Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images

Oral dysplasia is a pre-malignant stage of oral epithelial carcinomas, e.g., oral squamous cell carcinoma, where significant changes in tissue layers and cells can be observed under the microscope. However, malignancy can be reverted or cured using proper medication or surgery if the grade of malignancy is assessed properly. The assessment of correct grade is therefore critical in patient management as it can change the treatment decisions and prognosis for the dysplastic lesion. This assessment is highly challenging due to considerable inter- and intraobserver variability in pathologists’ agreement, which highlights the need for an automated grading system that can predict more accurate and reliable grade. Recent advancements have made it possible for digital pathology (DP) and artificial intelligence (AI) to join forces from the digitization of tissue slides into images and using those images to train and predict more accurate grades using complex AI models. In this regard, we propose a novel morphometric approach exploiting the architectural features in dysplastic lesions i.e., irregular epithelial stratification where we measure the widths of different layers of the epithelium from the boundary layer i.e., keratin projecting inwards to the epithelium and basal layers to the rest of the tissue section from a clinically significant viewpoint

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

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 was 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, tumour-associated 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 tumour-associated stroma infiltrating lymphocytes score (TASIL-score). Finally, the prognostic significance of the TASIL-score for disease-specific and disease-free survival was investigated using the Cox proportional hazard analysis. Three different cohorts of haematoxylin and 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 compared with the manual tumour-infiltrating lymphocytes (TILs) 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 TASILs 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. Although we validated our method on three different cohorts (n = 537 cases in total), a comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice