drugs associated with a high risk of DI-ILD that we reviewed

Characterisation of the guinea pig model of osteoarthritis by in vivo three-dimensional magnetic resonance imaging

AbstractObjective: To characterise longitudinal changes in joint integrity and cartilage volume in vivo in the guinea pig spontaneous osteoarthritis (OA) model by magnetic resonance imaging (MRI).Methods: Guinea pigs knee were imaged in vivo by high-resolution three-dimensional (3D) MRI between the ages of 3 and 12 months. Image analysis was performed to assess qualitative knee joint changes between 3 and 12 months (n=16) and quantitative volumetric changes of the medial tibial cartilage between 9 and 12 months (n=7). After imaging, animals were killed and knees were assessed macroscopically and histologically.Results: From 3 to 6 months qualitative observation by MRI and histopathology indicated localised cartilage swelling on the medial tibial plateau. At 6 months, bone cysts had developed in the epiphysis. At 9 months, we observed by MRI and histopathology, fragmentation of the medial tibial cartilage in areas not protected by the meniscus. Cartilage degeneration had intensified at 12 months with evidence of widespread loss of cartilage throughout the tibial plateau. Segmentation of the MR cartilage images showed a 36% loss of volume between 9 and 12 months.Conclusions: We have achieved 3D image acquisition and segmentation of knee cartilage in a guinea pig model of chronic OA, which permits measurements previously only possible in man. High resolution and short acquisition time allowed qualitative longitudinal characterisation of the entire knee joint and enabled us to quantify for the first time longitudinal tibial cartilage volume loss associated with disease progression

Can cartilage loss be detected in knee osteoarthritis (OA) patients with 3–6 months' observation using advanced image analysis of 3T MRI?

SummaryPurposePrior investigations of magnetic resonance imaging (MRI) biomarkers of cartilage loss in knee osteoarthritis (OA) suggest that trials of interventions which affect this biomarker with adequate statistical power would require large clinical studies of 1–2 years duration. We hypothesized that smaller, shorter duration, “Proof of Concept” (PoC) studies might be achievable by: (1) selecting a population at high risk of rapid medial tibio-femoral (TF) progression, in conjunction with; (2) high-field MRI (3T), and; (3) using advanced image analysis. The primary outcome was the cartilage thickness in the central medial femur.MethodsMulti-centre, non-randomized, observational cohort study at four sites in the US. Eligible participants were females with knee pain, a body mass index (BMI)≥25kg/m2, symptomatic radiographic evidence of medial TF OA, and varus mal-alignment. The 29 participants had a mean age of 62 years, mean BMI of 36kg/m2, with eight index knees graded as Kellgren–Lawrence (K&L)=2 and 21 as K&L=3. Eligible participants had four MRI scans of one knee: two MRIs (1 week apart) were acquired as a baseline with follow-up MRI at 3 and 6 months. A trained operator, blind to time-point but not subject, manually segmented the cartilage from the Dual Echo Steady State water excitation MR images. Anatomically corresponding regions of interest were identified on each image by using a three-dimensional statistical shape model of the endosteal bone surface, and the cartilage thickness (with areas denuded of cartilage included as having zero thickness – ThCtAB) within each region was calculated. The percentage change from baseline at 3 and 6 months was assessed using a log-scale analysis of variance (ANOVA) model including baseline as a covariate. The primary outcome was the change in cartilage thickness within the aspect of central medial femoral condyle exposed within the meniscal window (w) during articulation, neglecting cartilage edges [nuclear (n)] (nwcMF·ThCtAB), with changes in other regions considered as secondary endpoints.ResultsAnatomical mal-alignment ranged from −1.9° to 6.3°, with mean 0.9°. With one exception, no changes in ThCtAB were detected at the 5% level for any of the regions of interest on the TF joint at 3 or 6 months of follow-up. The change in the primary variable (nwcMF·ThCtAB) from (mean) baseline at 3 months from the log-scale ANOVA model was −2.1% [95% confidence interval (CI) (−4.4%, +0.2%)]. The change over 6 months was 0.0% [95% CI (−2.7%, +2.8%)]. The 95% CI for the change from baseline did not include zero for the cartilage thickness within the meniscal window of the lateral tibia (wLT·ThCtAB) at 6 month follow-up (−1.5%, 95% CI [−2.9, −0.2]), but was not significant at the 5% level after correction for multiple comparisons.ConclusionsThe small inconsistent compartment changes, and the relatively high variabilities in cartilage thickness changes seen over time in this study, provide no additional confidence for a 3- or 6-month PoC study using a patient population selected on the basis of risk for rapid progression with the MRI acquisition and analyses employed

