Pancreatic cancer remains one of the most lethal malignancies and is projected to become the second leading cause of cancer-related deaths worldwide by 2030 [1] . According to data from the National Cancer Institute (NCI), it is estimated that there will be 64,050 new cases of pancreatic cancer in the United States in 2023, with approximately 50,550 deaths [2] . Surgical resection remains the best treatment option for pancreatic cancer; however, the 5-year survival rate for patients who undergo surgical resection is still only about 20% [3] . Postoperative recurrence is a major factor contributing to the short survival period of pancreatic cancer patients, with literature reporting that most patients experience recurrence within two years after surgery [4] .

Several studies have identified various risk factors for postoperative recurrence, including carbohydrate antigen 19-9 (CA19-9) levels, resection margin status, lymph node metastasis, neural invasion, and adjuvant chemotherapy [5] - [7] . However, existing scoring systems that integrate multiple clinical factors remain imperfect, and there is a lack of clinical models that comprehensively assess the probability of recurrence by considering preoperative, intraoperative, and postoperative factors. Therefore, developing a new clinical scoring model that provides a deeper understanding of the risk factors associated with postoperative recurrence could lead to more individualized treatment for pancreatic cancer patients.

This study aims to develop and validate a clinical predictive model for postoperative recurrence in pancreatic cancer patients by integrating preoperative, intraoperative, and postoperative factors. The goal is to better predict the risk and timing of recurrence, thereby assisting in clinical decision-making.

This study identified seven independent risk factors for postoperative recurrence in pancreatic cancer through univariate and multivariate analyses: diabetes, tumor size, operative time, differentiation grade, margin status, neural invasion, and postoperative CA199 level. Previous literature suggests a bidirectional relationship between diabetes and pancreatic cancer, with diabetes being both a cause and a result of pancreatic cancer. Patients without diabetes have a longer disease-free survival [9] . Tumors smaller than 2 cm are associated with significantly better long-term outcomes after pancreatic cancer surgery, consistent with our findings [10] . Prolonged operative time may be related to more complex tumor structures and local invasion, and surgery may induce the shedding of cancer cells into the circulatory system, inhibit antitumor immunity, and trigger local and systemic inflammatory responses, potentially accelerating the growth of residual and micrometastatic disease [11] . As operative time increases, these effects may become more pronounced. We found that moderately and well-differentiated pancreatic tumors have a lower risk of recurrence than poorly differentiated tumors, with lower differentiation indicating higher malignancy and invasiveness, leading to a greater tendency for recurrence. Margin status, neural invasion, and CA199 levels are recognized risk factors for postoperative recurrence in pancreatic cancer. Positive margins and neural invasion suggest a high likelihood of residual disease and a high risk of local recurrence. CA199 is the most widely reported and well-studied tumor marker for pancreatic cancer [6] [12] [13] . Postoperative CA199 levels indicate poor biological behavior of the tumor, leading to a higher risk of recurrence. Due to the differences in chemotherapy administration across the two cohorts, including the decision to administer chemotherapy, the specific chemotherapy regimens used, dosages, treatment duration, and the inclusion of interventional chemotherapy in some patients, the overall impact of chemotherapy on recurrence did not reach statistical significance. These variations in treatment protocols likely contributed to the lack of statistical significance in the comparison between chemotherapy and non-chemotherapy groups. Furthermore, the heterogeneous nature of the treatment regimens may have diluted the effect of chemotherapy on recurrence, leading to inconclusive results when analyzed across the entire cohort.

Whether chemotherapy is administered also significantly affects tumor recurrence [14] - [16] . In our training cohort, 130 (60.5%) patients received postoperative chemotherapy, with 37 (17.2%) receiving AG regimen chemotherapy, 30 (14%) receiving gemcitabine monotherapy, and 63 (29.3%) receiving various chemotherapy regimens, often in combination with different immunotherapy regimens. This variability in treatment may explain the lack of statistical significance in our study on chemotherapy’s impact on recurrence.

This study has several limitations. First, as a retrospective study, it is subject to potential selection bias, although external validation has demonstrated good predictive ability. Second, the study population was from a localized region in China, and the model’s applicability to other regions and ethnicities requires further validation with larger datasets. Third, for other risk factors previously identified in the literature, such as lymph node metastasis, CEA level, vascular invasion, preoperative CA199 level, and histological type, our univariate and multivariate Cox regression analyses found no statistical significance, possibly requiring larger sample sizes or more multicenter clinical experience for more robust data. Based on the model’s predictions, clinicians can use the risk scores to better stratify patients and personalize treatment regimens. For high-risk patients, more aggressive treatment strategies such as combination therapies may be considered, while low-risk patients could benefit from less intensive approaches. Furthermore, the model’s predictions can aid in patient counseling by providing a more accurate prognosis, helping patients make informed decisions regarding their treatment options.

By integrating data from two centers, we developed and validated a clinical predictive model identifying seven independent predictors of pancreatic cancer recurrence: diabetes, tumor size, operative time, differentiation grade, margin status, neural invasion, and postoperative CA199 level. This model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making. Further validation in larger and more diverse populations is necessary to ensure its broader applicability.

This research was conducted without any external funding support.

LJZ Collected the data and conceptualized the article, conducted the literature search, and drafted the initial manuscript. CY made essential revisions to the manuscript’s content. All authors have reviewed and approved the final manuscript.

^*Corresponding author.