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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.
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.
