Watts Richard, Thomas Alex, Filippi Christopher G, Nickerson Joshua P, Freeman Kalev
From the Departments of Radiology (R.W., C.G.F., J.P.N.), Surgery (A.T., K.V.), and Neurology (C.G.F.), University of Vermont, Given Medical Building E301, 89 Beaumont Ave, Burlington, VT 05405.
Radiology. 2014 Jul;272(1):217-23. doi: 10.1148/radiol.14131856. Epub 2014 Mar 14.
To investigate the extent of bias in a clinical study involving "pothole analysis" of diffusion-tensor imaging (DTI) data used to quantify white matter lesion load in diseases with a heterogeneous spatial distribution of pathologic findings, such as mild traumatic brain injury (TBI), and create a mathematical model of the bias.
Use of the same reference population to define normal findings and make comparisons with a patient group introduces bias, which potentially inflates reported diagnostic performance. In this institutional review board-approved prospective observational cohort study, DTI data were obtained in 20 patients admitted to the emergency department with mild TBI and in 16 control subjects. Potholes and molehills were defined as clusters of voxels with fractional anisotropy values more than 2 standard deviations below and above the mean of the corresponding voxels in the reference population, respectively. The number and volume of potholes and molehills in the two groups were compared by using a Mann-Whitney U test.
Standard analysis showed significantly more potholes in mild TBI than in the control group (102.5 ± 34.3 vs 50.6 ± 28.9, P < .001). Repeat analysis by using leave-one-out cross-validation decreased the apparent difference in potholes between groups (mild TBI group, 102.5 ± 34.3; control group, 93.4 ± 27.2; P = .369). It was demonstrated that even with 100 subjects, this bias can decrease the voxelwise false-positive rate by more than 30% in the control group.
The pothole approach to neuroimaging data may introduce bias, which can be minimized by independent training and test groups or cross-validation methods. This bias is sufficient to call into question the previously reported diagnostic performance of DTI for mild TBI.
研究一项临床研究中的偏倚程度，该研究涉及对扩散张量成像（DTI）数据进行“坑洼分析”，以量化病理表现具有异质空间分布的疾病（如轻度创伤性脑损伤（TBI））中的白质病变负荷，并创建偏倚的数学模型。
使用相同的参考人群来定义正常表现并与患者组进行比较会引入偏倚，这可能会夸大所报告的诊断性能。在这项经机构审查委员会批准的前瞻性观察性队列研究中，获取了20名因轻度TBI入住急诊科的患者和16名对照受试者的DTI数据。坑洼和小丘分别定义为体素簇，其分数各向异性值分别低于和高于参考人群中相应体素平均值2个标准差以上。使用Mann-Whitney U检验比较两组中坑洼和小丘的数量和体积。
标准分析显示，轻度TBI组的坑洼明显多于对照组（102.5±34.3对50.6±28.9，P<.001）。使用留一法交叉验证进行的重复分析减少了两组之间坑洼的明显差异（轻度TBI组，102.5±34.3；对照组，93.4±27.2；P=.369）。结果表明，即使有100名受试者，这种偏倚也可使对照组的体素水平假阳性率降低30%以上。
神经影像学数据的坑洼分析方法可能会引入偏倚，通过独立的训练组和测试组或交叉验证方法可将其降至最低。这种偏倚足以使人质疑先前报道的DTI对轻度TBI的诊断性能。
