Multiphase enhanced CT-based transformer for differential diagnosis and predicting surgical risk events of solid abdominal tumors in children.

Background: For pediatric patients with solid abdominal tumors, early diagnosis can guide clinical treatment decisions, and comprehensive preoperative evaluation is essential to reduce surgical risk. The aim of this study was to explore the feasibility of multiphase enhanced CT-based transformer in the early diagnosis of tumors and prediction of surgical risk events (SRE).

Methods: A total of 496 pediatric patients with solid abdominal tumors were enrolled in the study. With Swin transformer, we constructed and trained two Swin-T models based on preoperative multiphase enhanced CT for personalized prediction of tumor type and SRE status. Subsequently, we comprehensively evaluated the performance of each model and constructed four benchmark models for performance comparison.

Results: There was no significant difference in SRE status between tumor types. In the diagnostic task, areas under the receiver operating characteristic curves (AUC) of the Swin-T model were 0.987 (95 % CI, 0.973-0.997) and 0.844 (95 % CI, 0.730-0.940) in the training and validation cohorts, respectively. In predicting SRE, AUCs of the Swin-T model were 0.920 (95 % CI, 0.885-0.948) and 0.741 (95 % CI, 0.632-0.838) in the training and test cohorts, respectively. The Swin-T model achieved the best performance in both classification tasks compared to benchmark models.

Conclusion: The Swin-T model is a promising tool to assist pediatricians in the differential diagnosis of abdominal tumors and in comprehensive preoperative evaluation.