Xia Ding, Wei Xu, Yan Xu, Yongchuang Zhang, Huaxiao Xu, Lin Guo, Lei Li
World J Surg Oncol. 2025 Apr 9;23(1):135. doi: 10.1186/s12957-025-03774-6.
Background: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.
Purpose: To develop and test a transformer model utilizing dual-energy computed tomography (DECT) for predicting LNM in patients with PDAC.
Materials and methods: This retrospective study examined patients who had undergone surgical resection and had pathologically confirmed PDAC, with DECT performed between August 2016 and October 2022. Six predictive models were constructed: a DECT report model, a clinical model, 100 keV DL model, 150 keV DL model, a combined 100 + 150 keV DL model, and a model that integrated clinical information with DL-derived signatures. Multivariable logistic regression analysis was employed to develop the integrated model. The efficacy of these models was assessed by comparing their areas under the receiver operating characteristic curve (AUC) using the Delong test. Survival analysis was conducted using Kaplan-Meier curves.
Results: In brief, 223 patients (mean age, 57 years � 11 standard deviation; 93 men) were evaluated. All patients were divided into training (n = 160) and test (n = 63) sets. Patients with LNM accounted for 96 of the 223 patients (43%). In the test set, the integrated model, which integrated DECT parameters such as IC and Z, CA- 199 levels, DECT reports, and DL signatures, demonstrated the highest performance in predicting LNM, with an AUC of 0.93. In contrast, the radiologists'assessment and the clinical model yielded AUCs of 0.60 and 0.62, respectively. The integrated model-predicted positive LNM was associated with worse overall survival (hazard ratio, 1.75; 95% confidence interval: 1.22 - 2.83; P =.023).
Conclusion: A transformer-based model outperformed radiologists and clinical model for prediction of LNM at DECT in patients with PDAC.