Article

Machine learning-based imaging biomarkers improve statistical power in clinical trials

2019-10-29

Abstract excerpt

Radiomic models, which leverage complex imaging patterns and machine learning, are increasingly accurate in predicting patient response to treatment and clinical outcome on an individual patient basis. In this work, we show that this predictive power can be utilized in clinical trials to significantly increase statistical power to detect treatment effects or reduce the sample size required to achieve a given power...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 6bbfcba3-3ff5-5471-a5cf-2bd622ebd70f
DOI: 10.1101/19010041

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning-based imaging biomarkers improve statistical power in clinical trialsDOI 10.1101/19010041Read

From Genes to Imaging Phenotypes: Radiomics and Machine Learning as Tools to Decode Molecular Pathways in Alzheimer's Disease.Semantic proximity Read

A review of AI-based radiogenomics in neurodegenerative diseaseSemantic proximity Read

Boosting power for clinical trials using classifiers based on multiple biomarkers.Semantic proximity Read

Predicting future cognitive impairment in preclinical Alzheimer’s disease using multimodal imaging: a multisite machine learning studySemantic proximity Read

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.Semantic proximity Read

Simulating the outcome of amyloid treatments in Alzheimer’s disease from imaging and clinical dataSemantic proximity Read

Mendelian randomization and clinical retrospective radiomics study reveal causal relationships between brain imaging-derived phenotypes and epilepsy risk.Semantic proximity Read

Generating synthetic tau-PET scans in Alzheimer’s disease from MRI, blood biomarkers and demographics with deep learningSemantic proximity Read

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer’s Disease Clinical TrialsSemantic proximity Read

Generate Synthetic Data in R for a Hypothetical Alzheimer’s Disease TrialSemantic proximity Read

Building Reliable Radiomic Models Using Image PerturbationSemantic proximity Read

Use of preclinical Alzheimer’s disease trajectories for clinical trial designSemantic proximity Read