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Updated: Sep 8, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Histopathological subtypes independently predict survival in ampullary carcinoma: a contemporary SEER nomogram study
Minghui Zhang1,2, Kuinan Tong2,3, Chao Jing2,3
1Cardiovascular Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background:
Ampullary adenocarcinoma (AAC) has distinct histopathological subtypes impacting prognosis, yet American Joint Committee on Cancer (AJCC) staging overlooks this distinction. Few large-scale studies have evaluated cancer-specific survival (CSS) or integrated subtypes into prognostic nomograms. This study aimed to evaluate the prognostic impact of histopathological subtypes on CSS in AAC patients and to develop a validated nomogram incorporating subtype for individualized risk stratification.
Methods:
Patients diagnosed between 2004-2021 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. CSS was compared using Kaplan-Meier and Cox models. A nomogram incorporating histopathological subtype was developed and validated using C-index, receiver operating characteristic (ROC) curves, and decision curve analysis.
Results:
Among 726 patients, 385 (53.0%) had intestinal type and 341 (47.0%) had pancreatobiliary/mixed type. Pancreatobiliary/mixed subtypes showed significantly worse CSS [median: 39 vs. 168 months; adjusted hazard ratio (HR) =1.60, 95% confidence interval (CI): 1.22-2.09, P<0.001] and higher proportions of T3 tumors (57.5% vs. 34.5%), nodal involvement (62.8% vs. 51.4%), and poor differentiation (35.8% vs. 19.2%; all P<0.01). The nomogram incorporating subtype, age, tumor (T)/node (N)/metastasis (M) stage, surgery, and chemotherapy achieved a C-index of 0.751 and areas under the curve (AUCs) of 0.812-0.837 for 1-, 3-, and 5-year CSS.
Conclusions:
Histopathological subtype independently predicts CSS in ampullary carcinoma. This subtype-inclusive nomogram outperforms conventional staging for individualized prediction, supporting precision oncology approaches.
