Removing batch effects from histopathological images for enhanced cancer diagnosis.
Removing batch effects from histopathological images for enhanced cancer diagnosis.
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DOI：
10.1109/jbhi.2013.2276766
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发表时间：
2014-05
影响因子：
7.7
通讯作者：
Wang MD
中科院分区：
文献类型：
--
作者：
Kothari S;Phan JH;Stokes TH;Osunkoya AO;Young AN;Wang MD
研究人员已经为转化医学开发了计算机辅助决策支持系统，旨在使用组织病理学图像客观有效地诊断癌症。然而，这类系统的性能受到非生物实验变化或“批量效应”的干扰，这些变化通常发生在组织病理学数据中，特别是当图像是使用不同的成像设备和患者样本获取时。在需要跨实验室共享大量数据的大规模研究中，这就更成问题了。批处理效应会改变定量的形态图像特征，降低预测性能。使用四批肾肿瘤图像，比较了一种图像级和五种特征级的批处理效果去除方法。主成分变异分析表明，批次是图像特征方差的一大来源。结果表明，特征级归一化方法将批次贡献方差降低到几乎为零。此外，特征级归一化，特别是ComBatN，提高了跨批次和组合批次预测的性能。与未归一化相比，ComBatN分别在83%和90%的跨批次和组合批次预测模型中提高了性能。
Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or “batch effects” that can commonly occur in histopathological data, especially when images are acquired using different imaging devices and patient samples. This is even more problematic in large-scale studies in which cross-laboratory sharing of large volumes of data is necessary. Batch effects can change quantitative morphological image features and decrease the prediction performance. Using four batches of renal tumor images, we compare one image-level and five feature-level batch effect removal methods. Principal component variation analysis shows that batch is a large source of variance in image features. Results show that feature-level normalization methods reduce batch-contributed variance to almost zero. Moreover, feature-level normalization, especially ComBatN, improves cross-batch and combined-batch prediction performance. Compared to no normalization, ComBatN improves performance in 83% and 90% of cross-batch and combined-batch prediction models, respectively.