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首页> 外文会议>International Molecular Medicine Tri-Conference. >Development and Validation of a Deep Learning Algorithm for PD-L1 Scoring in Tumour Cells and Immune Cells
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Development and Validation of a Deep Learning Algorithm for PD-L1 Scoring in Tumour Cells and Immune Cells

机译：肿瘤细胞和免疫细胞PD-L1评分深度学习算法的开发与验证

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摘要

Treatment decisions in oncology are commonly informed by the visual assessment of immunohistochemistry (IHC) biomarkers (such as PD-L1 expression) by pathologists. However, pathology services face mounting pressure as diagnostic demand increases and workforce decreases. Digital pathology and artificial intelligence have the potential to streamline the diagnostic workflow thereby improving pathologists' workload, accelerating turn-around-times and facilitating access to testing. Here, we report the in-house development and analytical validation of a deep learning algorithm for automated scoring of PD-L1 expression in samples processed with the VENTANA PD-L1 (SP263) Assay. The algorithm was trained to score PD-L1 expression in tumour cells and in immune cells using 29318 manually annotated cells across a set of 150 PD-L1 IHC images from 30 urothelial carcinoma (UC) samples. The algorithm was then validated in an independent cohort of UC samples. In the validation cohort, the algorithm demonstrated high inter-scan reproducibility (99% overall percent agreement, N=197), high inter-scanner reproducibility (100% overall percent agreement, N=33) and substantial agreement with pathologist-based scoring of PD-L1 expression (84% overall percent agreement, N=195).
机译：在肿瘤学的治疗决定通常是通过由病理学家免疫组织化学的视觉评估（IHC）的生物标志物（诸如PD-L1表达）通知。然而，病理学服务面临越来越大的压力作为诊断需求的增加和劳动力减少。数字病理学和人工智能必须简化工作流程诊断从而改善病理学家的工作量，加快周转倍和促进对测试接入的潜力。这里，我们报告与VENTANA PD-L1（SP263）测定处理的样本为PD-L1表达的自动评分的内部开发和分析验证了深刻的学习算法的。该算法被训练得分的肿瘤细胞，然后使用在一组150 PD-L1 IHC图像29318个手动注释细胞从30尿路上皮癌（UC）的样品的免疫细胞PD-L1表达。然后该算法在UC样品的独立队列验证。在验证队列中，该算法表现出高跨扫描再现性（99％总百分比协议，N = 197），高扫描器间再现性（100％总百分比协议，N = 33）和大量的协议与基于病理学家得分PD-L1表达（84％总百分比协议，N = 195）。

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