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

Shengjie Li (), Jiazhen Cao, Danhui Li, Jun Ren, Jianing Wu, Yingzhu Li, Mengyu Zhang, Henggui Hu, Yunxiao Song, Jie Cheng (), Ming Guan () and Wenjun Cao ()
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Shengjie Li: Fudan University, Department of Clinical Laboratory, Eye & ENT Hospital
Jiazhen Cao: Fudan University, Department of Laboratory Medicine, Huashan Hospital
Danhui Li: Shanghai JiaoTong University, Department of Pathology, RenJi Hospital, School of Medicine
Jun Ren: Fudan University, Department of Clinical Laboratory, Eye & ENT Hospital
Jianing Wu: Fudan University, Department of Clinical Laboratory, Eye & ENT Hospital
Yingzhu Li: Fudan University, Department of Clinical Laboratory, Eye & ENT Hospital
Mengyu Zhang: Anhui Wanbei Electricity Group General Hospital, Department of Clinical Laboratory
Henggui Hu: Anhui Wanbei Electricity Group General Hospital, Department of Clinical Laboratory
Yunxiao Song: Fudan University, Department of Clinical Laboratory, Shanghai Xuhui Central Hospital
Jie Cheng: Fudan University, Department of General Practice, Shanghai Xuhui Central Hospital
Ming Guan: Fudan University, Department of Laboratory Medicine, Huashan Hospital
Wenjun Cao: Fudan University, Department of Clinical Laboratory, Eye & ENT Hospital

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-65693-0

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