European journal of cancer (Oxford, England : 1990) · 2025
AI-informed retinal biomarkers predict 10-year risk of onset of multiple hematological malignancies.
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Singh Amritpal, Nooka Ajay K, Modanwal Gourav, Jain Nieraj, Dhodapkar Madhav V, Arepalli Sruthi, Lonial Sagar, Madabhushi Anant
Аннотация
BACKGROUND: Early detection of hematological malignancies improves long-term survival but remains a critical challenge due to heterogeneity in clinical presentation. Chronic inflammation is a key driver in hematologic cancers and is known to induce compensatory microvascular changes. High-resolution, non-invasive retinal imaging can allow the quantification of microvascular changes for the early detection of hematological malignancies.
METHODS: This study evaluated RetHemo, an explainable AI tool predicting hematological malignancy onset up to 10 years before diagnosis using retinal imaging in 1237 UK Biobank patients. Retinal vasculature features (curvature, tortuosity, branching angles) were extracted from segmented vessels, arteries, and veins, enabling high-risk subgroup identification and outperforming traditional clinical predictors.
RESULTS: RetHemo demonstrated significant predictive performance for leukemia (c-index = 0.611, HR = 2.45, 95 % CI: 1.27-4.75, p = 0.027), myeloma (c-index = 0.636, HR = 6.69, 95 % CI: 2.06-21.65, p = 0.006). Unsupervised hierarchical clustering based on retinal vasculature features identified distinct high-risk subgroups for leukemia (p = 0.013), myeloma (p < 0.001), and lymphoma (p = 0.034). Serum proteomics analysis revealed significantly elevated levels of inflammatory proteins, including ITGAL and SLAMF7, in high-risk patients. Comparison with clinical variables showed that RetHemo outperformed traditional clinical and hematologic parameters in stratifying at-risk individuals.
CONCLUSION: These findings support the potential of AI-driven retinal biomarkers as a novel prognostic tool for early detection of hematological malignancies, enabling timely intervention and improved patient outcomes.
METHODS: This study evaluated RetHemo, an explainable AI tool predicting hematological malignancy onset up to 10 years before diagnosis using retinal imaging in 1237 UK Biobank patients. Retinal vasculature features (curvature, tortuosity, branching angles) were extracted from segmented vessels, arteries, and veins, enabling high-risk subgroup identification and outperforming traditional clinical predictors.
RESULTS: RetHemo demonstrated significant predictive performance for leukemia (c-index = 0.611, HR = 2.45, 95 % CI: 1.27-4.75, p = 0.027), myeloma (c-index = 0.636, HR = 6.69, 95 % CI: 2.06-21.65, p = 0.006). Unsupervised hierarchical clustering based on retinal vasculature features identified distinct high-risk subgroups for leukemia (p = 0.013), myeloma (p < 0.001), and lymphoma (p = 0.034). Serum proteomics analysis revealed significantly elevated levels of inflammatory proteins, including ITGAL and SLAMF7, in high-risk patients. Comparison with clinical variables showed that RetHemo outperformed traditional clinical and hematologic parameters in stratifying at-risk individuals.
CONCLUSION: These findings support the potential of AI-driven retinal biomarkers as a novel prognostic tool for early detection of hematological malignancies, enabling timely intervention and improved patient outcomes.