Microenvironmental information significantly improves the recognition of cell types in human lung cancer patients

Accurate single-cell phenotypic classification in histopathological tissue sections is essential for understanding tumor behavior, identifying potential therapeutic targets, and improving prognostic assessments in cancer research. In this study, we applied deep learning to classify lung cancer cell phenotypes in hematoxylin and eosin-stained tissue sections. Using 11 whole slide images from 11 patients, we annotated nearly 20,000 cells into seven distinct phenotypes for training and validation. We used a fisheye transformation technique, which modifies images to mimic fisheye camera effects in order to incorporate cellular microenvironment information to enhance deep learning models. We evaluated its effectiveness on lung cancer tissue sections, optimizing transformation parameters and assessing classification performance through multiple cross-validation strategies. Our results demonstrate that the transformation significantly improves classification accuracy, approaching human level performance, particularly for phenotypes that rely on subtle morphological differences. The approach enhances model generalizability across patient samples, highlighting the importance of integrating spatial context in computational pathology. These findings suggest that incorporating adaptive image transformations can significantly improve automated histopathological analysis, with potential implications for more robust and clinically applicable AI-driven diagnostics.