Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.
Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.
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DOI：
10.1038/s41598-018-35501-5
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发表时间：
2018-11-26
影响因子：
4.6
通讯作者：
Brieu N
中科院分区：
文献类型：
--
作者：
Kapil A;Meier A;Zuraw A;Steele KE;Rebelatto MC;Schmidt G;Brieu N
免疫组织化学（IHC）检测中的PD-L1表达水平是识别可能对抗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病理学家对显示PD-L1染色的肿瘤细胞百分比（肿瘤比例评分或TPS）的视觉估计。已知的挑战，如围绕临床相关临界值的阳性估计的差异和样本的次优质量，使得视觉评分繁琐且主观，从而在病理学家之间产生评分差异。在这项工作中，我们提出了一种新的深度学习解决方案，可以在晚期NSCLC针吸活检中首次自动和客观地对PD-L1表达进行评分。考虑到活检图像中可用的组织数量较少，并限制训练所需的手动注释量，我们探索了使用半监督方法来对抗标准的全监督方法。我们整合了用于培训的手动注释以及用于多位病理学家定量评估的可视化TPS评分。在训练过程中看不见的一组幻灯片上计算的一致性指标提供了证据，证明我们的自动评分方法与所考虑数据集的视觉评分相匹配，同时确保了可重复性和客观性。
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 by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. 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 TPS 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.
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作者：
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