论文标题
一种务实的机器学习方法，用于量化整个幻灯片图像中的肿瘤浸润淋巴细胞
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
论文作者
论文摘要
癌症组织中肿瘤浸润淋巴细胞（TIL）的水平升高表明许多类型的癌症的结果。免疫细胞的手动量化是不准确的，并且对于病理学家而言是不准时的。我们的目的是利用一种计算解决方案来自动量化来自肺癌患者的标准诊断血久毒素和曙红染色切片（H＆E幻灯片）的整个幻灯片图像（WSI）。我们的方法是转移一种开源机器学习方法，用于对经过公共数据训练的H＆E幻灯片中的核分割和分类，以直接定量，而无需手动标记我们的数据。我们的结果表明，当在几个样品/有限的组织类型上训练时，额外的增强可提高模型的转移性。接受足够样品/组织类型训练的模型并不能从我们的其他增强政策中受益。 Further, the resulting TIL quantification correlates to patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small lung cancer (current standard CD8 cells in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30 95% CI 0.15-0.60, HoVer-Net Monusac Aug HR 0.27 95％CI 0.14-0.53）。此外，我们实施了一个基于云的系统，以训练，部署和视觉检查基于机器学习的H＆E幻灯片的注释。我们的务实方法弥合了机器学习研究，翻译临床研究和临床实施之间的差距。但是，需要在前瞻性研究中进行验证，以断言该方法在临床环境中起作用。
Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in whole slide images (WSIs) of standard diagnostic haematoxylin and eosin stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that additional augmentation improves model transferability when training on few samples/limited tissue types. Models trained with sufficient samples/tissue types do not benefit from our additional augmentation policy. Further, the resulting TIL quantification correlates to patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small lung cancer (current standard CD8 cells in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30 95% CI 0.15-0.60, HoVer-Net MoNuSAC Aug model HR 0.27 95% CI 0.14-0.53). Moreover, we implemented a cloud based system to train, deploy and visually inspect machine learning based annotation for H&E slides. Our pragmatic approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, validation in prospective studies is needed to assert that the method works in a clinical setting.
