Introduction: Multiplex biomarker analysis of cytological body fluid specimens is often used to assist cytologists in distiguishing metastatic cancer cells from reactive mesothelial cells. However, evaluating biomarker expression visually may be challenging, especially when the cells of interest are scant. Deep-learning algorithms (DLAs) may be able to assist cytologists in analyzing multiple biomarker expression at the single cell level in the multiplex fluorescence imaging (MFI) setting. This preliminary study was performed to test the feasibility of using DLAs to identify immunofluorescence-stained metastatic adenocarcinoma cells in body fluid cytology samples. Methods: A DLA was developed to analyze MFI-stained cells in body fluid cytological samples. A total of 41 pleural fluid samples, comprising of 20 positives and 21 negatives, were retrospectively collected. Multiplex immunofluorescence labeling for MOC31, BerEP4, and calretinin, were performed on cell block sections, and results were analyzed by manual analysis (manual MFI) and DLA analysis (MFI-DLA) independently. Results: All cases with positive original cytological diagnoses showed positive results either by manual MFI or MFI-DLA, but 2 of the 14 (14.3%) original cytologically negative cases had rare cells with positive MOC31 and/or BerEP4 staining in addition to calretinin. Manual MFI analysis and MFI-DLA showed 100% concordance. Conclusion: MFI combined with DLA provides a potential tool to assist in cytological diagnosis of metastatic malignancy in body fluid samples. Larger studies are warranted to test the clinical validity of the approach.
Acta Cytologica

中文翻译：

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用于分析体液细胞学标本中多重免疫荧光生物标志物的深度学习算法的初步研究

简介：细胞学体液标本的多重生物标志物分析通常用于帮助细胞学家区分转移性癌细胞和反应性间皮细胞。然而，在视觉上评估生物标志物的表达可能具有挑战性，尤其是当感兴趣的细胞很少时。深度学习算法 (DLA) 可能能够帮助细胞学家在多重荧光成像 (MFI) 环境中分析单细胞水平的多种生物标志物表达。进行这项初步研究是为了测试使用 DLA 识别体液细胞学样本中免疫荧光染色的转移性腺癌细胞的可行性。方法：开发了 DLA 来分析体液细胞学样本中 MFI 染色的细胞。回顾性收集了总共 41 份胸水样本，包括 20 份阳性和 21 份阴性。MOC31、BerEP4和calretinin的多重免疫荧光标记在细胞块切片上进行，结果分别通过手动分析（manual MFI）和DLA分析（MFI-DLA）进行分析。结果：所有原始细胞学诊断阳性的病例均通过手动 MFI 或 MFI-DLA 显示阳性结果，但 14 例（14.3%）原始细胞学阴性病例中的 2 例（14.3%）具有除钙视网膜蛋白外 MOC31 和/或 BerEP4 染色阳性的罕见细胞。手动 MFI 分析和 MFI-DLA 显示 100% 的一致性。结论：MFI 与 DLA 相结合提供了一种潜在的工具，可帮助对体液样本中的转移性恶性肿瘤进行细胞学诊断。需要进行更大规模的研究来测试该方法的临床有效性。
细胞学学报