A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.

BACKGROUND

Detecting pituitary microadenomas using non-contrast multi-parametric magnetic resonance imaging (MRI) is challenging yet essential for clinical decisions. This study aimed to develop a transformer deep learning (DL) model for detecting pituitary microadenomas based on non-contrast multiparametric MRI and explore the explainability techniques to enhance transparency in convolutional neural network (CNN)-based classification. The primary research question addressed is how to improve the accuracy, generalization, and interpretability of CNNs for microadenomas detection.

METHODS

Non-contrast multiparametric MRI sella area scans of 590 patients were retrospectively collected from three hospitals. The development and comparison of 2D_DL, 2.5D_DL, 2D_multichannel, and transformer models for classification. By incorporating Explainable AI (XAI), including Gradient-weighted Class Activation Mapping(Grad-CAM) and SHapley Additive exPlanations (SHAP), we improve model interpretability.

RESULTS

The performance of the 2D_multichannel model, with an area under the curve (AUC) of 0.893, was better to that of the 2D_T1SAG_DL, 2D_T1COR_DL, 2D_T2COR_DL (AUC, 0.884, 0.779, and 0.846, respectively). The performance of the transformer model, with an area under the curve (AUC) of 0.985, was superior to that of the 2.5D_T1SAG_DL, 2.5D_T1COR_DL, 2.5D_T2COR_DL (AUC, 0.763, 0.863, and 0.835, respectively). The non-contrast MRI-based 2.5D_DL transformer model all shows outperforming performance in the internal and two external test sets (AUC, 0.874, 0.829, and 0.819, respectively).

CONCLUSIONS

Given its robust diagnostic performance and enhanced interpretability, this model demonstrates significant potential for clinical translation as a decision-support tool in the detection of pituitary microadenomas.