Abstract:

Objective: To evaluate the clinical utility of an artificial intelligence (AI) model constructed from multiple laboratory indicators for diagnosing ovarian malignancies. Methods: A retrospective analysis was conducted on laboratory data from 3 465 patients with ovarian malignant tumors and 6 987 patients with benign ovarian tumors at the First Affiliated Hospital of Zhengzhou University. Patients were randomly divided into training (n=7 317) and testing (n=3 135) sets at a ratio of 7∶3. Feature selection was performed using the Boruta algorithm and Lasso regression. Four models(random forest, logistic regression, support vector machine, and gradient boosting decision tree) were constructed. Model performance was evaluated by area under the curve (AUC), accuracy, precision, recall, and F1-score. Results: The random forest model achieved optimal performance in the testing set (AUC=0.924, accuracy=0.872, recall=0.749), outperforming the other three models and conventional single biomarkers [human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA125), CA15-3, and D-dimer]. Feature importance analysis identified 15 clinical significant indicators: HE4, CA15-3, CA19-9, CA125, CA724, alpha-fetoprotein, D-dimer, fibrinogen, albumin, lactate dehydrogenase, neutrophil percentage, absolute neutrophil count, lymphocyte percentage, absolute lymphocyte count, and platelet count. Conclusions: The random forest model based on laboratory data demonstrated high diagnostic efficacy for ovarian malignancies with promising clinical applicability. Future studies should incorporate multicenter data and multimodal information to enhance model generalizability and interpretability, facilitating its integration into clinical practice.

Key words: Ovarian neoplasms, Diagnosis, Artificial intelligence, Machine learning, Laboratory test indicators, Random Forest, Feature selection