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.

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.

scholarly journals Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians

2021 ◽  
Vol 11 (7) ◽  
pp. 602
Author(s):  
Anne-Noëlle Frix ◽  
François Cousin ◽  
Turkey Refaee ◽  
Fabio Bottari ◽  
Akshayaa Vaidyanathan ◽  
...  
Keyword(s):  
Lung Diseases ◽  
Learning Algorithm ◽  
Quantitative Imaging ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Modern Medicine ◽  
Imaging Biomarkers ◽  
Specific Method ◽  
Imaging Data ◽  
Deep Learning Algorithm

Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.

scholarly journals Methods for the Selection of Covariates in Nutritional Epidemiology Studies: A Meta-Epidemiological Review

2019 ◽  
Vol 3 (10) ◽  
Author(s):  
Dena Zeraatkar ◽  
Kevin Cheung ◽  
Kirolos Milio ◽  
Max Zworth ◽  
Arnav Gupta ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Observational Studies ◽  
A Priori ◽  
High Impact ◽  
Nutritional Epidemiology ◽  
Epidemiology Studies ◽  
Analytic Models ◽  
Residual Confounding ◽  
Criteria For Selection ◽  
Selection Of

ABSTRACT Background Observational studies provide important information about the effects of exposures that cannot be easily studied in clinical trials, such as nutritional exposures, but are subject to confounding. Investigators adjust for confounders by entering them as covariates in analytic models. Objective The aim of this study was to evaluate the reporting and credibility of methods for selection of covariates in nutritional epidemiology studies. Methods We sampled 150 nutritional epidemiology studies published in 2007/2008 and 2017/2018 from the top 5 high-impact nutrition and medical journals and extracted information on methods for selection of covariates. Results Most studies did not report selecting covariates a priori (94.0%) or criteria for selection of covariates (63.3%). There was general inconsistency in choice of covariates, even among studies investigating similar questions. One-third of studies did not acknowledge potential for residual confounding in their discussion. Conclusion Studies often do not report methods for selection of covariates, follow available guidance for selection of covariates, nor discuss potential for residual confounding.

scholarly journals Panels and models for accurate prediction of tumor mutation burden in tumor samples

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Elizabeth Martínez-Pérez ◽  
Miguel Angel Molina-Vila ◽  
Cristina Marino-Buslje
Keyword(s):  
Clinical Trials ◽  
Linear Regression ◽  
External Validation ◽  
Standard Of Care ◽  
Immune Checkpoint Blockade ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Step Selection ◽  
Selection Of Patients ◽  
Selection Of

AbstractImmune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.

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

Fluorescent potentiometric dyes for membrane-potential imaging

1994 ◽  
Vol 52 ◽  
pp. 166-167
Author(s):  
Leslie M. Loew
Keyword(s):  
Membrane Potential ◽  
Single Cells ◽  
Quantitative Imaging ◽  
Optical Recording ◽  
Biological Processes ◽  
Major Focus ◽  
The Past ◽  
Excitable Systems ◽  
Major Application ◽  
The Impact

A major application of potentiometric dyes has been the multisite optical recording of electrical activity in excitable systems. After being championed by L.B. Cohen and his colleagues for the past 20 years, the impact of this technology is rapidly being felt and is spreading to an increasing number of neuroscience laboratories. A second class of experiments involves using dyes to image membrane potential distributions in single cells by digital imaging microscopy - a major focus of this lab. These studies usually do not require the temporal resolution of multisite optical recording, being primarily focussed on slow cell biological processes, and therefore can achieve much higher spatial resolution. We have developed 2 methods for quantitative imaging of membrane potential. One method uses dual wavelength imaging of membrane-staining dyes and the other uses quantitative 3D imaging of a fluorescent lipophilic cation; the dyes used in each case were synthesized for this purpose in this laboratory.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

The Datalogical Drug Mule

2017 ◽  
Vol 3 (3) ◽  
pp. 9-29
Author(s):  
Juan Llamas-Rodriguez
Keyword(s):  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Data Driven ◽  
Biological Processes ◽  
Cultural Narratives ◽  
Government Documents ◽  
Human Labor ◽  
Information And Communication ◽  
Data Capturing ◽  
Legal Structures

Borders and bodies are increasingly regulated by data-capturing mechanisms spread across the world through information and communication technologies. This article traces the features and implications of such a border-body datalogical entanglement through the figure of the drug mule. It analyzes government documents and recorded case studies to argue that this figure emerges from an assemblage of cultural narratives, legal structures, human labor, technical practices, and biological processes. The datalogical drug mule is already implicated in a struggle over what, and how, data is meaningful and actionable. Investigating this figure allows us to begin disentangling the data-driven mechanisms that constitute modern borders and bodies while at the same time accounting for analog continuities in contemporary practices of border security.

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