The prognosis of BR/LAPC is exceedingly poor, characterized by a low likelihood of long-term survival. The standard approach for managing localized disease typically entails a multimodal treatment regimen consisting of chemotherapy, radiation therapy, and surgical resection (Tempero et al., 2021). Multidisciplinary treatment (MDT)-based combination therapy can improve the prognosis and survival outcomes of BR/LAPC patients, increase the 5-year survival rate and median survival, and increase the overall survival (OS) to 35–60 months for those who can undergo R0 resection after treatment (Reames et al., 2021). However, even with aggressive treatment, the five-year overall survival (OS) rate for BRPC/LAPC is still less than 15%, and not all patients benefit from surgery (Rawla et al., 2019).
Currently, the clinical assessment and prediction of the comprehensive therapeutic efficacy for BR/LAPC primarily rely on imaging examinations. However, this method can only evaluate and predict efficacy based on the changes of tumor morphology, surrounding vascular relationships, and affected lymph nodes (Eisenhauer et al., 2009), without precision in assessing and predicting therapeutic effects. Related studies have shown that this method overestimates tumor unresectability (White et al., 2001). Despite radiology predicting persistent unresectability, Ferrone et al. demonstrated that 92% of the studied patients were able to successfully have their tumors removed while avoiding tumoral involvement at the resection margin and have a better prognosis (Ferrone et al., 2015). Therefore, further research and development of more accurate evaluation and prediction methods are needed to guide clinical treatment selection and optimize treatment outcomes. In the present study, we analyzed the baseline characteristics of a total of 6425 BR/LAPC patients from the SEER databases. In order to compare whether primary tumor resection prolongs survival, we utilized PSM to balance the variables between the surgical and non-surgical groups and eliminate selection bias. The PSM process involved building a predictive model that calculated the probability of surgery for each patient based on a range of covariates, such as age, gender, disease severity, etc., and then pairing the surgical and non-surgical groups based on this propensity score. This method effectively minimized the differences in the distribution of potential confounding variables between the two groups, allowing for a more accurate comparison of survival outcomes. According to the median CSS of the non-surgical group (8 months), the surgical group was divided into two subgroups: a surgical benefit group and a non-surgical benefit group. Subsequently, we trained and validated six ML models to predict postoperative benefit for BR/LAPC patients, revealing four important findings. First, all six ML models achieved high AUC values of > 0.75. Second, after comparing the performance of the six ML-based models, XGBoost exhibited the best predictive performance. Third, based on the XGBoost model, the relative importance of the variables was ranked with age as the most important, followed by chemotherapy and radiotherapy. Furthermore, through internal and external validation, we were able to test the generalizability of the XGBoost algorithm, and its findings indicated that the model had the potential to facilitate personalized treatment planning for BR/LAPC patients.
In addition to age, the implementation of chemotherapy and radiation regimens was found to be the most crucial variable in this study, which highlighted the critical role of radiochemotherapy in a comprehensive multidimensional treatment model for BR/LAPC. With advancements in treatment concepts and therapeutic technologies, neoadjuvant radiochemotherapy has gained significant importance in the comprehensive management of PC and its clinical value has been explicitly recommended in authoritative guidelines (Cloyd et al., 2019; Cohen et al., 2005; Moertel et al., 1981). In neoadjuvant therapy for BR/LAPC, there are several reasons for its rationale (Versteijne et al., 2016). Firstly, as it targets tissue that has not been dissected and is well-oxygenated, neoadjuvant radiochemotherapy can maximize the potential benefits of both radiation and chemotherapy, compared to adjuvant therapy. Secondly, it can shrink tumor volume, improve the feasibility of surgical resection, and decrease involvement of regional lymph nodes, thereby mitigating the risk of local recurrence. Thirdly, by extirpating adjacent structures that are infiltrated by the tumor, neoadjuvant radiochemotherapy can reduce staging and augment the proportion of R0 resection. Notably, a recent European consensus highlighted preoperative radiochemotherapy as a key area for future clinical research, underscoring its growing importance in the field (Van Laethem et al., 2012).
As anticipated, the feature importance ranking of surgical resection extent variable was relatively lower in the XGBoost model. BR/LAPC often necessitates combined procedures, including multi-organ resections, vascular reconstruction, and extensive lymph node dissection, which significantly augment the complexity of the surgery. These interventions are exclusively achievable in large pancreatic centers and require collaboration among multidisciplinary teams (MDTs) (Christians et al., 2014). However, according to a consensus statement from ISGPS (International Study Group for Pancreatic Surgery) (Hartwig et al., 2014), extended resections are associated with higher rates of perioperative complications and no significant difference in overall survival compared to standard resections. This could be due to the fact that pancreatectomy with arterial resection for PC is associated with a total operative mortality rate as high as 10%-20%, which limits the potential benefits of tumor resection itself. This indicates that "the scalpel ≠ omnipotence", as surgery alone cannot address the highly invasive tumor biology of PC (Katz and Varadhachary, 2019). Therefore, the implementation of a comprehensive multidimensional treatment approach is crucial in the management of PC, where neoadjuvant radiochemotherapy plays a significant role.
As far as we know, there is currently no reliable ML model that can accurately predict the postoperative prognosis of BR/LAPC. This is because the effectiveness of BR/LAPC depends on multiple factors, including tumor stage, biological characteristics, treatment plan, and patient's overall health status. Although some recent studies have found certain molecules that are related to the prognosis of PC (Friess et al., 1998; Jin et al., 2021; Yu et al., 2010), prognostic indicators at the molecular level are not as convenient and practical as easily obtainable clinical pathological indicators. In this study, we established a high-performance ML model through eight readily available clinical pathological indicators, and confirmed the generalization ability of the model through internal and external validation. ML in this study involved heterogeneous data with regional differences, racial differences, treatment method differences related to years, and different combinations of treatment methods. This is an innovative attempt to help us establish a relatively stable model from complex data.
However, this study has several limitations to be considered. Firstly, the study outcomes may be affected by potential bias as a result of utilizing retrospective data. Secondly, specific biochemical parameters like CA199 were not included in the SEER databases, which warrants further investigation. Additionally, the study did not provide information on the sequence of chemotherapy and surgery. Moreover, due to the potential differences in clinical practices across different countries and regions, the external validation cohort only included Chinese patients. Therefore, the generalizability of the ML model to other countries and populations remains unclear and requires further exploration.