Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group

2021 ◽  
Vol 153 ◽  
pp. 64-71
Author(s):  
Petra J. van Houdt ◽  
Hina Saeed ◽  
Daniela Thorwarth ◽  
Clifton D. Fuller ◽  
William A. Hall ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Working Group ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Quantitative Imaging Biomarkers ◽  
Multicentre Studies

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects

2021 ◽  
pp. 174077452098193
Author(s):  
Nancy A Obuchowski ◽  
Erick M Remer ◽  
Ken Sakaie ◽  
Erika Schneider ◽  
Robert J Fox ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Measurement Bias ◽  
Technical Performance ◽  
Quantitative Imaging Biomarker ◽  
Performance Validation

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.

Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage

2017 ◽  
Vol 27 (10) ◽  
pp. 3139-3150 ◽  
Author(s):  
Nancy A Obuchowski ◽  
Jennifer Bullen
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Performance Studies ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Technical Performance ◽  
Quantitative Imaging Biomarkers ◽  
Confidence Interval Coverage ◽  
Quantitative Imaging Biomarker

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.

scholarly journals Evaluation of Quantitative Imaging Biomarkers for Early-phase Clinical Trials of Steatohepatitis in Adolescents

2020 ◽  
Vol 70 (1) ◽  
pp. 99-105
Author(s):  
Nidhi P. Goyal ◽  
Mary Catherine Sawh ◽  
Patricia Ugalde-Nicalo ◽  
Jorge E. Angeles ◽  
James A. Proudfoot ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Quantitative Imaging ◽  
Imaging Biomarkers ◽  
Quantitative Imaging Biomarkers ◽  
Early Phase Clinical Trials

Statistical Considerations for Planning Clinical Trials with Quantitative Imaging Biomarkers

2018 ◽  
Vol 111 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Nancy A Obuchowski ◽  
P David Mozley ◽  
Dawn Matthews ◽  
Andrew Buckler ◽  
Jennifer Bullen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
Imaging Biomarkers ◽  
Quantitative Imaging Biomarkers

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

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.

scholarly journals Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment

2014 ◽  
Vol 24 (1) ◽  
pp. 27-67 ◽  
Author(s):  
David L Raunig ◽  
Lisa M McShane ◽  
Gene Pennello ◽  
Constantine Gatsonis ◽  
Paul L Carson ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Statistical Methods ◽  
Quantitative Imaging ◽  
Imaging Biomarkers ◽  
Technical Performance ◽  
Quantitative Imaging Biomarkers

scholarly journals Correction: Global spectral and local molecular connects for optical coherence tomography features to classify oral lesions towards unravelling quantitative imaging biomarkers

RSC Advances ◽  
2016 ◽  
Vol 6 (48) ◽  
pp. 42612-42612
Author(s):  
Satarupa Banerjee ◽  
Swarnadip Chatterjee ◽  
Anji Anura ◽  
Jitamanyu Chakrabarty ◽  
Mousumi Pal ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Quantitative Imaging ◽  
Imaging Biomarkers ◽  
Oral Lesions ◽  
Optical Coherence ◽  
Quantitative Imaging Biomarkers

Correction for ‘Global spectral and local molecular connects for optical coherence tomography features to classify oral lesions towards unravelling quantitative imaging biomarkers’ by Satarupa Banerjee et al., RSC Adv., 2016, 6, 7511–7520.

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