染色变异与颜色归一化对病理学预后预测的影响
Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology
AI总结：
本研究发现，即便采用传统颜色调整或CycleGAN染色归一化，在一批H&E染色肺癌组织切片上训练的DNN也无法泛化到同组织块不同时间制备的切片，凸显需开发新的组织图像采集处理方法以保障DNN预后预测的泛用性。
摘要：
近年来，深度神经网络（DNNs）在病理学应用中展现出卓越性能，凭借从大型数据集中学习细微特征的能力，其表现甚至可能超越专业病理学家。为DNN任务准备数字病理学数据集时，一个复杂问题是染色质量存在差异。解决该问题的常用方法是对图像进行染色归一化。本研究表明，即便应用了染色归一化方法，在一批组织学切片上训练良好的DNN模型也无法泛化到同一组织块在不同时间制备的另一批切片。本研究使用了此前报道的一个DNN的样本数据，该DNN通过对同期处理的H&E染色原发肿瘤组织切片数字图像进行训练和测试，能够高精度识别早期非小细胞肺癌（NSCLC）患者的肿瘤是否发生转移。本研究获取了一批新的组织学切片，这些切片来自同一实验室在不同时间处理的相同组织块的相邻再切片。我们发现，在任意一批切片/图像上训练的DNN都无法泛化，也无法预测另一批切片/图像的病情进展（跨批次AUC为0.52-0.53，而同批次AUC为0.74-0.81）。即便通过传统颜色调整或借助循环生成对抗网络（CycleGAN）流程进行染色归一化来校正染色差异，这种泛化失败的情况也未得到改善。这凸显了开发一种全新方法的必要性，该方法需能处理和采集一致的组织学切片显微图像，以用于训练预测性DNN算法并实现其通用应用。
英文摘要：
In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study we obtained a new series of histologic slides from the adjacent recuts of same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on the either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC_cross-batch = 0.52 - 0.53 compared to AUC_same-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference correction were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.
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