Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer
This work addresses the need for efficient and accurate prognostic biomarkers in non-small cell lung cancer treatment, though it is incremental as it builds on existing models like HoVer-Net.
The study tackled the challenge of laborious and variable manual quantification of tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer by developing an automated pipeline that excludes 70% of non-prognostic areas and uses only 5% of patches to achieve a prognostic accuracy with a c-index of 0.65, outperforming traditional methods.
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.