Molecular Imaging Biomarkers for Oncology Clinical Trials

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.

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Author Correction: COVID-19 vaccine guidance for patients with cancer participating in oncology clinical trials

Nature Reviews Clinical Oncology ◽

10.1038/s41571-021-00503-2 ◽

2021 ◽

Author(s):

Aakash Desai ◽

◽

Justin F. Gainor ◽

Aparna Hegde ◽

Alison M. Schram ◽

...

Keyword(s):

Clinical Trials ◽

Patients With Cancer ◽

Oncology Clinical Trials

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Why performance status? A case for alternative functional assessments in pediatric oncology clinical trials

Cancer ◽

10.1002/cncr.33741 ◽

2021 ◽

Author(s):

Angela Steineck ◽

Abby R. Rosenberg

Keyword(s):

Clinical Trials ◽

Pediatric Oncology ◽

Performance Status ◽

Functional Assessments ◽

Oncology Clinical Trials

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

Clinical Trials ◽

10.1177/1740774520981934 ◽

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.

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The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Neurotherapeutics ◽

10.1007/s13311-021-01027-4 ◽

2021 ◽

Author(s):

Adam J. Schwarz

Keyword(s):

Clinical Trials ◽

Drug Development ◽

Neurological Disorders ◽

Amyloid Β ◽

Development Program ◽

Imaging Biomarkers ◽

Common Disease ◽

Structural Imaging ◽

Target Engagement ◽

Pharmacodynamic Effect

AbstractImaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect—especially with a clear relationship to dose—can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to “de-risk” the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer’s disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

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