MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma.

Objectives: The progesterone receptor (PR) is an important biomarker in meningiomas, influencing tumor growth, prognosis, and potential treatment options. The objective of this study was to predict PR expression in meningioma via deep transfer learning (DTL). Methods: A total of 307 patients were included in the study, including 173 positive patients and 134 negative patients. The clinical features were analyzed. The DTL features were extracted via the fine-tuned ResNet 50 model and selected by the intraclass correlation coefficient (ICC), spearman correlation coefficient and least absolute shrinkage and selection operator (LASSO). The predictive models were built via logistic regression (LR), support vector machine (SVM) and naive Bayes. The discriminative ability of the model was assessed by receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). The accuracy, sensitivity and specificity were also calculated. Decision curve analysis (DCA) curves were drawn to evaluate the clinical usefulness of the nomogram. Results: A total of 2048 DTL features were extracted, and 35 features were selected for model construction. In the test set, the AUCs of the LR, naive Bayes, and SVM models were 0.819 (95% CI: 0.7081-0.9300), 083(95% CI: 0.7216-0.9376), and 0.842 (95% CI: 0.7359-0.9488), respectively. There was no significant difference between any two models according to the Delong test. The SVM model exhibited a greater net benefit across the highest probability according to the DCA curve. Conclusions: The SVM model achieved better predictive performance and represents a useful tool for evaluating meningioma.