Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer

TL;DR

This paper presents an automated, efficient pipeline for quantifying tumor-infiltrating lymphocytes in non-small cell lung cancer, improving prognostic accuracy while reducing manual effort and variability.

Contribution

The study introduces a semi-stochastic patch sampling and classification pipeline combined with cell quantification that streamlines TIL assessment in NSCLC, outperforming traditional methods.

Findings

Excludes 70% of irrelevant areas for prognosis

Uses only 5% of remaining patches to maintain accuracy

Achieves a prognostic c-index of 0.65, surpassing traditional scoring methods

Abstract

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). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score's strong association with patient survival, which outperforms 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.