Article in European journal of cancer (Oxford, England : 1990), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
8 authors.
Amritpal SinghEmory University, Atlanta, USA.
Ajay K NookaWinship Cancer Institute, Emory University, Atlanta, USA.
Gourav ModanwalWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Nieraj JainDepartment of Ophthalmology, Emory University, Atlanta, USA.
Madhav V DhodapkarWinship Cancer Institute, Emory University, Atlanta, USA.
Sruthi ArepalliDepartment of Ophthalmology, Emory University, Atlanta, USA.
Sagar LonialWinship Cancer Institute, Emory University, Atlanta, USA.
Anant MadabhushiEmory University, Atlanta, USA; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, USA. Electronic address: anantm@emory.edu.
Funding
P-CARRS-BRAIN: Multi-domain (genetic, socio-behavioral, vascular) risk factors and prediction of Alzheimer’s Disease continuum in South Asians in IndiaR01AG089759 · NIA · EMORY UNIVERSITY · PI Suvarna Alladi, ALLAN I LEVEY · 2024 to 2026
$12.8M
KPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4M
Spatial Transcriptomics Explorer (STE): An open-source resource for visualizing spatial gene expression dataU24CA274494 · NCI · SAGE BIONETWORKS · PI Jineta Banerjee, Susheel Varma · 2022 to 2026
$10.1M
Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
Computational Pathology of Proteinuric DiseasesR01DK118431 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI BARISONI, LAURA MARIACHIARA, HODGIN, JEFFREY BENTON · 2018 to 2025
$3.6M
Computer-Assisted Histologic Evaluation of Cardiac Allograft RejectionR01HL151277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MADABHUSHI, ANANT, MARGULIES, KENNETH BER · 2020 to 2023
$3.2M
Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)R01CA249992 · NCI · EMORY UNIVERSITY · PI LEWIS, JAMES, MADABHUSHI, ANANT · 2021 to 2025
$3.2M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
Prognostic and Predictive Digital Tissue Image Assay for Prostate CancerR01CA268287 · NCI · EMORY UNIVERSITY · PI GUPTA, SHILPA, LAL, PRITI · 2022 to 2025
$3.0M
RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATIONR01HL158071 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI BARNARD, JOHN, CHUNG, MINA KAY · 2021 to 2024
backgroundEarly 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.
methodsThis 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.
resultsRetHemo 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.
conclusionThese 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.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
AI-informed retinal biomarkers predict 10-year risk of onset of multiple hematological malignancies. · full record | Socratic