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A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

arXiv 2104.12862 ↗First posted 16 Apr 2021eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method. A deep learning based automated method was employed to segment tumour, stroma and lymphocytes in digitally scanned whole slide images of HNSCC tissue slides. The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the TASIL-score. Finally, prognostic significance of the TASIL-score for disease-specific and disease-free survival was investigated with the Cox proportional hazard analysis. Three different cohorts of Haematoxylin & Eosin (H&E) stained tissue slides of HNSCC cases (n=537 in total) were studied, including publicly available TCGA head and neck cancer cases. The TASIL-score carries prognostic significance (p=0.002) for disease-specific survival of HNSCC patients. The TASIL-score also shows a better separation between low- and high-risk patients as compared to the manual TIL scoring by pathologists for both disease-specific and disease-free survival. A positive correlation of TASIL-score with molecular estimates of CD8+ T cells was also found, which is in line with existing findings. To the best of our knowledge, this is the first study to automate the quantification of TASIL from routine H&E slides of head and neck cancer. Our TASIL-score based findings are aligned with the clinical knowledge with the added advantages of objectivity, reproducibility and strong prognostic value. A comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 37 references that are in the map. First posted on arXiv 16 Apr 2021.

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4 of 37 references.

References · 4

  1. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  2. 2020Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotationscited by 89
  3. 2019Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Imagescited by 39
  4. 2018Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Featurescited by 28

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Citing papers per year: 2021: 1; 2022: 0; 2023: 2.

3 papers.