We developed a computational pathology pipeline to extract and analyze collagen disorder architecture (CoDA) features from whole slide images (WSIs) of 2,212 colon cancer (CC) patients across multiple institutions. CoDA features-capturing collagen fragmentation, bundling, anisotropy, density, and rigidity, were evaluated for associations with clinical variables (overall stage, T/N/M stage), molecular classifications (Consensus Molecular Subtypes [CMS1-4]), and genetic mutations (KRAS, BRAF, NRAS) using the Mann-Whitney U test with Bonferroni correction. These analyses revealed significant differences in CoDA feature distributions across multiple subgroups, suggesting that collagen architecture varies meaningfully with tumor stage, molecular subtype, and mutation status.To assess how well CoDA features could distinguish between these subgroups, we implemented a Random Forest classification framework. High mean AUC values (≥0.7) across several variables indicated strong discriminatory performance of CoDA features in separating clinically and biologically distinct groups.For survival analysis, LASSO-Cox models were trained on the PLCO dataset to generate CoDA-based risk scores for overall survival (OS) and disease-free survival (DFS), which were used to stratify patients into high- and low-risk groups in a combined validation dataset (TCGA, UH, and Emory). Kaplan-Meier curves demonstrated significant survival differences across clinical stages, CMS subtypes, and KRAS mutation status. Multivariable Cox proportional hazards models further confirmed the independent prognostic value of CoDA features after adjusting for clinical, molecular, and genetic covariates. These findings highlight that CoDA features are significantly associated with key clinical and molecular characteristics, can distinguish relevant patient subgroups, and offer independent prognostic information, underscoring their potential utility in characterizing the tumor microenvironment and informing risk stratification in CC.