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

2024-05-05

AI Summary

Manual quantification of tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) is time-consuming, subjective, and exhibits limited prognostic utility. Method: We developed a fully automated TILs quantification pipeline featuring a novel “70% irrelevant region removal + 5% patch sampling” paradigm, integrated with semi-random patch sampling, prognostic patch classification, HoVer-Net–based instance segmentation, and cell counting. Prognostic performance was rigorously validated using concordance index (c-index = 0.65) and survival analysis (p < 0.001). Contribution/Results: Compared to conventional CD8 immunohistochemistry, our method improves analytical throughput ~20-fold. TIL scores derived from the pipeline demonstrate significant association with overall survival and superior prognostic discrimination. This framework delivers an efficient, reproducible, and clinically actionable digital pathology tool for precision prognostication and personalized therapy in NSCLC.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Non-Small Cell Lung Cancer
• Tumor-Infiltrating Lymphocytes
• Personalized Treatment

Innovation

• HoVer-Net Model
• Automated TILs Quantification
• Efficient Personalized Therapy