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Journal of the Korean Neurological Association 2009;27(4): 369-374.
Development of a Semi-automatic Computer System to Register MRI Lesions Onto a Brain Template for Quantitative Analyses in Clinical Trials Having MRI Findings as Surrogate Endpoints: A Preliminary Report
김동억, 권건환a 고은아b 지명구 정지원b 노상미 강동희 탁윤오c 김태윤a 박경종a 정상욱 최흥국a
동국대학교 일산병원 신경과, 인제대학교 전산학과a, 미국 라이스대학교 생화학 세포생물학과b, 인제대학교 의료영상과학과c
뇌MRI를 대리결론변수로 하는 임상시험을 위한 병변의 뇌표준판 등록 및 정량분석 소프트웨어 개발: 예비보고
Dong-Eog Kim
Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea; Department of Computer Sciencea, Inje University, Gimhae, Korea; Department of Biochemistry and Cell Biologyb, William Marsh Rice University, Houston, Texas, USA; Department of Medical Imaging Sciencec, Inje University, Gimhae, Korea
Abstract
Background: Clinical trials that utilize imaging findings as surrogate endpoints are considered to be cost-effective. However, unlike numeric data, magnetic resonance imaging (MRI) findings are not quantifiable. Thus, we have begun to develop a software package that is able to convert qualitative MRI findings into quantifiable data.
Methods: Computer software (DUIH_Image) was created with which every patient’s MRI data can be registered on a standard brain template. Interuser and intrauser reliabilities for the registration were measured, and then a proof-of-principle experiment was conducted to determine whether the system could identify factors that were associated with a greater National Institutes of Health Stroke Scale (NIHSS) score at admission. We studied 40 consecutive patients [65.1±14.2 years old (mean±SD); 22 males and 18 females] with first-ever acute lacunar infarction of the corona radiata, who were divided into two groups according to their NIHSS score (i.e., low: 0?2; high: ≥3). The following parameters were compared between these two groups: (1) data retrieved from clinical profiles, including demographic and risk factor variables; and (2) accumulated diffusion MRI lesions mapped on a standard template.
Results: Modest levels of interuser and intrauser reliability were observed (p<0.05, R2=0.63?0.84, Pearson correlations). Regarding the clinical profiles, no significant difference was found for the numeric data sets or infarct size between the two groups. However, on the accumulated lesion map image, the lesion area that overlapped the most was located more posterolaterally in the high NIHSS score group than in the low NIHSS score group.
Conclusions: In this pilot study we have demonstrated the potential usefulness of the DUIH_Image software. We plan to update this software to enable its utilization in actual clinical trials.Key Words: Magnetic resonance imaging, Quantification, Computer software, Brain template, NIHSS
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