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

• Main Authors: Weiss, Rebecca J (Author), Bates, Sara V (Author), Song, Ya'nan (Author), Zhang, Yue (Author), Herzberg, Emily M (Author), Chen, Yih-Chieh (Author), Gong, Maryann M. (Author), Chien, Isabel (Author), Zhang, Lily (Author), Murphy, Shawn N (Author), Gollub, Randy L (Author), Grant, P. E (Author), Ou, Yangming (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
• Format: Article
• Language: English
• Published: BioMed Central, 2020-07-17T19:27:08Z.
• Subjects: Article
• Online Access: Get fulltext

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.