Trial reportJournal of cancer research and clinical oncology2023
Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer.
Trial report in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Modern Approaches to Diagnosis and Evaluation of Survival Prognosis in Patients with Pancreatic Cancer.International journal of molecular sciences · 2026Pooled it
- Association of tumor associated collagen signature with lymph node metastasis in pancreatic ductal adenocarcinoma.Bioengineering & translational medicine · 2026Article
- Preoperative prediction of metastatic body-tail peripancreatic lymph nodes as a guide for surgical decision-making in pancreatic neck ductal adenocarcinoma.Updates in surgery · 2026Article
- Ultrasound-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma.Frontiers in medicine · 2026Article
- Article
- Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer.World journal of radiology · 2025Article
- Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.World journal of surgical oncology · 2025Article
- Development and validation of a risk prediction model for distant metastasis in muscle-invasive bladder cancer: a retrospective study integrating SEER data with external validation cohort and biomarker analysis.Frontiers in oncology · 2025Article
- Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer.BMC cancer · 2024Article
- Albumin corrected anion gap for predicting in-hospital death among patients with acute myocardial infarction: A retrospective cohort study.Clinics (Sao Paulo, Brazil) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundLymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients.
methods13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike's information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM.
resultA total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700-0.722), 0.700 (95%CI: 0.683-0.717), and 0.845 (95%CI: 0.749-0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility.
conclusionWe constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.
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