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, Wenjun Cao · 2025
Read A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma by Shengjie Li, Jiazhen Cao, Danhui Li, Jun Ren, Jianing Wu, Yingzhu Li, Mengyu Zhang, Henggui Hu, Yunxiao Song, Jie Cheng, Ming Guan, Wenjun Cao (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12660402.txt.
Description
Li and eleven co-authors screen for a rare eye lymphoma with a routine blood test, in this two thousand twenty-five study. Primary vitreoretinal lymphoma is aggressive and often mistaken for other eye diseases, delaying treatment.
Their case-control study covered two hundred fifty-five patients and two hundred ninety-two controls. A random forest model using six complete blood count features reached strong accuracy, beating the eye-fluid biomarker doctors currently use. In a hospital population of over one hundred thousand people, the model flagged sixty-six as high risk and thirty-eight of them truly had the lymphoma, a sensitivity of ninety-five percent. A community cohort gave similar results. A web app turns the score into quick triage.
Catalog record
| CREATOR | Shengjie Li, Jiazhen Cao, Danhui Li, Jun Ren, Jianing Wu, Yingzhu Li, Mengyu Zhang, Henggui Hu, Yunxiao Song, Jie Cheng, Ming Guan, Wenjun Cao |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |