Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

Autor: Isabel Chien, Emily M. Herzberg, Shawn N. Murphy, Randy L. Gollub, Rebecca J. Weiss, Lily N. Zhang, P. Ellen Grant, Yue Zhang, Yangming Ou, Sara V. Bates, Maryann Gong, Yih-Chieh Chen, Ya’nan Song
Rok vydání: 2019
Předmět:
Bioinformatics
lcsh:Medicine
Neonatal encephalopathy
Machine learning
computer.software_genre
General Biochemistry
Genetics and Molecular Biology
Hypoxic Ischemic Encephalopathy
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
International Classification of Diseases
Health informatics tools
Protocol
medicine
Humans
Probability
Protocol (science)
Clinical Trials as Topic
medicine.diagnostic_test
business.industry
lcsh:R
Infant
Newborn
Magnetic resonance imaging
Retrospective cohort study
Outcome prediction
General Medicine
medicine.disease
Magnetic Resonance Imaging
3. Good health
Clinical trial
Treatment Outcome
Hypoxia-Ischemia
Brain
Biomarker (medicine)
Artificial intelligence
business
Hypoxic ischemic encephalopathy
computer
Algorithms
Biomarkers
030217 neurology & neurosurgery
MRI
Zdroj: Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-16 (2019)
Journal of Translational Medicine
ISSN: 1479-5876
Popis: Background Secondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction. Methods This retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals. Discussion Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts. Trial Registration Not applicable. This study protocol mines existing clinical data thus does not meet the ICMJE definition of a clinical trial that requires registration
Databáze: OpenAIRE
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