用QuPath实现乳腺癌病理图像自动评估肿瘤浸润淋巴细胞
Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
- 基于像素分类与深度学习模型分割组织区域并检测细胞
- 自动计算淋巴细胞密度，外部测试集一致性达0.71（Cohen's kappa）
- 开源工具链，适合临床研究与病理实验室快速部署
本研究在QuPath中构建了一个端到端的肿瘤浸润淋巴细胞（TILs）评估流程，展示了通用工具实现复杂任务自动化的潜力。首先，训练了像素级分类器以分割乳腺癌H&E染色全幻灯片图像（WSI）中的肿瘤、肿瘤相关间质及其他组织区域，用于后续分析。接着，在QuPath中应用预训练的StarDist深度学习模型进行细胞检测，并利用提取的细胞特征训练二分类器以区分TILs与其他细胞。为评估该流程，计算每张WSI的TIL密度并划分为低、中、高水平。在外部测试集上，该方法与病理科医生评分的Cohen's kappa值达到0.71，验证了先前研究结果。结果表明，现有软件可为乳腺癌H&E-WSI中的TIL评估提供实用解决方案。
原文摘要 · Abstract (English)
In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs) assessment pipeline within QuPath, demonstrating the potential of easily accessible tools to perform complex tasks in a fully automatic fashion. First, we trained a pixel classifier to segment tumor, tumor-associated stroma, and other tissue compartments in breast cancer H&E-stained whole-slide images (WSI) to isolate tumor-associated stroma for subsequent analysis. Next, we applied a pre-trained StarDist deep learning model in QuPath for cell detection and used the extracted cell features to train a binary classifier distinguishing TILs from other cells. To evaluate our TILs assessment pipeline, we calculated the TIL density in each WSI and categorized them as low, medium, or high TIL levels. Our pipeline was evaluated against pathologist-assigned TIL scores, achieving a Cohen's kappa of 0.71 on the external test set, corroborating previous research findings. These results confirm that existing software can offer a practical solution for the assessment of TILs in H&E-stained WSIs of breast cancer.
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