Development and validation of the Massachusetts General Hospital/Memorial Sloan Kettering nomogram to predict overall survival of resected patients with pancreatic ductal adenocarcinoma treated with neoadjuvant therapy

Prognostication in patients undergoing resection for pancreatic ductal adenocarcinoma following neoadjuvant therapy remains challenging. In this study, we aimed to develop and validate a nomogram for the prediction of overall survival of these patients. Patients who underwent neoadjuvant therapy followed by surgical resection at the Massachusetts General Hospital were analyzed (training cohort). Patients from Memorial Sloan Kettering were included as a validation cohort. A nomogram to predict overall survival was designed, trained, and subjected to internal (bootstrap) validation. A total of 325 patients were identified from Massachusetts General Hospital. Multivariable Cox regression analysis demonstrated that age (hazard ratio 1.828, 95% confidence interval 1.251–2.246; P = .007), serum carbohydrate antigen 19-9 ≥ 37 U/mL (HR 1.602, 95% confidence interval 1.187–3.258; P = .015), tumor size (hazard ratio 2.278, 95% confidence interval 1.405–4.368; P = .003), nodal status (hazard ratio 1.309, 95% confidence interval 1.108–2.439; P = .032), and R1 tumor resection (hazard ratio 1.481, 95% confidence interval 1.049–2.091; P = .026) were independent factors associated with overall survival. A nomogram that incorporated these significant prognostic factors was established. The calibration plots demonstrated high concordance between predictive nomogram values and actual overall survival for 1-year, 3-year, and 5-year overall survival. The model demonstrated excellent discriminatory power in both the Massachusetts General Hospital and Memorial Sloan Kettering cohorts, with adjusted Harrel’s concordance index values of 0.729 and 0.712, respectively. In this report, we established and validated a novel nomogram for predicting the survival of patients who underwent neoadjuvant therapy followed by pancreatectomy. This model allows clinicians to better estimate the survival of these specific patients.