ArticleNPJ precision oncology2024
AI-driven eyelid tumor classification in ocular oncology using proteomic data.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.Drug delivery and translational research · 2026Article
- MRI assessment of extramural venous invasion in rectal cancer: a practical pictorial review.Abdominal radiology (New York) · 2026Review
- Review
- 3D-Printed Scaffold-Based Glioblastoma Spheroid In Vitro Model for Drug Screening Application.Annals of biomedical engineering · 2026Article
- High-resolution optical coherence tomography for screening ocular surface tumors: Historical markers and future directions.World journal of clinical cases · 2025Article
- Artificial intelligence in oculoplastics: Novel applications and future prospects.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Eyelid tumors pose diagnostic challenges due to their diverse pathological types and limited biopsy materials. This study aimed to develop an artificial intelligence (AI) diagnostic system for accurate classification of eyelid tumors. Utilizing mass spectrometry-based proteomics, we analyzed proteomic data from eight tissue types and identified eighteen novel biomarkers based on 233 formalin-fixed, paraffin-embedded (FFPE) samples from 150 patients. The 18-protein model, validated by an independent cohort (99 samples from 60 patients), exhibited high accuracy (84.8%), precision (86.2%), and recall (84.8%) in multi-class classification. The model demonstrated distinct clustering of different lesion types, as visualized through UMAP plots. Receiver operator characteristic (ROC) curve analysis revealed strong predictive ability with area under the curve (AUC) values ranging from 0.80 to 1.00. This AI-based diagnostic system holds promise for improving the efficiency and precision of eyelid tumor diagnosis, addressing the limitations of traditional pathological methods.
Identifiers
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Registered trials
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 OpenQuestion graph.