Work

Neurovascular Retinomics for Noninvasive Profiling of Cancer Risk

Year: 2026

Type: article

Abstract: Purpose To comprehensively evaluate the predictive value of neurovascular retinal measurements for incident cancer. Design Population-based, prospective cohort study. Participants 41,424 participants with eligible color fundus photography and optical coherence tomography data from the UK Biobank. Methods Retinomics were derived from multimodal retinal imaging of the UK Biobank, comprising 156 retinal features. Four Cox regression models were developed: Model 1 (age and sex), Model 2 (Model 1 + ethnicity and lifestyle factors), Model 3 (Model 1 + retinomics), and Model 4 (full model). Least Absolute Shrinkage and Selection Operator regression was used to select relevant retinal biomarkers, tailored to each cancer type. Predictive performance was assessed via the concordance index (C-index), detection rate (DR), and positive likelihood ratio (LR+). DR and LR+ were evaluated at a false-positive rate of 10%. Calibration was assessed by calibration slope and calibration-in-the-large (CITL). Hazard ratios (HRs) were used to stratify cancer risk between high- and low-risk groups identified by retinomics. Main Outcome Measures Incident diagnoses of any cancer and specific types: breast, uterine/endometrial, prostate, oesophageal, lung, stomach, colon, rectal, skin, and other cancers. Results Among 41,424 participants, 3,633 (8.77%) developed cancer over 12.5 years of follow-up. Incorporating retinomics significantly improved the C-index, DR, and LR+ across most cancer types, with the C-index values for Model 4 ranging from 0.581 to 0.885 across individual cancers. The largest gain was observed in uterine/endometrial cancer (ΔC-index = 0.220 [95% confidence interval (CI): 0.119–0.327]; ΔDR = 40%; ΔLR+ = 4.003), though this finding is based on only 20 incident cases and should be interpreted with caution. Calibration analysis demonstrated good agreement between predicted and observed risks across all models (slope range: 0.635–1.233; CITL near zero). The high-risk group identified by retinomics exhibited a 7% higher annual cancer risk (HR = 1.07, 95% CI: 1.06–1.08, p < 0.001), accumulating to a nearly threefold higher risk over 12 years. Conclusions Retinomics demonstrates predictive value for cancer incidence, with cancer type-specific biomarker selection enhancing accuracy. These findings highlight the transformative potential of retinal imaging as a scalable, non-invasive approach for early cancer risk profiling and proactive prevention.

Source: Ophthalmology Science

DOI: 10.1016/j.xops.2026.101413