Imaging biomarkers of lung ventilation in interstitial lung disease from ¹²⁹Xe and oxygen enhanced ¹H MRI

Purpose: To compare imaging biomarkers from hyperpolarised 129Xe ventilation MRI and dynamic oxygen-enhanced MRI (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. To evaluate if biomarkers can separate ILD subtypes and detect early signs of disease resolution or progression. Study type: Prospective longitudinal. Population: Forty-one ILD (fourteen idiopathic pulmonary fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), eleven drug-induced ILD (DI-ILD), five connective tissue disease related-ILD (CTD-ILD)) patients and ten healthy volunteers imaged at visit 1. Thirty-four ILD patients completed visit 2 (eleven IPF, eight HP, ten DIILD, five CTD-ILD) after 6 or 26 weeks. Field strength/sequence: MRI was performed at 1.5 T, including inversion recovery T1 mapping, dynamic MRI acquisition with varying oxygen levels, and hyperpolarised 129Xe ventilation MRI. Subjects underwent standard spirometry and gas transfer testing. Assessment: Five 1H MRI and two 129Xe MRI ventilation metrics were compared with spirometry and gas transfer measurements. Statistical test: To evaluate differences at visit 1 among subgroups: ANOVA or Kruskal-Wallis rank tests with correction for multiple comparisons. To assess the relationships between imaging biomarkers, PFT, age and gender, at visit 1 and for the change between visit 1 and 2: Pearson correlations and multilinear regression models. Results: The global PFT tests could not distinguish ILD subtypes. Percentage ventilated volumes were lower in ILD patients than in HVs when measured with 129Xe MRI (HV 97.4 ± 2.6, CTD-ILD: 91.0 ± 4.8 p = 0.017, DI-ILD 90.1 ± 7.4 p = 0.003, HP 92.6 ± 4.0 p = 0.013, IPF 88.1 ± 6.5 p < 0.001), but not with OE-MRI. 129Xe reported more heterogeneous ventilation in DI-ILD and IPF than in HV, and OE-MRI reported more heterogeneous ventilation in DI-ILD and IPF than in HP or CTD-ILD. The longitudinal changes reported by the imaging biomarkers did not correlate with the PFT changes between visits. Data conclusion: Neither 129Xe ventilation nor OE-MRI biomarkers investigated in this study were able to differentiate between ILD subtypes, suggesting that ventilation-only biomarkers are not indicated for this task. Limited but progressive loss of ventilated volume as measured by 129Xe-MRI may be present as the biomarker of focal disease progresses. OE-MRI biomarkers are feasible in ILD patients and do not correlate strongly with PFT. Both OE-MRI and 129Xe MRI revealed more spatially heterogeneous ventilation in DI-ILD and IPF

Use of in vivo Imaging and physiologically-based kinetic modelling to predict hepatic transporter mediated drug–drug interactions in rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97–98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans

Functional principal component analyses of biomedical images as outcome measures

Medical imaging data are often valuable in evaluating disease and therapeutic effects. However, in formal assessment of treatment efficacy, it is usual to discard most of the rich information within the image, instead relying on simple summary measures. This reflects the absence of satisfactory statistical tools for the description and analysis of variability between images. We present extended techniques of functional data analysis applied to distributions of variable values extracted from specified regions within images, which are used to produce displays of 'principal densities' that allow interpretation of principal modes of variation in terms of features in the distributions of the voxel values. These techniques are especially relevant in circumstances where the spatial distribution of variables within the specified region is not of interest. Tumours, for example, are disorganized in nature and may change shape rapidly so it is not possible, even in principle, to create a 1–1 correspondence between images before and post treatment. The techniques that are introduced here, however, enable us to distinguish differences between pretreatment and post-treatment densities. These methods are essentially exploratory; hence we develop a permutation test providing more formal assessment of differences of treatment, which assesses the changes within dose group. Extensions to multivariate images of two or more variables are also illustrated and we show that the methodology makes bivariate functional data just as easy to handle as univariate data