Exploring the angiographic-biologic phenotype in the IMAGINE study: quantitative UWFA and cytokine expression

2021 ◽  
pp. bjophthalmol-2020-318726
Author(s):  
Joseph R Abraham ◽  
Charles C Wykoff ◽  
Sruthi Arepalli ◽  
Leina Lunasco ◽  
Hannah J Yu ◽  
...  

BackgroundThis study investigates the association of intraocular cytokine expression and ultrawide-field fluorescein angiography (UWFA) quantitative imaging biomarkers and their association with angiographical feature response after antivascular endothelial growth factor (VEGF) therapy in diabetic macular oedema (DME).MethodsThe IMAGINE DME study is a post hoc imaging biomarker and intraocular cytokine assessment from the DAVE study, a prospective DME clinical trial that included aqueous humour sampling and UWFA imaging. Fifty-four cytokines associated with inflammation and angiogenesis were evaluated through multiplex arrays. UWFA parameters were assessed using an automated feature analysis platform to determine ischaemic and leakage indices and microaneurysm (MA) count. Eyes were classified into UWFA responder or non-responder groups based on longitudinal quantitative UWFA parameter improvement. Cytokine expression was correlated with UWFA metrics and evaluated in the context of therapeutic response.ResultsTwenty-one eyes were included with a mean age of 55±10 years. Increased panretinal leakage index correlated with VEGF (r=0.70, p=0.0005), angiopoietin-like 4 (r=0.77, p=4.6E-5) and interleukin (IL)-6 (r=0.64, p=0.002). Panretinal ischaemic index was associated with tissue inhibitor of metalloproteinases 1 (TIMP-1, r=0.49, p=0.03) and peripheral ischaemia correlated with VEGF (r=0.45, p=0.05). MA count correlated with increased monocyte chemotactic protein-4 (MCP-4, r=0.60, p=0.004) and platelet and endothelial cell adhesion molecule 1 (PECAM-1, r=0.58, p=0.005). Longitudinal MA reduction was associated with decreased baseline VEGF and urokinase receptor (uPAR) (p<0.05). High baseline VEGF and IL-6 were associated with dramatic reduction in macular leakage (p<0.05).ConclusionsBaseline and longitudinal quantitative UWFA imaging parameters correlated with multiple aqueous humour cytokine concentrations, including VEGF and IL-6. Further research is needed to assess the possible implications of using these findings for evaluating treatment response.

2021 ◽  
pp. 174077452098193
Author(s):  
Nancy A Obuchowski ◽  
Erick M Remer ◽  
Ken Sakaie ◽  
Erika Schneider ◽  
Robert J Fox ◽  
...  

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.


2017 ◽  
Vol 28 (4) ◽  
pp. 1003-1018 ◽  
Author(s):  
Brian J Smith ◽  
Reinhard R Beichel

Quantitative biomarkers derived from medical images are being used increasingly to help diagnose disease, guide treatment, and predict clinical outcomes. Measurement of quantitative imaging biomarkers is subject to bias and variability from multiple sources, including the scanner technologies that produce images, the approaches for identifying regions of interest in images, and the algorithms that calculate biomarkers from regions. Moreover, these sources may differ within and between the quantification methods employed by institutions, thus making it difficult to develop and implement multi-institutional standards. We present a Bayesian framework for assessing bias and variability in imaging biomarkers derived from different quantification methods, comparing agreement to a reference standard, studying prognostic performance, and estimating sample size for future clinical studies. The statistical methods are illustrated with data obtained from a positron emission tomography challenge conducted by members of the NCI's Quantitative Imaging Network program, in which tumor volumes were measured manually and with seven different semi-automated segmentation algorithms. Estimates and comparisons of bias and variability in the resulting measurements are provided along with an R software package for the technical performance analysis and an online web application for sample size and power analysis.


2014 ◽  
Vol 42 (2) ◽  
pp. 328-354 ◽  
Author(s):  
Ronald Boellaard ◽  
Roberto Delgado-Bolton ◽  
Wim J. G. Oyen ◽  
Francesco Giammarile ◽  
Klaus Tatsch ◽  
...  

