@article{a81b80cd41954fb49fcf6d660a67e83f,
title = "“Apr{\`e}s Mois, Le D{\'e}luge”: Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era",
abstract = "Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.",
keywords = "Biomedical informatics, Clinical informatics, Imaging informatics, Informatics, MR LINAC, MRI, MRI-guided radiotherapy, Radiomics",
author = "Kiser, {Kendall J.} and Smith, {Benjamin D.} and Jihong Wang and Fuller, {Clifton D.}",
note = "Funding Information: CF has received funding from the National Institute for Dental and Craniofacial Research Award (1R01DE025248-01/R56DE025248) and Academic-Industrial Partnership Award (R01 DE028290), the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679), the NIH Big Data to Knowledge (BD2K) Program of the National Cancer Institute (NCI) Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825), the NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program (1R01CA218148), the NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672), the NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Program (R25EB025787) as well as direct industry grant support, speaking honoraria and travel funding from Elekta AB. BS has received funding from the Cancer Prevention & Research Institute of Texas (RP160674), NIH R01 CA207216-01 and is an Andrew Sabin Family Fellow. JW has received research funding from NIH, Elekta AB, and GE Medical. Publisher Copyright: {\textcopyright} 2019 Kiser, Smith, Wang and Fuller.",
year = "2019",
month = sep,
day = "1",
doi = "10.3389/fonc.2019.00983",
language = "English (US)",
volume = "9",
journal = "Frontiers in Oncology",
issn = "2234-943X",
publisher = "Frontiers Media S. A.",
number = "SEP",
}