Development and External Validation of Survival Prediction Model for Pancreatic Cancer Using Two Nationwide Database: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP)

A new staging system for postoperative prognostication in pancreatic ductal adenocarcinoma

The current TNM staging system for pancreatic ductal adenocarcinoma (PDAC) has revised the definitions of T and N categories as well as stage groups. However, studies validating these modifications have yielded inconsistent results. The existing TNM staging system in prognostic prediction remains unsatisfactory. The prognosis of PDAC is closely associated with pathological and biological factors. Herein, we propose a new staging system incorporating distant metastasis, postoperative serum levels of CA19-9 and CEA, tumor size, lymph node metastasis, lymphovascular involvement, and perineural invasion to enhance the accuracy of prognosis assessment. The proposed staging system exhibited a strong correlation with both overall survival and recurrence-free survival, effectively stratifying survival into five distinct tiers. Additionally, it had favorable discrimination and calibration. Thus, the proposed staging system demonstrates superior prognostic performance compared to the TNM staging system, and can serve as a valuable complementary tool to address the limitations of TNM staging in prognostication.

Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods—random survival forests (RSF) and support vector machines (SVM)—for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

Clinicopathological Features, Prognostic Factors and Survival in Patients With Pancreatic Cancer Bone Metastasis

Purpose

The purpose of this study is to reveal the clinicopathological features and identify risk factors of prognosis among patients with pancreatic cancer bone metastasis (PCBM).

Patients and Methods

Patients with PCBM were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Independent predictors for survival of those patients were determined by the univariate and multivariate Cox regression analysis. Forest plots were drawn by GraphPad 8.0.1 and used to visually display the results of multivariate analysis.

Results

We identified 2072 eligible PCBM patients, of which 839 patients (40.5%) were female. Patients with age >60 years accounted for 70.6%. Multivariable Cox regression analysis indicated that age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). Kaplan–Meier survival curves showed that for patients with PCBM, age ≤60 years, non-ductal adenocarcinoma type, chemotherapy, no liver metastasis, no lung metastasis, and married status were correlated with increased survival. This population-based study showed that 1-year OS and CSS were 13.6% and 13.7%, respectively.

Conclusion

The present study identified six independent predictors of prognosis in PCBM, including age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status. Knowledge of these survival predictors is helpful for clinicians to accelerate clinical decision process and design personalized treatment for patients with PCBM.

Distinct Survival Outcomes in Subgroups of Stage III Pancreatic Cancer Patients: Taiwan Cancer Registry and Surveillance, Epidemiology and End Results registry

Purpose

Pancreatic cancer is one of the most malignant cancers with poor survival. The latest edition of the American Joint Committee on Cancer (AJCC) staging system classifies the majority of operable pancreatic cancer patients as stage-III, while dramatic heterogeneity is observed among these patients. Therefore, subgrouping is required to accurately predict their prognosis and define a treatment plan. This study conducts a cohort study to provide a more precise classification system for stage-III pancreatic cancer patients by utilizing clinical variables.

Methods

We analyzed survival using log-rank tests, univariate Cox-regression models, and Kaplan-Meier survival curves for stage-III pancreatic ductal adenocarcinoma (PDAC) patients from the Taiwan Cancer Registry (TCR). Patients were further divided into subgroups using classification and regression tree (CART) algorithm. All results were validated using the SEER database.

Results

Among stage-III PDAC patients, lymph node and tumor grade showed significant association with survival. Patients with N2 stage had higher mortality risks (hazard ratio [HR] = 2.30, 95% confidence interval [CI] 1.71–3.08, p < 0.0001) than N0 patients. Patients with grade 3 also had higher risk of mortality (HR = 3.80, 95% CI 2.25–6.39, p < 0.0001) than grade 1 patients. The CART algorithm stratified stage-III patients into four subgroups with significantly different survival rates. The median survival of the four subgroups was 23.5, 18.4, 14.5, and 9.0 months, respectively (p < 0.0001). Similar results were observed with SEER data.

Conclusions

Lymph node involvement and tumor grade are predictive factors for survival in stage-III PDAC patients. This new precise classification system can be used to guide treatment planning in advanced-stage pancreatic cancer.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-021-11030-w.