  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.
