[A Transformer-based model using laboratory indicators efficiently differentiates ovarian cancer].

objectivesTo evaluate the diagnostic performance of a Transformer-based deep learning model that integrates real-world laboratory test indicators for differential diagnosis of ovarian cancer.

methodsThe 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.

resultsFive-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.

conclusionsThe 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.