A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.

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 i

SUBMITTER: Li S 

PROVIDER: S-EPMC12660402 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature