Ahmed Irfan, Zhang Wei, Cheung Pikting, Basnet Vardhan, Ali Zulfiqar, Tse May Py, Hill Fraser, Chan Tom Tak Lam, Hu Haibo, Li Xinyue, Lau Condon
Department of Physics, City University of Hong Kong, Hong Kong SAR, China.
Centre for Advances in Reliability and Safety, Hong Kong SAR, China.
Diagn Pathol. 2025 Jul 17;20(1):86. doi: 10.1186/s13000-025-01687-2.
Presently, pathologists need to stain biopsy samples with standard and antibody-based immunocytochemistry (ICC) reagents for final diagnosis. Antibody reagents take hours to days to perform staining, along with requiring specialized equipment and technical skills. We have developed an AI-based virtual ICC platform that measures individual cell morphological features in whole slide images and labels the cells as immuno-positive or negative. The platform runs on the cloud in minutes, saving pathologists significant time and cost. For this purpose, cytopathology slides were obtained from N = 100 suspected cases of canine T-cell and B-cell lymph node lymphomas through Fine Needle Aspiration (FNA). Cytopathology slides were initially stained with the standard Wright-Giemsa (WG) and then re-stained with ICC reagents, anti-CD3 or anti-PAX5 antibodies, resulting in a pair of stained slides (WG-CD3 or WG-PAX5). Prior to AI training, cytopathology slides were digitally scanned, and the resulting images underwent a comprehensive pre-processing protocol to separate stains of interest for nuclei segmentation in WG and CD3 or PAX5. Following nuclei segmentation, the cell features from processed image pairs were translated into a structured tabular features format with immuno-positive and negative labeled classes. In total, the geometrical features of 8.48 million segmented cells (4.24 million pairs) were translated into a tabular format and paired based on the Euclidean cell matching algorithm. This approach facilitated the prediction of cell labels, achieving sensitivity and specificity of 0.98 and 0.97 (0.94 and 0.99), respectively for CD3 (PAX5). Additionally, the AI-based virtual ICC has demonstrated capabilities in cell counting, cell spatial distribution, cell segmentation, and classification. It offers a rapid, accurate, and precise evaluation of FNA samples and has the potential to help advance diagnostic cellular and molecular pathology capabilities.
目前，病理学家需要使用标准试剂和基于抗体的免疫细胞化学（ICC）试剂对活检样本进行染色，以做出最终诊断。抗体试剂进行染色需要数小时至数天时间，同时还需要专门的设备和技术技能。我们开发了一个基于人工智能的虚拟ICC平台，该平台可测量全切片图像中单个细胞的形态特征，并将细胞标记为免疫阳性或阴性。该平台在云端几分钟内即可运行，为病理学家节省了大量时间和成本。为此，通过细针穿刺抽吸（FNA）从100例疑似犬T细胞和B细胞淋巴结淋巴瘤病例中获取了细胞病理学切片。细胞病理学切片最初用标准的瑞氏-吉姆萨（WG）染色，然后用ICC试剂、抗CD3或抗PAX5抗体重新染色，得到一对染色切片（WG-CD3或WG-PAX5）。在进行人工智能训练之前，对细胞病理学切片进行数字扫描，然后对所得图像进行全面的预处理方案，以分离用于WG和CD3或PAX5中细胞核分割的感兴趣染色。细胞核分割后，将处理后的图像对中的细胞特征转化为具有免疫阳性和阴性标记类别的结构化表格特征格式。总共，848万个分割细胞（424万对）的几何特征被转化为表格格式，并根据欧几里得细胞匹配算法进行配对。这种方法有助于细胞标签的预测，对于CD3（PAX5）分别实现了0.98和0.97（0.94和0.99）的灵敏度和特异性。此外，基于人工智能的虚拟ICC在细胞计数、细胞空间分布、细胞分割和分类方面已展现出能力。它能对FNA样本进行快速、准确和精确的评估，并有潜力帮助提升诊断细胞和分子病理学能力。
