Feed

The extracellular matrix (ECM), a complex network of proteins and carbohydrates, regulates key cellular and developmental processes. While computational methods for characterising collagen topology are well-established, the organisation of fibronectin (FN), another vital ECM protein, remains comparatively underexplored. FN's more intricate structure and thinner fibrillar arrays make existing collagen-based methods less effective for its analysis. This work aims to lay the groundwork for studying clinical tumour images from head and neck cancer patients, with the goal of integrating it into a broader multimodal framework to predict resistance to immunotherapy.Our approach leverages handcrafted feature extraction combined with standard machine learning algorithms to identify key discriminative statistical measures distinguishing between FN assembled by control fibroblasts and tumour-like fibroblasts. These include the alignment index, closeness and betweenness centrality measures derived from graph representations, and branch length. Despite the success of state-of-the-art (SOTA) methods in other domains, we demonstrate that our handcrafted feature-based approach achieves competitive performance on our dataset. Our results demonstrate that domain-specific feature engineering can effectively complement SOTA techniques, especially in biomedical applications where interpretability is crucial.