  Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue
indicate favourable outcomes in many types of cancer. Manual quantification of
immune cells is inaccurate and time consuming for pathologists. Our aim is to
leverage a computational solution to automatically quantify TILs in whole slide
images (WSIs) of standard diagnostic haematoxylin and eosin stained sections
(H&E slides) from lung cancer patients. Our approach is to transfer an open
source machine learning method for segmentation and classification of nuclei in
H&E slides trained on public data to TIL quantification without manual labeling
of our data. Our results show that additional augmentation improves model
transferability when training on few samples/limited tissue types. Models
trained with sufficient samples/tissue types do not benefit from our additional
augmentation policy. Further, the resulting TIL quantification correlates to
patient prognosis and compares favorably to the current state-of-the-art method
for immune cell detection in non-small lung cancer (current standard CD8 cells
in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net
PanNuke Aug Model HR 0.30 95% CI 0.15-0.60, HoVer-Net MoNuSAC Aug model HR 0.27
95% CI 0.14-0.53). Moreover, we implemented a cloud based system to train,
deploy and visually inspect machine learning based annotation for H&E slides.
Our pragmatic approach bridges the gap between machine learning research,
translational clinical research and clinical implementation. However,
validation in prospective studies is needed to assert that the method works in
a clinical setting.
