  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.
