Measuring repeatability of dynamic contrast-enhanced MRI biomarkers improves evaluation of biological response to radiotherapy in lung cancer.

OBJECTIVES: To measure dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) biomarker repeatability in patients with non-small cell lung cancer (NSCLC). To use these statistics to identify which individual target lesions show early biological response. MATERIALS AND METHODS: A single-centre, prospective DCE-MRI study was performed between September 2015 and April 2017. Patients with NSCLC were scanned before standard-of-care radiotherapy to evaluate biomarker repeatability and two weeks into therapy to evaluate biological response. Volume transfer constant (Ktrans), extravascular extracellular space volume fraction (ve) and plasma volume fraction (vp) were measured at each timepoint along with tumour volume. Repeatability was assessed using a within-subject coefficient of variation (wCV) and repeatability coefficient (RC). Cohort treatment effects on biomarkers were estimated using mixed-effects models. RC limits of agreement revealed which individual target lesions changed beyond that expected with biomarker daily variation. RESULTS: Fourteen patients (mean age, 67 years +/- 12, 8 men) had 22 evaluable lesions (12 primary tumours, 8 nodal metastases, 2 distant metastases). The wCV (in 8/14 patients) was between 9.16% to 17.02% for all biomarkers except for vp, which was 42.44%. Cohort-level changes were significant for Ktrans and ve (p < 0.001) and tumour volume (p = 0.002). Ktrans and tumour volume consistently showed the greatest number of individual lesions showing biological response. In distinction, no individual lesions had a real change in ve despite the cohort-level change. CONCLUSION: Identifying individual early biological responders provided additional information to that derived from conventional cohort cohort-level statistics, helping to prioritise which parameters would be best taken forward into future studies. CLINICAL RELEVANCE STATEMENT: Dynamic contrast-enhanced magnetic resonance imaging biomarkers Ktrans and tumour volume are repeatable and detect early treatment-induced changes at both cohort and individual lesion levels, supporting their use in further evaluation of radiotherapy and targeted therapeutics. KEY POINTS: Few literature studies report quantitative imaging biomarker precision, by measuring repeatability or reproducibility. Several DCE-MRI biomarkers of lung cancer tumour microenvironment were highly repeatable. Repeatability coefficient measurements enabled lesion-specific evaluation of early biological response to therapy, improving conventional assessment.

Bioactivity-Guided Isolation and Identification of Anti-adipogenic Constituents from the n-Butanol Fraction of Cissus quadrangularis.

Obesity is marked by the buildup of fat in adipose tissue that increases body weight and the risk of many associated health problems, including diabetes and cardiovascular disease. Treatment options for obesity are limited, and available medications have many side effects. Thus there is a great need to find alternative medicines for treating obesity. This study explores the anti-adipogenic potential of the n-butanol fraction of Cissus quadrangularis (CQ-B) on 3T3-L1 mouse preadipocyte cell line. The expression of various lipogenic marker genes such as adiponectin, peroxisome proliferator-activated receptor gamma, leptin, fatty acid-binding proteins, sterol regulatory element-binding proteins, fetal alcohol syndrome, steroyl-CoA desaturase-1, lipoproteins, acetyl-CoA carboxylase alpha, and acetyl-CoA carboxylase beta were variously significantly downregulated. After establishing the anti-adipogenic potential of CQ-B, it was fractionated to isolate anti-adipogenic compounds. We observed significant reduction in neutral lipid content of differentiated cells treated with various fractions of CQ-B. Gas chromatography-mass spectrometry analysis revealed the presence of thirteen compounds with reported anti-adipogenic activities. Further studies to purify these compounds can offer efficacious and viable treatment options for obesity and related complications.

Osteogenic potential of hexane and dichloromethane fraction of Cissus quadrangularis on murine preosteoblast cell line MC3T3-E1 (subclone 4).

In continuation of the investigation of osteogenic potential of solvent fractions of ethanolic extract of Cissus quadrangularis (CQ), an ancient medicinal plant, most notably known for its bone-healing properties, to isolate and identify antiosteoporotic compounds. In the current study, we report the effect of hexane fraction (CQ-H) and dichloromethane fraction (CQ-D) of CQ on the differentiation and mineralization of mouse preosteoblast cell line MC3T3-E1 (subclone 4). Growth, viability, and proliferation assays revealed that low concentrations (0.1, 1, and 100 ng/ml) of both solvent fractions were nontoxic, whereas higher concentrations were toxic to the cells. Differentiation and mineralization of MC3T3-E1 with nontoxic concentrations of CQ-D and CQ-H revealed that CQ-D delayed the mineralization of MC3T3-E1 cells. However, early and enhanced mineralization was observed in cultures treated with nontoxic concentrations of CQ-H, as indicated by Von Kossa staining and expression profile of osteoblast marker genes such as osterix, Runx2, alkaline phosphatase (ALP), collagen (Col1a1), integrin-related bone sialoprotein (IBSP), osteopontin (OPN), and osteocalcin (OCN). These findings suggest CQ-H as the most efficacious solvent fraction for further investigation to isolate and identify the active compounds in CQ-H.

Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI.

This paper presents Bingham-NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham-NODDI that extends the NODDI formalism to address this limitation. Bingham-NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.

Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test.

PURPOSE: Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed. The purpose of this study is to propose a method (inspired by the Turing Test) for providing contour quality measures that directly draw upon practitioners' assessments of manual and automatic contours. This approach assumes that an inability to distinguish automatically produced contours from those of clinical experts would indicate that the contours are of sufficient quality for clinical use. In turn, it is anticipated that such contours would receive less manual editing prior to being accepted for clinical use. In this study, an initial assessment of this approach is performed with radiation oncologists and therapists. METHODS: Eight clinical observers were presented with thoracic organ-at-risk contours through a web interface and were asked to determine if they were automatically generated or manually delineated. The accuracy of the visual determination was assessed, and the proportion of contours for which the source was misclassified recorded. Contours of six different organs in a clinical workflow were for 20 patient cases. The time required to edit autocontours to a clinically acceptable standard was also measured, as a gold standard of clinical utility. Established quantitative measures of autocontouring performance, such as Dice similarity coefficient with respect to the original clinical contour and the misclassification rate accessed with the proposed framework, were evaluated as surrogates of the editing time measured. RESULTS: The misclassification rates for each organ were: esophagus 30.0%, heart 22.9%, left lung 51.2%, right lung 58.5%, mediastinum envelope 43.9%, and spinal cord 46.8%. The time savings resulting from editing the autocontours compared to the standard clinical workflow were 12%, 25%, 43%, 77%, 46%, and 50%, respectively, for these organs. The median Dice similarity coefficients between the clinical contours and the autocontours were 0.46, 0.90, 0.98, 0.98, 0.94, and 0.86, respectively, for these organs. CONCLUSIONS: A better correspondence with time saving was observed for the misclassification rate than the quantitative contour measures explored. From this, we conclude that the inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall. Hence, task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.

In vivo estimation of dispersion anisotropy of neurites using diffusion MRI.

We present a technique for mapping dispersion anisotropy of neurites in the human brain in vivo. Neurites are the structural substrate of the brain that support its function. Measures of their morphology from histology provide the gold standard for diagnosing various brain disorders. Some of these measures, e.g. neurite density and orientation dispersion, can now be mapped in vivo using diffusion MRI, enabling their use in clinical applications. However, in vivo methods for estimating more sophisticated measures, such as dispersion anisotropy, have yet to be demonstrated. Dispersion anisotropy allows more refined characterisation of the complex neurite configurations such as fanning or bending axons; its quantification in vivo can offer new imaging markers. The aim of this work is to develop a method to estimate dispersion anisotropy in vivo. Our approach builds on the Neurite Orientation Dispersion and Density Imaging (NODDI), an existing clinically feasible diffusion MRI technique. The estimation of dispersion anisotropy is achieved by incorporating Bingham distribution as the neurite orientation distribution function, with no additional acquisition requirements. We show the first in vivo maps of dispersion anisotropy and demonstrate that it can be estimated accurately with a clinically feasible protocol. We additionally show that the original NODDI is robust to the effects of dispersion anisotropy, when the the new parameter is not of interest.

The importance of being dispersed: A ranking of diffusion MRI models for fibre dispersion using in vivo human brain data.

In this work we compare parametric diffusion MRI models which explicitly seek to explain fibre dispersion in nervous tissue. These models aim at providing more specific biomarkers of disease by disentangling these structural contributions to the signal. Some models are drawn from recent work in the field; others have been constructed from combinations of existing compartments that aim to capture both intracellular and extracellular diffusion. To test these models we use a rich dataset acquired in vivo on the corpus callosum of a human brain, and then compare the models via the Bayesian Information Criteria. We test this ranking via bootstrapping on the data sets, and cross-validate across unseen parts of the protocol. We find that models that capture fibre dispersion are preferred. The results show the importance of modelling dispersion, even in apparently coherent fibres.