Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

SIMPLE SUMMARY: Early recurrence is common after curative resection for pancreatic ductal adenocarcinoma (PDAC). Patients with a high-risk of early recurrence may benefit from a neoadjuvant-first approach instead of an upfront surgery. In our study, a deep-learning model for predicting early recurrence was developed and validated. The results showed that the deep learning model outputs were an independent risk factors associated with early recurrence. Additionally, higher values of deep learning model outputs were significantly associated with worse recurrence-free survival in various subgroups and demonstrated more advanced tumor behaviors. The comprehensive nomogram that integrated the deep learning model outputs and independent radiological factors further improved the predictive performance. Our findings show that the deep learning-based nomogram could noninvasively predict early recurrence in PDAC patients, which may support clinical decision-making about upfront resection or neoadjuvant treatment strategies. ABSTRACT: Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients.