Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025
A comprehensive population-based study and predictive survival model for undifferentiated carcinoma of the pancreas.
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Luo Wenhao, Chen Hao, Fu Yifan, Wang Jun, Zhang Taiping
Аннотация
BACKGROUND: Undifferentiated carcinoma of the pancreas (UCP) is a lesser-known type of pancreatic cancer with poor knowledge of epidemiology, clinical features, survival and prognosis outcomes. This article aims to investigate several prognostic factors influencing survival in patients with UCP, with the goal of constructing a nomogram predictive model, aiding clinicians in risk stratification and personalized treatment decision-making.
METHODS: UCP patient data between 2000 and 2019 was extracted from the Surveillance Epidemiology and End Results (SEER) database. Univariate and multivariable Cox regression analysis were conducted to present prognostic factors for overall survival (OS) and cancer-specific survival (CSS) of UCP patients. We applied Kaplan-Meier curves to evaluate the OS and CSS for each feature or therapeutic method. Moreover, a nomogram predictive model was completed to demonstrate the prognosis of UCP patients.
RESULTS: The study comprised 177 participants specifically selected as UCP patients. Kaplan-Meier curves illustrated that UCP patients who were female, underwent primary tumor resection (PTR), received chemotherapy, had tumor sizes equal to or less than 5 cm, and lacked liver metastasis or exhibited histology of osteoclast-like giant cells experienced significantly improved OS and cancer-specific survival CSS compared to their male counterparts, those who did not undergo PTR or did not receive chemotherapy, had tumor sizes larger than 5 cm, with liver metastasis, or histology lacking osteoclast-like giant cells. Multivariable Cox regression analysis revealed that histological type, PTR, chemotherapy, tumor size, and the presence of liver metastasis independently influenced both OS and CSS. The predictive nomogram model demonstrated acceptable predictive performance.
CONCLUSION: The nomogram model, which incorporates histological type, PTR, chemotherapy, tumor size, and liver metastasis, provides valuable guidance and insights for both clinicians and UCP patients.
METHODS: UCP patient data between 2000 and 2019 was extracted from the Surveillance Epidemiology and End Results (SEER) database. Univariate and multivariable Cox regression analysis were conducted to present prognostic factors for overall survival (OS) and cancer-specific survival (CSS) of UCP patients. We applied Kaplan-Meier curves to evaluate the OS and CSS for each feature or therapeutic method. Moreover, a nomogram predictive model was completed to demonstrate the prognosis of UCP patients.
RESULTS: The study comprised 177 participants specifically selected as UCP patients. Kaplan-Meier curves illustrated that UCP patients who were female, underwent primary tumor resection (PTR), received chemotherapy, had tumor sizes equal to or less than 5 cm, and lacked liver metastasis or exhibited histology of osteoclast-like giant cells experienced significantly improved OS and cancer-specific survival CSS compared to their male counterparts, those who did not undergo PTR or did not receive chemotherapy, had tumor sizes larger than 5 cm, with liver metastasis, or histology lacking osteoclast-like giant cells. Multivariable Cox regression analysis revealed that histological type, PTR, chemotherapy, tumor size, and the presence of liver metastasis independently influenced both OS and CSS. The predictive nomogram model demonstrated acceptable predictive performance.
CONCLUSION: The nomogram model, which incorporates histological type, PTR, chemotherapy, tumor size, and liver metastasis, provides valuable guidance and insights for both clinicians and UCP patients.