Pancreatic adenosquamous carcinoma (PASC) is a heterogeneous group of primary pancreatic cancers characterized by the coexistence of both glandular and squamous differentiation. The aim of this study was to develop nomograms to predict survival outcomes in patients with PASC.
In this retrospective study, data on PASC, including clinicopathological characteristics, treatments, and survival outcomes, were collected from the SEER database between 2000 and 2018. The primary endpoints were overall survival (OS) and cancer-specific survival (CSS). The eligible patients were randomly divided into development cohort and validation cohort in a 7:3 ratio. The nomograms for prediction of OS and CSS were constructed by the development cohort using a LASSO-Cox regression model, respectively. Besides the model performance was internally and externally validated by examining the discrimination, calibration, and clinical utility.
A total of 632 consecutive patients who had been diagnosed with PASC were identified and randomly divided into development (n = 444) and validation (n = 188) cohorts. In the development cohort, the estimated median OS was 7.0 months (95% CI: 6.19–7.82) and the median CSS was 7.0 months (95% CI: 6.15–7.85). In the validation cohort, the estimated median OS was 6.0 months (95% CI: 4.46–7.54) and the median CSS was 7.0 months (95% CI: 6.25–7.75). LASSO-penalized COX regression analysis identified 8 independent predictors in the OS prediction model and 9 independent risk factors in the CSS prediction model: age at diagnosis, gender, year of diagnosis, tumor location, grade, stage, size, lymph node metastasis, combined metastasis, surgery, radiation, and chemotherapy. The Harrell C index and time-dependent AUCs manifested satisfactory discriminative capabilities of the models. Calibration plots showed that both models were well calibrated. Furthermore, decision curves indicated good utility of the nomograms for decision-making.
Nomogram-based models to evaluate personalized OS and CSS in patients with PASC were developed and well validated. These easy-to-use tools will be useful methods to calculate individualized estimate of survival, assist in risk stratification, and aid clinical decision-making.