The impact of adjuvant therapy on outcome in UICC stage I pancreatic cancer

Adjuvant chemotherapy in pancreatic cancer at different AJCC stages: a propensity score matching analysis

OBJECTIVE

In the treatment of resectable pancreatic cancer, adjuvant chemotherapy is viewed as essential. However, it is yet unclear how well adjuvant chemotherapy works at different illness stages. This study aims to investigate the efficacy of adjuvant chemotherapy in various pancreatic cancer stages.

MATERIALS AND METHODS

Patients with pancreatic cancer who underwent surgical intervention at Sun Yat-sen University Cancer Center between January 2018 and January 2021 were included in this retrospective analysis.

RESULTS

168 patients were divided into two groups: the group receiving adjuvant chemotherapy (AC) and the group receiving independent surgery (no-AC). Survival analysis reveals that among stage I patients, the AC group demonstrates significant superiority over the no-AC group in terms of recurrence-free survival (RFS) and overall survival (OS) (P = 0.0028; P = 0.022). While there was no discernible difference in RFS between the AC and no-AC groups for patients with stage II illness (P = 0.69), the AC group significantly outperformed the no-AC group in terms of OS (P = 0.047). There was no discernible difference in RFS or OS between the AC and no-AC groups for patients with stage III pancreatic cancer (P = 0.40 and P = 0.20, respectively).

CONCLUSIONS

The administration of adjuvant chemotherapy has been shown to improve the prognosis of patients diagnosed with stage I and II pancreatic cancer. However, its efficacy is limited in individuals with stage III pancreatic cancer. Therefore, there is an urgent need to investigate and develop more effective therapeutic options for patients in the advanced stage.

Impact of para aortic lymph node removal on survival following resection for pancreatic adenocarcinoma

INTRODUCTION

For PDAC patients undergoing resection, it remains unclear whether metastases to the paraaortic lymph nodes (PALN+) have any prognostic significance and whether metastases should lead to the operation not being carried out. Our hypothesis is that PALN + status would be associated with short overall survival (OS) compared with PALN-, but longer OS compared with patients undergoing surgical exploration only (EXP).

METHODS

Patients with registered PALN removal from the nationwide Danish Pancreatic Cancer Database (DPCD) from May 1st 2011 to December 31st 2020 were assessed. A cohort of PDAC patients who only had explorative laparotomy due to non-resectable tumors were also included (EXP group). Survival analysis between groups were performed with cox-regression in a multivariate approach including relevant confounders.

RESULTS

A total of 1758 patients were assessed, including 424 (24.1%) patients who only underwent explorative surgery leaving 1334 (75.8%) patients for further assessment. Of these 158 patients (11.8%) had selective PALN removal, of whom 19 patients (12.0%) had PALN+. Survival analyses indicated that explorative surgery was associated with significantly shorter OS compared with resection and PALN + status (Hazard Ratio 2.36, p < 0.001). No difference between PALN + and PALN- status could be demonstrated in resected patients after controlling for confounders.

CONCLUSION

PALN + status in patients undergoing resection offer improved survival compared with EXP. PALN + should not be seen as a contraindication for curative intended resection.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.