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AI-driven eyelid tumor classification in ocular oncology using proteomic data.

Linyan Wang1,2,3, Xizhe Dai4,5, Zicheng Liu6

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An artificial intelligence (AI) system accurately classifies eyelid tumors using proteomic data. This AI diagnostic tool enhances precision and efficiency in identifying various eyelid lesions.

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Area of Science:

  • Ophthalmology
  • Oncology
  • Biotechnology

Background:

  • Eyelid tumors present diagnostic challenges due to varied pathology and limited biopsy samples.
  • Accurate classification is crucial for effective treatment and patient outcomes.

Purpose of the Study:

  • To develop an artificial intelligence (AI) diagnostic system for precise eyelid tumor classification.
  • To leverage mass spectrometry-based proteomics for identifying novel diagnostic biomarkers.

Main Methods:

  • Proteomic analysis of 233 formalin-fixed, paraffin-embedded (FFPE) eyelid tumor samples from 150 patients.
  • Identification of 18 novel protein biomarkers and development of an AI classification model.
  • Validation of the AI model using an independent cohort of 99 samples from 60 patients.

Main Results:

  • The 18-protein AI model achieved high accuracy (84.8%), precision (86.2%), and recall (84.8%) in multi-class classification.
  • UMAP visualization confirmed distinct clustering of different eyelid lesion types.
  • ROC curve analysis demonstrated strong predictive performance with AUC values from 0.80 to 1.00.

Conclusions:

  • The developed AI diagnostic system shows significant promise for improving eyelid tumor diagnosis.
  • This proteomic-based AI approach addresses limitations of traditional histopathological methods.
  • The system offers enhanced efficiency and precision for clinical application in ophthalmology and oncology.