Evidence map›Paper›PMID 41735720›Full record

ArticleDiscover oncology2026

Clinicopathological features and survival outcomes of patients with different distant and lymph node metastasis in pancreatic cancer.

JianLin Lai, Tiansheng Lin, Lingpeng Tang, Liangyu Wei, Shizhao Chen, Miao Gao, Bofei Xu, Junxuan Huang, Zihan Qiu, Haoxiang Zhang

Abstract read
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

JianLin Lai *Department of Hepatopancreatobiliary Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, 350001, Fuzhou, China.
Tiansheng Lin *Department of Hepatopancreatobiliary Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, 350001, Fuzhou, China.
Lingpeng Tang *Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China.
Liangyu WeiSchool of Medicine, Fuzhou University, Fuzhou, 350108, China.
Shizhao ChenFujian Medical University, Fuzhou, 350004, China.
Miao GaoFujian Medical University, Fuzhou, 350004, China.
Bofei XuFujian Medical University, Fuzhou, 350004, China.
Junxuan HuangFujian Medical University, Fuzhou, 350004, China.
Zihan QiuFujian Medical University, Fuzhou, 350004, China.
Haoxiang ZhangDepartment of Hepatopancreatobiliary Surgery, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, 350001, Fuzhou, China. z_haoxiang@163.com.

Funding

Fujian Provincial Health Commission 2020QNA004Fujian Provincial Health Technology Project 2024GGA004Fujian Provincial Natural Science Foundation of China 2024J08244National Natural Science Foundation of China 32401082the Joint Funds for the Innovation of Science and Technology, Fujian Province 2023Y9311
6 · The paper itself

Abstract

backgroundPancreatic cancer is a highly lethal malignancy, usually diagnosed when metastasis is present. Detailed analysis of distant (DM) and lymph node (LN) metastasis patterns is crucial for prognosis. This study evaluated metastasis-specific prognosis in pancreatic cancer patients from 2010 to 2016 and developed a validated nomogram for survival prediction.

methodsData of patients with metastatic pancreatic cancer who received chemotherapy or radiotherapy were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Pearson’s chi-square test was utilized to analyze clinicopathological variables. Kaplan–Meier survival analysis and log-rank tests were employed to evaluate overall survival and prognostic outcomes. A nomogram for prognosis prediction was constructed using univariate and multivariate analyses, combined with the Cox proportional hazards regression model.

resultsDistant metastasis (DM) most commonly occurred in the liver (44.8%), followed by the bone (9.5%). Approximately 63.2% of patients had single-site metastasis, whereas 26.0% had metastases in two distant organs. Regional lymph node (LN) involvement was observed in 14.3% of pancreatic cancer patients, among whom only 3.7% concurrently had DM, with the liver being the most frequently involved organ, in which the liver was the most common organ. At the end of the follow-up period 152 patients in 1805 were alive. For pancreatic carcinoma patients with DM the median survival decreased to 4.0 months, while with LN that was 31.05 months. The nomogram was proven to be acceptable consistency in train and validation sets, and the c-index was 0.763.

conclusionWe conducted a comparative analysis of clinicopathological characteristics and clinical outcomes among cases of metastatic pancreatic cancer. Furthermore, we developed a prognostic nomogram that demonstrated excellent performance in both the training and validation cohorts.

Indexed as

MetastasisNomogramPancreatic cancerPrognosis prediction modelSEER database

Identifiers

PMID41735720
PMCPMC13038698

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