Specific and effective breast cancer therapy relies on the accurate quantification of PD-L1 positivity in tumors, which appears in the form of brown stainings in high resolution whole slide images (WSIs). However, the retrieval and extensive labeling of PD-L1 stained WSIs is a time-consuming and challenging task for pathologists, resulting in low reproducibility, especially for borderline images. This study aims to develop and compare models able to classify PD-L1 positivity of breast cancer samples based on WSI analysis, relying only on WSI-level labels. The task consists of two phases: identifying regions of interest (ROI) and classifying tumors as PD-L1 positive or negative. For the latter, two model categories were developed, with different feature extraction methodologies. The first encodes images based on the colour distance from a base color. The second uses a convolutional autoencoder to obtain embeddings of WSI tiles, and aggregates them into a WSI-level embedding. For both model types, features are fed into downstream ML classifiers. Two datasets from different clinical centers were used in two different training configurations: (1) training on one dataset and testing on the other; (2) combining the datasets. We also tested the performance with or without human preprocessing to remove brown artefacts Colour distance based models achieve the best performances on testing configuration (1) with artefact removal, while autoencoder-based models are superior in the remaining cases, which are prone to greater data variability.

翻译：针对乳腺癌的特异性有效治疗依赖于肿瘤中PD-L1阳性表达的准确定量，这种表达在高分辨率全切片图像中表现为棕色染色区域。然而，获取并对PD-L1染色的WSI进行广泛标注对病理学家而言是一项耗时且具有挑战性的任务，导致可重复性较低，尤其对于边界图像。本研究旨在开发并比较基于WSI分析、仅依赖WSI级别标签对乳腺癌样本进行PD-L1阳性分类的模型。任务包含两个阶段：识别感兴趣区域以及将肿瘤分类为PD-L1阳性或阴性。针对后者，我们开发了两类采用不同特征提取方法的模型。第一类基于图像与基准色的色差进行编码；第二类使用卷积自编码器获取WSI图块的嵌入向量，并将其聚合为WSI级别嵌入。对于两类模型，特征均输入下游机器学习分类器。我们使用了来自不同临床中心的两个数据集，在两种训练配置下进行实验：(1) 在一个数据集上训练并在另一个数据集上测试；(2) 合并数据集。同时，我们还测试了有无人工预处理去除棕色伪影时的性能。基于色差的模型在测试配置(1)且去除伪影时表现最佳，而基于自编码器的模型在其余更易受数据变异性影响的情况下表现更优。