Empiric Methods to Account for Pre-analytical Variability in Digital Histopathology in Frontotemporal Lobar Degeneration
Empiric Methods to Account for Pre-analytical Variability in Digital Histopathology in Frontotemporal Lobar Degeneration
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DOI：
10.3389/fnins.2019.00682
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发表时间：
2019-07-03
影响因子：
4.3
通讯作者：
Irwin, David J.
中科院分区：
文献类型：
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作者：
Giannini, Lucia A. A.;Xie, Sharon X.;Irwin, David J.
数字病理学在神经退行性疾病研究中越来越突出，但染色批次之间免疫组织化学染色强度的变化阻碍了大规模的比较研究。在这里，我们提供了一种统计学上严格的方法，以解释大样本的额颞叶变性与tau夹杂物（FTLD-Tau，N = 39）或TDP-43夹杂物（FTLD-TDP，N = 53）的脑组织中的染色批次效应。我们分析了数字病理学的重复测量之间的关系，即，来自两个不同染色批次的灰质（GM）和白色质（WM）的病理学占据面积百分比（%AO）。我们发现FTLD-Tau中不同染色批次的重复测量存在显著差异（平均差异：GM = 1.13 +/- 0.44，WM = 1.28 +/- 0.56; p < 0.001）和FTLD-TDP（GM = 0.95 +/- 0.66，WM = 0.90 +/- 0.77; p < 0.001），这些测量值呈线性相关（R平方[Rsq]：FTLD-Tau GM = 0.92，WM = 0.92; FTLD-TDP GM = 0.75，WM = 0.78; p均< 0.001）。因此，我们使用线性回归将不同染色批次的%AO转换为等效值。使用训练测试集设计，我们检查了转换先决条件（即，Rsq），并且我们应用了等价因子（即，β，截距）到独立测试集以确定转换结果（即，组内相关系数（ICC）。首先，线性回归的随机迭代（x100）表明，对于前瞻性使用可行的较小训练集（N = 12-24）具有可接受的转换先决条件（平均Rsq：FTLD-Tau >= 0.9; FTLD-TDP >= 0.7）。当在独立的互补测试集上进行交叉验证时，在FTLD-Tau中，N = 12个训练集导致100%的GM和WM转换，具有最佳转换结果（ICC >= 0.8），而在FTLD-TDP中，N = 24个训练集导致测试集中的最佳ICC（GM = 72%，WM = 98%）。因此，我们建议在FTLD-Tau中设置N = 12的训练集，在FTLD-TDP中设置N = 24的训练集，以进行前瞻性转换。最后，转换使我们能够显著减少FTLD-Tau（GM/WM：p < 0.001）和FTLD-TDP（GM/WM：p <0.001）重复测量的批次相关差异，并减少FTLD-Tau（GM：-40%; WM：-34%）和FTLD-TDP（GM：-20%; WM：-30%）功效分析中估计的必要样本量。最后，我们使用第二个开源图像分析平台测试了我们方法的通用性，并发现了类似的结果。我们得出结论，一式两份染色的少量组织样本可用于解释分析前的变异性，如染色批次效应，从而改进未来研究的方法。
Digital pathology is increasingly prominent in neurodegenerative disease research, but variability in immunohistochemical staining intensity between staining batches prevents large-scale comparative studies. Here we provide a statistically rigorous method to account for staining batch effects in a large sample of brain tissue with frontotemporal lobar degeneration with tau inclusions (FTLD-Tau, N = 39) or TDP-43 inclusions (FTLD-TDP, N = 53). We analyzed the relationship between duplicate measurements of digital pathology, i.e., percent area occupied by pathology (%AO) for grey matter (GM) and white matter (WM), from two distinct staining batches. We found a significant difference in duplicate measurements from distinct staining batches in FTLD-Tau (mean difference: GM = 1.13 +/- 0.44, WM = 1.28 +/- 0.56; p < 0.001) and FTLD-TDP (GM = 0.95 +/- 0.66, WM = 0.90 +/- 0.77; p < 0.001), and these measurements were linearly related (R-squared [Rsq]: FTLD-Tau GM = 0.92, WM = 0.92; FTLD-TDP GM = 0.75, WM = 0.78; p < 0.001 all). We therefore used linear regression to transform %AO from distinct staining batches into equivalent values. Using a train-test set design, we examined transformation prerequisites (i.e., Rsq) from linear-modeling in training sets, and we applied equivalence factors (i.e., beta, intercept) to independent testing sets to determine transformation outcomes (i.e., intraclass correlation coefficient [ICC]). First, random iterations (x100) of linear regression showed that smaller training sets (N = 12-24), feasible for prospective use, have acceptable transformation prerequisites (mean Rsq: FTLD-Tau >= 0.9; FTLD-TDP >= 0.7). When cross-validated on independent complementary testing sets, in FTLD-Tau, N = 12 training sets resulted in 100% of GM and WM transformations with optimal transformation outcomes (ICC >= 0.8), while in FTLD-TDP N = 24 training sets resulted in optimal ICC in testing sets (GM = 72%, WM = 98%). We therefore propose training sets of N = 12 in FTLD-Tau and N = 24 in FTLD-TDP for prospective transformations. Finally, the transformation enabled us to significantly reduce batch-related difference in duplicate measurements in FTLD-Tau (GM/WM: p < 0.001 both) and FTLD-TDP (GM/WM: p < 0.001 both), and to decrease the necessary sample size estimated in a power analysis in FTLD-Tau (GM:-40%; WM: -34%) and FTLD-TDP (GM: -20%; WM: -30%). Finally, we tested generalizability of our approach using a second, open-source, image analysis platform and found similar results. We concluded that a small sample of tissue stained in duplicate can be used to account for pre-analytical variability such as staining batch effects, thereby improving methods for future studies.