Abstract 6316: Predictive performance comparison of foundational and CNN models for single-cell immune profiling
Authors: Colwyn Jia Kang Lai, Wei Kit Tan, Marcia Zhang, Timothy Wang, Felicia Wee, Ai Lin Wang, Solomonraj Wilson, Tomotaka Ugai, Joe Sheng Yeong Poh, Jonathan A. Nowak, Shuji Ogino, Juha P. Väyrynen, Mai Chan Lau
Journal: Cancer Research (2025)

Abstract Introduction: Characterizing the immune contexture is key to understanding the tumor microenvironment, developing biomarkers, and guiding therapeutic strategies. In our previous work on colorectal cancer (CRC), we used a pathologist-guided approach to annotate/count eosinophils and lymphocytes. Their abundance and spatial relationships with tumor cells revealed prognostic significance. While clinically valuable, identifying this demands significant pathologist effort. Advancements in AI, particularly foundational models trained on millions of histology images, have driven significant progress in digital pathology and hold promise to address this gap. However, their application to single-cell tasks remains underexplored. This study investigates the potential of foundation model and compares it with conventional convolutional neural network (CNN) using previously annotated eosinophils and lymphocytes in CRC histology images. Methods: We used 578 CRC cases in The Cancer Genome Atlas with H&amp;E-stained whole-slide images (WSIs) containing 8, 836, 393 cells, including 896, 322 lymphocytes and eosinophils. For evaluation, 109 WSIs were held out as a test set, with 5 WSIs per density quartile selected from our previous study. The foundational model UNI (11.2M parameters) was fine-tuned and a ResNet18 (303M parameters) model was trained. Performance was evaluated using F1-score and AUROC, with Spearman’s correlation accessing WSI-level cell density correlations. Results: For eosinophil, ResNet18 achieved an F1-score of 0.841, AUROC of 0.986, and Spearman's correlation of 0.679, compared to UNI’s 0.847, 0.990, and 0.697, respectively. For lymphocytes, ResNet18 scored 0.697, 0.932, and 0.686, while UNI achieved 0.719, 0.941, and 0.662. ResNet18 performed comparably to UNI despite simpler architecture, at both cell and patient levels. Predicted cell densities generally aligned with the annotated quartiles from previous work, particularly in the 1st and 4th quartiles. Moderate density correlations (0.66-0.69) were observed, but a pathologist's review of misclassified cells revealed imperfections in the semi-supervised cell annotations. Conclusion: The H&amp;E-based AI model for predicting immune cells offers a scalable alternative to manual annotation, enabling automated quantification of existing immune biomarkers like tumor-infiltrating lymphocytes and tumor-associated tissue eosinophilia. Adaptable to other tumor types, it also opens opportunities to discover novel spatial immune biomarkers by leveraging large H&amp;E cohorts and integrating protein-stained immune cell data with histopathological knowledge. Future work will focus on enhancing performance through weakly supervised approaches using carefully selected training cells, and multi-cell type modeling to better capture the morphological characteristics of diverse cell types in a single model. Citation Format: Colwyn Jia Kang Lai, Wei Kit Tan, Marcia Zhang, Timothy Wang, Felicia Wee, Ai Lin Wang, Solomonraj Wilson, Tomotaka Ugai, Joe Sheng Yeong Poh, Jonathan A. Nowak, Shuji Ogino, Juha P. Väyrynen, Mai Chan Lau. Predictive performance comparison of foundational and CNN models for single-cell immune profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6316.