Wang Linyan, Dai Xizhe, Liu Zicheng, Zhao Yaxing, Sun Yaoting, Mao Bangxun, Wu Shuohan, Zhu Tiansheng, Huang Fengbo, Maimaiti Nuliqiman, Cai Xue, Li Stan Z, Sheng Jianpeng, Guo Tiannan, Ye Juan
Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China.
NPJ Precis Oncol. 2024 Dec 23;8(1):289. doi: 10.1038/s41698-024-00767-8.
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.
眼睑肿瘤因其病理类型多样且活检材料有限而带来诊断挑战。本研究旨在开发一种用于准确分类眼睑肿瘤的人工智能（AI）诊断系统。利用基于质谱的蛋白质组学，我们分析了来自八种组织类型的蛋白质组数据，并基于150例患者的233份福尔马林固定石蜡包埋（FFPE）样本鉴定出18种新型生物标志物。由一个独立队列（60例患者的99份样本）验证的18蛋白模型在多类分类中表现出高精度（84.8%）、精准度（86.2%）和召回率（84.8%）。通过UMAP图可视化，该模型展示了不同病变类型的明显聚类。受试者工作特征（ROC）曲线分析显示出强大的预测能力，曲线下面积（AUC）值范围为0.80至1.00。这种基于AI的诊断系统有望提高眼睑肿瘤诊断的效率和精准度，解决传统病理方法的局限性。
