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Automated nuclear segmentation in head and neck squamous cell carcinoma (HNSCC) pathology reveals relationships between cytometric features and ESTIMATE stromal and immune scores

      ABSTRACT

      The tumor microenvironment (TME) plays an important role in the progression of head and neck squamous cell carcinoma (HNSCC). Currently, pathological assessment of TME is non-standardized and subject to observer bias. Genome-wide transcriptomic approaches to understanding the TME, while less subject to bias, are expensive and not currently part of standard of care for HNSCC. To identify pathology-based biomarkers that correlate with genomic and transcriptomic signatures of TME in HNSCC, cytometric feature maps were generated in a publicly available cohort of patients with HNSCC with available whole-slide tissue images and genomic and transcriptomic phenotyping (N=49). Cytometric feature maps were generated based on whole-slide nuclear detection, using a deep learning algorithm trained for StarDist nuclear segmentation. Cytometric features were measured for each patient and compared to transcriptomic measurements, including Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) scores, as well as stemness scores. When corrected for multiple comparisons, one feature (nuclear circularity) demonstrated a significant linear correlation with ESTIMATE stromal score. Two features (nuclear maximum and minimum diameter) correlated significantly with ESTIMATE immune score. Three features (nuclear solidity, nuclear minimum diameter, and nuclear circularity) correlated significantly with transcriptomic stemness score. This study provides preliminary evidence that observer-independent, automated tissue slide analysis can provide insights into the HNSCC TME which correlate with genomic and transcriptomic assessments.
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