Transformer-based integration of radiomics and deep learning for differentiating lipid-poor adrenal adenomas from malignant tumors

Kai Zhao, Zhongqi Sun, Hao Jiang, Zhixuan Zou, Qiong Wu, Yanjie Xin, Xiangru Liu, Huijie Jiang,
Transformer-based integration of radiomics and deep learning for differentiating lipid-poor adrenal adenomas from malignant tumors,
Meta-Radiology,
Volume 3, Issue 4,
2025,
100183,
ISSN 2950-1628,
https://doi.org/10.1016/j.metrad.2025.100183.
(https://www.sciencedirect.com/science/article/pii/S2950162825000517)
Abstract: Purpose
To evaluate the effectiveness of a Transformer model based on contrast-enhanced computed tomography (CECT) that integrates radiomics and deep learning features in differentiating adrenal lipid-poor adenomas (LPA) and malignant tumors (MT).
Methods
This retrospective study included 282 patients with adrenal tumors from two medical centers between October 2018 and October 2024. The patients were classified into adrenal (LPA) and adrenal (MT) groups. Radiomics and deep learning features were extracted from CECT images. A total of 240 patients from the first center were randomly divided into Training Set and Test Set at a 7:3 ratio, while 42 patients from the second center served as an External Validation Set. A Transformer algorithm was employed to integrate radiomics and deep learning features for building predictive models. Its self-attention mechanism was utilized to capture intrinsic associations within each feature type and to uncover hidden information related to clinical outcomes. Additionally, a Radiomics model, a Deep Learning model (DL_model), and a Traditional Combined model integrating radiomics and deep learning features were constructed. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and radar chart. Calibration curves and decision curve analysis (DCA) were employed to assess the predictive accuracy and clinical net benefit of the models. Furthermore, radiomics feature activation maps and gradient-weighted class activation mapping (Grad-CAM) were utilized to visualize radiomics and deep learning features, respectively.
Results
The Transformer model achieved the best predictive performance in the training, test, and external validation sets, with AUCs of 0.949, 0.917, and 0.852, respectively. The DeLong test indicated that the performance differences between this model and the other models were statistically significant. Furthermore, the radar chart illustrated that the Transformer model achieved superior overall performance, and DCA confirmed its higher clinical net benefit compared with the other models.
Conclusion
The Transformer model that integrates radiomics and deep learning features can accurately distinguish between LPA and MT. Furthermore, the visual analysis of radiomics feature activation maps and Grad-CAM intuitively illustrates the distribution of radiomics and deep learning features, enhancing their potential for clinical application in preoperative assessment of adrenal tumors.
Keywords: Lipid-poor adrenal adenomas; Computed tomography; Radiomics; Deep learning; Transformer