Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework

Abstract

Abstract Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility. Only a few studies have been conducted in automating TILs quantification for OSCC, existing methods use score-based systems that focus only on tissue-level spatial analysis, overlooking essential cellular-level information and do not provide TILs infiltration subcategories required for determining OSCC grading. To address these limitations, we propose OralTILs-ViT, a novel joint representation learning framework that integrates cellular and tissue-level information. Our model employs two parallel encoders: one extracts cellular features from cellular density maps, while the other processes tissue features from H&E-stained tissue images. This dual-encoder approach enables OralTILs-ViT to capture complex tissue-cellular interactions, classifying TILs infiltration categories consistent with Broders’ grading system-“Moderate to Marked”, “Slight” and “None to Very Less.” This approach reflects pathology practices and increases TILs classification accuracy. To generate cellular density maps, we introduce TILSeg-MobileViT, a multiclass segmentation model trained using a weakly supervised method, minimizing the need for manual annotation of cellular masks and overcoming the limitations of previous TILs assessment techniques. An extensive evaluation of our methodology demonstrates that OralTILs-ViT with the configuration (Adam, $$\alpha$$ α = 0.001) outperforms existing approaches, achieving 96.37% accuracy, 96.34% precision, 96.37% recall, and a 96.35% F1 score. Furthermore, TOPSIS analysis confirms that our method ranks first across all TILs infiltration categories. In summary, our proposed methodology outperforms single modality-representation learning approaches for accurate and automated TILs classification.