用极少标注数据训练出高效肿瘤浸润淋巴细胞评估模型
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
- 基于病理基础模型提取全片图像特征，直接回归淋巴细胞分数
- 仅需数百样本训练，跨五个外部队列相关性达r=0.54-0.74
- 结果与病理科医生评分高度一致，适合临床筛查与辅助诊断
肿瘤浸润淋巴细胞（TILs）水平是乳腺癌（尤其是三阴性乳腺癌）患者的预后因子。计算病理学评估（CTA）可辅助病理科医生完成这一耗时任务，但现有模型依赖大量精细标注。我们提出并验证了一种全新的、标签高效的深度学习型CTA方法——ECTIL，可在百倍减少标注量的前提下，仅用十分钟完成训练。研究整合了来自六个队列（含三个随机对照试验）共2,340例乳腺癌患者的全切片图像（WSI）及临床数据。利用病理基础模型从WSI中提取形态特征，ECTIL直接从这些特征回归TILs评分。在仅使用几百例样本训练的ECTIL-TCGA模型中，于五个异质外部队列中与病理医生评分呈现高一致性（r=0.54–0.74，AUROC=0.80–0.94）。在整合五个队列全部数据训练的ECTIL-combined模型中，于保留测试集上表现更优（r=0.69，AUROC=0.85）。多变量Cox回归分析显示，每增加10%的ECTIL评分，总生存率提升（HR 0.86，p<0.01），独立于临床病理因素，与病理医生评分结果相似（HR 0.87，p<0.001）。结果表明，ECTIL与专家病理科医生高度一致，且具有相似的风险比。该模型设计远比现有方法简单，可实现数量级减少标注需求。此模型可用于免疫治疗临床试验患者初筛或作为乳腺癌诊疗辅助工具。代码已开源（https://github.com/nki-ai/ectil）。
原文摘要 · Abstract (English)
The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations. We collected whole slide images (WSIs) with TILs scores and clinical data of 2,340 patients with BC from six cohorts including three randomised clinical trials. Morphological features were extracted from whole slide images (WSIs) using a pathology foundation model. Our label-efficient Computational stromal TIL assessment model (ECTIL) directly regresses the TILs score from these features. ECTIL trained on only a few hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all slides of five cohorts (ECTIL-combined) improved results on a held-out test set (r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that every 10% increase of ECTIL scores was associated with improved overall survival independent of clinicopathological variables (HR 0.86, p<0.01), similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL is highly concordant with an expert pathologist and obtains a similar hazard ratio. ECTIL has a fundamentally simpler design than existing methods and can be trained on orders of magnitude fewer annotations. Such a CTA may be used to pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a tool to assist clinicians in the diagnostic work-up of patients with BC. Our model is available under an open source licence (https://github.com/nki-ai/ectil).
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