Abstract The purpose of these guidelines is to assist physicians in recommending, performing, interpreting and reporting the results of FDG PET/CT for oncological imaging of adult patients. PET is a quantitative imaging technique and therefore requires a common quality control (QC)/quality assurance (QA) procedure to maintain the accuracy and precision of quantitation. Repeatability and reproducibility are two essential requirements for any quantitative measurement and/or imaging biomarker. Repeatability relates to the uncertainty in obtaining the same result in the same patient when he or she is examined more than once on the same system. However, imaging biomarkers should also have adequate reproducibility, i.e. the ability to yield the same result in the same patient when that patient is examined on different systems and at different imaging sites. Adequate repeatability and reproducibility are essential for the clinical management of patients and the use of FDG PET/CT within multicentre trials. A common standardised imaging procedure will help promote the appropriate use of FDG PET/CT imaging and increase the value of publications and, therefore, their contribution to evidence-based medicine. Moreover, consistency in numerical values between platforms and institutes that acquire the data will potentially enhance the role of semiquantitative and quantitative image interpretation. Precision and accuracy are additionally important as FDG PET/CT is used to evaluate tumour response as well as for diagnosis, prognosis and staging. Therefore both the previous and these new guidelines specifically aim to achieve standardised uptake value harmonisation in multicentre settings.


2021 ◽  
Vol 10 ◽  
Author(s):  
Petra J. van Houdt ◽  
Yingli Yang ◽  
Uulke A. van der Heide

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.


2017 ◽  
Vol 27 (10) ◽  
pp. 3139-3150 ◽  
Author(s):  
Nancy A Obuchowski ◽  
Jennifer Bullen

Introduction Quantitative imaging biomarkers (QIBs) are being increasingly used in medical practice and clinical trials. An essential first step in the adoption of a quantitative imaging biomarker is the characterization of its technical performance, i.e. precision and bias, through one or more performance studies. Then, given the technical performance, a confidence interval for a new patient’s true biomarker value can be constructed. Estimating bias and precision can be problematic because rarely are both estimated in the same study, precision studies are usually quite small, and bias cannot be measured when there is no reference standard. Methods A Monte Carlo simulation study was conducted to assess factors affecting nominal coverage of confidence intervals for a new patient’s quantitative imaging biomarker measurement and for change in the quantitative imaging biomarker over time. Factors considered include sample size for estimating bias and precision, effect of fixed and non-proportional bias, clustered data, and absence of a reference standard. Results Technical performance studies of a quantitative imaging biomarker should include at least 35 test–retest subjects to estimate precision and 65 cases to estimate bias. Confidence intervals for a new patient’s quantitative imaging biomarker measurement constructed under the no-bias assumption provide nominal coverage as long as the fixed bias is <12%. For confidence intervals of the true change over time, linearity must hold and the slope of the regression of the measurements vs. true values should be between 0.95 and 1.05. The regression slope can be assessed adequately as long as fixed multiples of the measurand can be generated. Even small non-proportional bias greatly reduces confidence interval coverage. Multiple lesions in the same subject can be treated as independent when estimating precision. Conclusion Technical performance studies of quantitative imaging biomarkers require moderate sample sizes in order to provide robust estimates of bias and precision for constructing confidence intervals for new patients. Assumptions of linearity and non-proportional bias should be assessed thoroughly.


2013 ◽  
Vol 26 (4) ◽  
pp. 630-641 ◽  
Author(s):  
Andrew J. Buckler ◽  
M. Ouellette ◽  
J. Danagoulian ◽  
G. Wernsing ◽  
Tiffany Ting Liu ◽  
...  

2013 ◽  
Vol 26 (4) ◽  
pp. 642-642
Author(s):  
Andrew J. Buckler ◽  
Tiffany Ting Liu ◽  
Erica Savig ◽  
Baris E. Suzek ◽  
Daniel L. Rubin ◽  
...  

Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.


Sign in / Sign up

Export Citation Format

Share Document