Bayesan Model to Predict R Status After Neoadjuvant Therapy in Pancreatic Cancer.

Objective: To build a Bayesian approach-based model to predict the success of surgical exploration post-neoadjuvant treatment.

Background: Pancreatic cancer (PDAC) is best treated with radical surgery and chemotherapy, offering the greatest chance of survival. Surgery after neoadjuvant treatment (NAT) is indicated in the absence of progression, knowing the limits in accurately predicting resectability with traditional radiology. R Status being a pathological parameter, it can be assessed only after surgery.

Method: Patients successfully resected for histologically confirmed PDAC after NAT for BR and LA disease were included, with attention to the predictors of R status from the existing literature. The Bayesian logistic regression model was estimated for predicting the R1 status. The area under curve (AUC) of the average posterior probability of R1 was calculated and results were reported considering the 95% posterior credible intervals for the odds ratios, along with the probability of direction.

Results: The final model demonstrated a commendable AUC value of 0.72, indicating good performance. The likelihood of positive margins was associated with older age, higher ASA score, the presence of venous and/or arterial involvement at preoperative radiology, tumor location within the pancreatic body, a lack of tumor size reduction post-NAT, and the persistence of an elevated Ca19.9 value.

Conclusions: A Bayesian approach using only preoperative items is firstly used with good performance to predict R Status in pancreatic cancer patients who underwent resection after neoadjuvant therapy.