Identifying optimal candidates for tumor resection among borderline and locally advanced pancreatic cancer: A population-based predictive model

医学 胰腺癌 切除术 内科学 肿瘤科 癌症
作者
Zhenhua Lu,Weiwei Shao,Xiaolei Shi,Tianhua Tan,Cheng Xing,Zhe Li,Jingyong Xu,Hongyuan Cui,Jinghai Song
出处
期刊:Pancreatology [Elsevier]
标识
DOI:10.1016/j.pan.2022.01.004
摘要

Whether patients with borderline resectable and locally advanced pancreatic cancer (BR/LAPC) benefit from resection of the primary cancer is controversial. We developed a nomogram to screen who would benefit from surgery for the primary tumor. We identified patients from the Surveillance, Epidemiology, and End Results (SEER) database and then divided them into surgical and non-surgical groups. A 1:1 propensity score matching (PSM) was used to mitigate the bias. We hypothesized that patients who underwent surgery would benefit from surgery by having a longer median overall survival (OS) than patients who did not undergo surgery. Univariate and multivariate logistic regression analyses were used to determine the variables affecting surgical outcomes, and a nomogram was created based on the multivariate logistic results. Finally, we verified the discrimination and calibration of the nomogram with receiver operator characteristic (ROC) curve and calibration plots. A total of 518 pairs of surgical and non-surgical pancreatic cancer patients were matched after PSM. Survival curves showed longer OS and CSS in the surgical group than in the non-surgical group, median survival times were 14 months versus 8 months and 16 months versus 9 months, respectively. In the surgical group, 340 (65.63%) patients have a longer survival time than 8 months (beneficial group). Multifactorial logit regression results showed that including age, tumor size, degree of differentiation, and chemotherapy were significant influences on the benefit of surgery for primary tumors and were used as predictors to construct a nomogram. The area under the ROC curve (AUC) reached 0.747 and 0.706 in the training and validation sets. We developed a practical predictive model to support clinical decision-making that can be used to help clinicians determine if there is a benefit to surgical resection of the primary tumor in patients with BR/LAPC.

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