Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.

Purpose: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).

Methods: Postoperative PDAC patients were retrospectively selected, all of whom had undergone preoperative CT imaging and surgery. Both patients with resectable or borderline-resectable pancreatic cancer met the eligibility criteria in this study. However, owing to the differences in treatment strategies and such, this research mainly focused on patients with resectable pancreatic cancer. All patients were subject to follow-up assessments for a minimum of 9 months. A total of 250 cases meeting the inclusion criteria were included. A clinical model, a conventional radiomics model, and a deep-radiomics model were constructed for ER prediction (defined as occurring within 9 months) in the training set. A model based on the TNM staging was utilized as a baseline for comparison. Assessment of the models' performance was based on the area under the receiver operating characteristic curve (AUC). Additionally, precision-recall (PR) analysis and calibration assessments were conducted for model evaluation. Furthermore, the clinical utility of the models was evaluated through decision curve analysis (DCA), net reclassification improvement (NRI), and improvement of reclassification index (IRI).

Results: In the test set, the AUC values for ER prediction were as follows: TNM staging, ROC-AUC = 0.673 (95% CI: 0.550, 0.795), PR-AUC = 0.362 (95% CI: 0.493, 0.710); clinical model, ROC-AUC = 0.640 (95% CI: 0.504, 0.775), PR-AUC = 0.481 (95% CI: 0.520, 0.735); radiomics model, ROC-AUC = 0.722 (95% CI: 0.604, 0.839), PR-AUC = 0.575 (95% CI: 0.466, 0.686); and deep-radiomics model, which exhibited the highest ROC-AUC of 0.895 (95% CI: 0.820, 0.970), PR-AUC = 0.834 (95% CI: 0.767, 0.923). The difference in both ROC-AUC and PR-AUC for the deep-radiomics model was statistically significant when compared to the other scores (all p < 0.05). The DCA curve of the deep-radiomics model outperformed the other models. NRI and IRI analyses demonstrated that the deep-radiomics model significantly enhances risk classification compared to the other prediction methods (all p < 0.05).

Conclusion: The predictive performance of deep features based on CT images exhibits favorable outcomes in predicting early recurrence.