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Radiomics and Deep Learning Models Based on Manual and Semi-automatic Segmentation of Ultrasound Images for HER2
Jiajing Zhuang1, Yihan Xie1, Yuefan Chen1
1Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China (J.Z., Y.X., Y.C., W.Z., G.L., Y.Z., H.Z., X.F., F.F., Q.Y.).
Rationale And Objectives:
To evaluate semi-automatic segmentation of bladder ultrasound images and develop human epidermal growth factor receptor 2 (HER2) prediction models using radiomics and deep learning.
Methods:
We retrospectively analyzed patients with urothelial carcinoma of the bladder from January 2022 to May 2025. The regions of interest (ROIs) were delineated using both semi-automatic segmentation based on segment anything in medical images (MedSAM) and manual segmentation. Radiomic features were extracted and screened to construct radiomics models. ResNet18 was trained to extract deep transfer learning (DTL) features, and the reduced-dimensional features were employed to build DTL models. The radiomic features and DL features were combined to develop deep learning radiomics (DLR) models. Univariate analysis and multivariate analysis were conducted to identify clinical, pathological, and ultrasonic features, and the selected feature was integrated with the most suitable DLR model to develop a combined model.
Results:
A total of 190 patients were enrolled (133 in the train set and 57 in the test set). The MedSAM-based semi-automatic segmentation achieved a dice similarity coefficient score of 0.824 ± 0.109. The random forest algorithm was selected for constructing both the radiomics signature and the DLR signature. In the manual segmentation group, the Rad, DTL, DLR, and combined signature achieved an area under curve (AUC) of 0.766 (0.638 - 0.894), 0.800 (0.681 - 0.920), 0.792 (0.676 - 0.908), and 0.805 (0.693 - 0.917) in the test set, respectively. In the semi-automatic segmentation group, the Rad, DTL, DLR, and combined signature achieved an AUC of 0.814 (0.700 - 0.930), 0.794 (0.673 - 0.915), 0.835 (0.725 - 0.945), and 0.842 (0.734 - 0.951) in the test set, respectively.
Conclusion:
MedSAM-based semi-automatic segmentation achieves segmentation performance approaching human experts. The model combining clinical, radiomics, and deep learning features showed promising performance in predicting HER2 status in urothelial carcinoma of the bladder.