From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.)
From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.).
AJNR Am J Neuroradiol. 2018 Apr;39(4):626-633. doi: 10.3174/ajnr.A5556. Epub 2018 Feb 22.
Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.
MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites.
The proposed count and the criterion standard count were highly correlated ( = 0.97, < .001) and not significantly different (t = -.83, = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated ( = -2.73, < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale ( = 0.35, < .01) than lesion load ( = 0.10, = .44) or lesion count ( = -.12, = .36) alone.
This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
病灶负荷是多发性硬化症的常用生物标志物，但历史上其与临床结果的相关性仅为中等程度。病灶计数包含了病灶形成的自然史，被认为提供了补充信息，但在病灶融合（即空间重叠）的患者中较难评估。我们引入了一种用于横截面计数病理性不同病灶的统计技术。
磁共振成像（MRI）用于评估每个位置发生病变的概率。使用一种新的技术对该图谱的纹理进行量化，并计数类似于病灶中心的簇。在 60 名接受纵向观察的患者中验证了与标准计数的有效性，在 14 名临床稳定的患者在 7 个部位采集的扫描中确定了可靠性。
所提出的计数与标准计数高度相关（ = 0.97， <.001）且无显著差异（t = -.83， =.41），并且在重复扫描中提出的计数的变异性与病灶负荷相当。在考虑到病灶负荷和年龄后，病灶计数与扩展残疾状况量表呈负相关（ = -2.73， <.01）。平均病灶大小与扩展残疾状况量表的相关性高于病灶负荷（ = 0.35， <.01），高于病灶计数（ = -.12， =.36）或病灶负荷（ = 0.10， =.44）单独的相关性。
本研究介绍了一种使用横截面数据计数病理性不同病灶的新技术，并证明了其恢复隐藏的纵向信息的能力。所提出的计数允许更准确地估计病灶大小，与残疾评分的相关性比病灶负荷或病灶计数单独的相关性更密切。
