  Addressing the critical need for accurate prognostic biomarkers in cancer
treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell
lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification
in whole slide images (WSIs) is laborious and subject to variability,
potentially undermining patient outcomes. Our study introduces an automated
pipeline that utilizes semi-stochastic patch sampling, patch classification to
retain prognostically relevant patches, and cell quantification using the
HoVer-Net model to streamline the TIL evaluation process. This pipeline
efficiently excludes approximately 70% of areas not relevant for prognosis and
requires only 5% of the remaining patches to maintain prognostic accuracy
(c-index 0.65 +- 0.01). The computational efficiency achieved does not
sacrifice prognostic accuracy, as demonstrated by the TILs score's strong
correlation with patient survival, which surpasses traditional CD8 IHC scoring
methods. While the pipeline demonstrates potential for enhancing NSCLC
prognostication and personalization of treatment, comprehensive clinical
validation is still required. Future research should focus on verifying its
broader clinical utility and investigating additional biomarkers to improve
NSCLC prognosis.
