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

Zhenhua Lu; Weiwei Shao; Xiaolei Shi; Tianhua Tan; Cheng Xing; Zhe Li; Jingyong Xu; Hongyuan Cui; Jinghai Song

doi:10.1016/j.pan.2022.01.004

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

Pancreatology. 2022 Mar;22(2):286-293. doi: 10.1016/j.pan.2022.01.004. Epub 2022 Jan 12.

Authors

Zhenhua Lu¹, Weiwei Shao², Xiaolei Shi³, Tianhua Tan², Cheng Xing³, Zhe Li³, Jingyong Xu³, Hongyuan Cui⁴, Jinghai Song⁵

Affiliations

¹ Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China; The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, PR China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, PR China.
² Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China; Peking Union Medical College (PUMC), Chinese Academy of Medical Sciences & PUMC, Beijing, PR China.
³ Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
⁴ Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China; Qinghai University Affiliated Hospital, PR China. Electronic address: cuihongyuan3921@bjhmoh.cn.
⁵ Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China; The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, PR China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, PR China. Electronic address: jhaisong2003@126.com.

PMID: 35058130
DOI: 10.1016/j.pan.2022.01.004

Abstract

Background: 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.

Methods: 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.

Results: A total of 518 pairs of surgical and non-surgical pancreatic cancer patients were matched after PSM. Survival curves showed longer OS in the surgical group than in the non-surgical group, median survival times were 14 months versus 8 months. 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.

Conclusion: 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.

Keywords: Locally advanced pancreatic cancer; Nomogram; Predictive model; Surgery.

MeSH terms

Humans
Nomograms*
Pancreatic Neoplasms* / surgery
Prognosis
Propensity Score
SEER Program