Background: Although minimally invasive pancreatoduodenectomy (MIPD) is increasingly being performed, there are still concerns about safe dissemination of this operative procedure due to its long learning curve. The learning curve is known to be influenced by hospital volume or and experiences of open surgery, but there has been no quantitative analysis on this. If we develop a difficulty scoring system based on factors affecting the MIPD learning curve, it would contribute to the safe dissemination of MIPD by predicting the risk of serious complications in the early learning curve and guiding surgeons to perform the procedure step by step approach. Purpose: The aims of this study are 1) to compare the learning curve of MIPD according to the surgical method (laparoscopic PD (LDP) and robotic PD (RPD)), hospital volume, and experience of open surgery 2) to compare the surgical results before and after the learning curve of LPD and RPD using a propensity score matching analysis, 3) to finally develop a difficulty scoring system of LPD and RPD. Method: The study population includes patients who underwent LPD and RPD between 2010 and 2022. A. Task 1: Comparison of learning curves of LPD and RPD - Analyze the learning curve using the risk-adjusted CUSUM model - Compare differences in learning curves of first- and second-generation surgeons considering hospital volume, open surgery experience, and training system. B. Task 2: Comparison of postoperative results of LPD and RPD using propensity score matching - Primary endpoint: Severe postoperative complications - Secondary endpoints: operation time, amount of blood loss, hospital stay, readmission rate C. Task 3: Development of difficulty score system for LPD an RPD predicting the risk of open conversion and severs complications of LPD and RPD. - Identify the characteristics of the patient group and divide the patients into training set and validation set - In the training set, perform a multivariate analysis to find preoperative factors related to open conversion, operation time, and sever complications and develop a scoring system based on the hazard ratio of the results according to each factor stage - Check the AUC in the validation set.