We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results to allow patient tracking. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system quantifies lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. We aim to evaluate the two most important features of the system, data mining and longitudinal lesion tracking, to demonstrate the MS eFolder’s capability in improving clinical workflow efficiency and outcome analysis for research. In order to evaluate data mining capabilities, we have collected radiological and neurological data from 72 patients, 36 Caucasian and 36 Hispanic matched by gender, disease duration, and age. Data analysis on those patients based on ethnicity is performed, and analysis results are displayed by the system’s web-based user interface. The data mining module is able to successfully separate Hispanic and Caucasian patients and compare their disease profiles. For longitudinal lesion tracking, we have collected 4 longitudinal cases and simulated different lesion growths over the next year. As a result, the eFolder is able to detect changes in lesion volume and identifying lesions with the most changes. Data mining and lesion tracking evaluation results show high potential of eFolder’s usefulness in patientcare and informatics research for multiple sclerosis.
Kevin C. Ma, Sydney Forsyth, Lilyana Amezcua, and Brent J. Liu, "Evaluation of longitudinal tracking and data mining for an imaging informatics-based multiple sclerosis e-folder (Conference Presentation)," Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380K (Presented at SPIE Medical Imaging: February 15, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2256579.5361903991001.
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