  Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognostic
values in cancers. However, very few automated, deep-learning-based TIL scoring
algorithms have been developed for colorectal cancers (CRC). Methods We
developed an automated, multiscale LinkNet workflow for quantifying
cellular-level TILs for CRC tumors using H&E-stained images. The predictive
performance of the automatic TIL scores (TIL) for disease progression and
overall survival was evaluate using two international datasets, including 554
CRC patients from The Cancer Genome Atlas (TCGA) and 1130 CRC patients from
Molecular and Cellular Oncology (MCO). Results The LinkNet model provided an
outstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347).
Clear dose-response relationships were observed between TILs and risk of
disease progression or death decreased in both TCGA and MCO cohorts. Both
univariate and multivariate Cox regression analyses for the TCGA data
demonstrated that patients with high TILs had significant (approx. 75%)
reduction of risk for disease progression. In both MCO and TCGA studies, the
TIL-high group was significantly associated with improved overall survival in
univariate analysis (30% and 54% reduction in risk, respectively). However,
potential confounding was observed in the MCO dataset. The favorable effects of
high TILs were consistently observed in different subgroups according to know
risk factors. Conclusion A deep-learning workflow for automatic TIL
quantification based on LinkNet was successfully developed.
