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Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies

2018-06-28 15:30:29
Ansh Kapil, Armin Meier, Aleksandra Zuraw, Keith Steele, Marlon Rebelatto, Günter Schmidt, Nicolas Brieu


The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation of a Tumor Cell (TC) score by a pathologist and consists of evaluating the ratio of PD-L1 positive and PD-L1 negative tumor cells. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TC scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

Abstract (translated)

免疫组织化学(IHC)测定中PD-L1表达水平是鉴定可能对抗PD-1 / PD-L1治疗有反应的非小细胞肺癌(NSCLC)患者的关键生物标志物。 PD-L1表达的定量目前包括由病理学家对肿瘤细胞(TC)评分的视觉估计,并且包括评估PD-L1阳性和PD-L1阴性肿瘤细胞的比率。已知的挑战如临床相关临界点附近的积极性评估差异和样品的次优质量使得视觉评分冗长和主观,产生病理学家之间的评分变异性。在这项工作中,我们提出了一种新型的深度学习解决方案,能够在晚期NSCLC穿刺活检中首次实现PD-L1表达的自动客观评分。为了解释活组织图像中可用的组织量较少并限制培训所需的手动注释的数量,我们探讨了在标准完全监督方法中使用半监督方法。我们整合了用于培训的手册注释以及用于与多位病理学家进行定量评估的视觉TC分数。通过在训练期间看不到的一组幻灯片计算出的一致性度量提供了证据,证明我们的自动评分方法与所考虑数据集的视觉评分匹配,同时确保可重复性和客观性。



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