Abstract 4167: Virtual inference of collagen architecture from H&E to characterize stromal fiber morphology and organization in colorectal cancer

Abstract Background: Collagen architecture is a key determinant of colorectal tumor progression, influencing immune exclusion, fibroblast differentiation, and tumor budding, and plays a central role in shaping the tumor microenvironment. Although image analysis algorithms exist to quantify collagen features such as fiber density, orientation, and organization, but typically require specialized stains like picrosirius red (PSR). Computational methods that infer collagen phenotypes directly from H 0.89), high-density matrix fraction (r 0.86), gap circle count (r 0.86), fiber spine area (r 0.83), coherency/anisotropy (r 0.82), fractal dimension (r 0.82), fiber angle (r 0.80), and fiber length (r 0.80). Diffusion-vPSR correlations ranked among the top 90% and exceeded pix2pix-vPSR correlations by an average Δr 0.07, with the gains observed for fiber contour- and thickness-based features, including median length (Δr 0.20), thickness variation (Δr = 0.19), and asymmetry indices (Δr 0.16). Smaller improvements were observed for fiber entropy length, variability and for skew/kurtosis/gap statistics (Δr=0.07-0.11). Pix2pix correlations surpassed diffusion correlations in only 10% of features; primarily gap fiber density features (Δr ≤ 0.05). Discussion: This study demonstrates the feasibility of generating vPSR images from routine H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4167.