ArticleNature communications2025
A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Review
- Routine blood tests and machine learning identify complications in high myopia.Nature communications · 2026Article
- Optical coherence tomography features and visual prognosis in vitreoretinal lymphoma: a structured phenotyping study.Frontiers in medicine · 2026Article
- A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.Nature communications · 2025Article
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Authors and funding
12 authors.
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Abstract
Primary vitreoretinal lymphoma (PVRL) is a rare and aggressive intraocular malignancy that is frequently misdiagnosed because of its nonspecific early manifestations and the lack of effective screening tools. We conduct a multicentre case-control study including 255 PVRL patients and 292 controls to develop a machine learning-based screening model using complete blood count data. A six-feature random forest model demonstrates high diagnostic accuracy in the discovery cohort (area under the curve [AUC] = 0.85) and validates across all cohorts (AUC = 0.80-0.83), outperforming intraocular biomarkers such as the interleukin-10/interleukin-6 ratio (AUC = 0.65-0.78). Model performance further validates in a hospital-based prospective cohort (n = 100,526), where 38 PVRLs are identified among 66 individuals classified as high risk, and 2 additional cases are identified among 83,610 individuals classified as low risk, yielding a sensitivity of 95.0%, specificity of 99.97%, positive predictive value (PPV) of 57.6%, and negative predictive value of 99.99%. In the community cohort (n = 515,326), 22 individuals are flagged as high risk, 13 of whom are confirmed as having PVRL (PPV = 59.1%). This study presents the noninvasive and scalable blood-based screening strategy for detection of PVRL, with a web application enabling timely triage and population-level risk stratification.
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