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J Transl Med. 2019 Nov 21;17(1):385. doi: 10.1186/s12967-019-2119-5.

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

Author information

1
Division of Newborn Medicine, Department of Pediatrics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
2
Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Landmark Center 7022, Boston, MA, 02115, USA.
3
Computer Science & Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
4
Laboratory of Computer Science, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
5
Department of Psychiatry and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
6
Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Landmark Center 7022, Boston, MA, 02115, USA. Ellen.Grant@childrens.harvard.edu.
7
Neuroradiology Division, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Ellen.Grant@childrens.harvard.edu.
8
Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Landmark Center 7022, Boston, MA, 02115, USA. Yangming.Ou@childrens.harvard.edu.
9
Neuroradiology Division, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Yangming.Ou@childrens.harvard.edu.
10
Computational Health Informatics Program (CHIP), Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Yangming.Ou@childrens.harvard.edu.

Abstract

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.

KEYWORDS:

Bioinformatics; Biomarkers; Hypoxic ischemic encephalopathy; MRI; Machine learning; Neonatal encephalopathy; Outcome prediction

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