Introduction/Objective: Retinal fundus photography captures microvascular and neuroretinal features reflecting systemic health. While Deep Learning (DL) has enabled extraction of cardiometabolic and aging biomarkers from retinal images, its application to oncologic disease status discrimination remains largely unexplored. This study evaluated whether DL analysis of retinal fundus photographs can discriminate individuals with colorectal cancer (CRC) from those without CRC (non-CRC) in a curated UK Biobank case-control setting. Methods: This retrospective imaging study used 1,600 UK Biobank fundus photographs (800 CRC, 800 non-CRC). Images underwent grayscale conversion, intensity normalization, and resizing with minimal preprocessing. Data were split at the participant level into training, validation, and test sets (70/15/15). An ImageNet-pretrained EfficientNet-B3 convolutional neural network was fine-tuned end-to-end for binary classification. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUROC). Prevalence-adjusted predictive values were estimated to approximate real-world classification performance. Results: On the independent test set, the model achieved AUROC = 0.945, accuracy = 87.08%, macro-F1 = 87.07%, sensitivity = 90.00%, specificity = 84.17%, and precision (PPV in the balanced test set) = 85.04%. When recalibrated to population prevalence, PPV decreased to 2.8% at 0.5% prevalence (and 6.5% at 1.2% prevalence), whereas NPV remained high (99.94% and 99.86%, respectively). Discussion: High discrimination achieved from retinal images alone supports the presence of CRC-associated retinal signatures. A covariates-only baseline using age and vascular/heart disease showed only modest discrimination (AUROC ≈ 0.63), making measured confounders an unlikely sole explanation for model performance, although the cross-sectional case-control design and possible residual confounding preclude causal or prediagnostic claims. Together with the low prevalence-adjusted PPV and high NPV, these findings position retinal analysis as a potential triage or rule-out tool rather than a stand-alone diagnostic classifier. Conclusion: DL analysis of retinal fundus photographs can discriminate CRC status with high accuracy in a case-control setting, supporting the presence of CRC-associated retinal signatures. Prevalence-adjusted projections indicate limited stand-alone screening utility, positioning retinal AI analysis primarily as a potential triage or risk-enrichment tool pending longitudinal validation.