南方医科大学学报2026，Vol.46Issue(4)：939-945,7.DOI:10.12122/j.issn.1673-4254.2026.04.22
基于实验室指标的Transformer模型可高效鉴别卵巢癌
A Transformer-based model using laboratory indicators efficiently differentiates ovarian cancer
摘要
Abstract
Objective To evaluate the diagnostic performance of a Transformer-based deep learning model that integrates real-world laboratory test indicators for differential diagnosis of ovarian cancer.Methods The clinical data and 99 laboratory test indicators were retrospectively collected from patients with ovarian cancer and benign ovarian lesions admitted to Department of Obstetrics and Gynecology of Tongji Hospital between January 1,2012 and April 4,2021.A feature selection algorithm based on ANOVA F-test was used on the training set to identify 20 key features.Each case was then converted into a unified embedded vector using a tabular data Transformer.An improved stacked Transformer model was then trained to encode these feature vectors.The proposed model was compared with multiple traditional machine learning methods.The evaluation metrics included the area under the receiver operating characteristic curve(AUC),accuracy,sensitivity,and specificity.Five-fold cross-validation was performed to assess the generalization ability and robustness of the model.Results Five-fold cross-validation showed that the Transformer-based deep learning model achieved the best performance in predicting ovarian cancer with an AUC of 0.931,an accuracy of 0.813,a sensitivity of 0.833,and a specificity of 0.865.Conclusion The proposed Transformer-based model demonstrates high accuracy and generalization capability in predicting ovarian cancer,and may thus offer a assistance in clinical diagnosis of ovarian tumors.关键词
卵巢癌/实验室检验指标/Transformer/嵌入向量/深度学习Key words
ovarian cancer/laboratory test indicators/Transformer/embedded vector/deep learning引用本文复制引用
谭顺谦,卓俐,曾敏,黄方俊,朱君,蔡光瑶,甄鑫..基于实验室指标的Transformer模型可高效鉴别卵巢癌[J].南方医科大学学报,2026,46(4):939-945,7.基金项目
国家自然科学基金(82572381,82404078) （82572381,82404078）
广东省自然科学基金(2024A1515012100) （2024A1515012100）
广东省基础与应用基础研究基金项目区域联合基金-青年基金项目(2023A1515110701) Supported by National Natural Science Foundation of China(82572381,82404078). （2023A1515110701）
