Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer.

IF 2 Q3 ONCOLOGY
Jessica Loo, Marc Robbins, Carson McNeil, Tadayuki Yoshitake, Charles Santori, Chuanhe Jay Shan, Saurabh Vyawahare, Hardik Patel, Tzu Chien Wang, Robert Findlater, David F Steiner, Sudha Rao, Michael Gutierrez, Yang Wang, Adrian C Sanchez, Raymund Yin, Vanessa Velez, Julia S Sigman, Patricia Coutinho de Souza, Hareesh Chandrupatla, Liam Scott, Shamira S Weaver, Chung-Wein Lee, Ehud Rivlin, Roman Goldenberg, Suzana S Couto, Peter Cimermancic, Pok Fai Wong
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引用次数: 0

Abstract

Significance: We extend the capabilities of virtual staining from AF to a different disease and stain modality. Our work includes newly developed virtual stains for H&E and a multiplex immunofluorescence panel (DAPI, PanCK, PD-L1, CD3, and CD8) for non-small cell lung cancer, which reproduce the key features of real stains.

自体荧光虚拟肺组织染色系统及多重免疫荧光在肺癌免疫肿瘤生物标志物研究中的应用。
数字病理学的虚拟染色在空间生物学研究、提高临床工作流程的效率和可靠性以及以非破坏性方式保存组织样本方面具有巨大的潜力。在这项研究中,我们证明了通过结合高通量高光谱荧光显微镜和机器学习,从未染色的非小细胞肺癌组织的自身荧光图像中生成苏木精和伊红(H&E)虚拟染色和多重免疫荧光(mIF)免疫肿瘤学小组(DAPI, PanCK, PD-L1, CD3, CD8)的可行性。使用特定领域的计算方法,我们评估了虚拟H&E对组织学亚型的准确性和虚拟mIF对基于细胞片段的测量的准确性,包括临床相关的测量,如肿瘤面积、T细胞密度和PD-L1表达(肿瘤比例评分和联合阳性评分)。与真实的染色相比,虚拟染色在组织和细胞水平上再现了关键的形态特征和蛋白质生物标志物的表达,能够识别对免疫肿瘤学重要的关键免疫表型,并在各种评估指标中表现出中等到良好的表现。这项研究扩展了我们之前在肝脏疾病和前列腺癌的自身荧光虚拟染色方面的工作,进一步证明了这种深度学习技术在不同疾病(肺癌)和染色模式(mIF)中的普遍性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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