ArticleJournal of oncology2021
A Novel Prognostic Nomogram for Gallbladder Cancer after Surgical Resection: A Single-Center Experience.
Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Genetic characteristics and clinical-specific survival prediction in elderly patients with gallbladder cancer: a genetic and population-based study.Frontiers in endocrinology · 2023Trial
- Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection.European radiology · 2022Trial
- Predictive and prognostic models and visualizations of distant metastasis in gallbladder cancer.Medicine · 2025Article
- What eliminates the chance for cure: a multi-center evaluation on 10-year follow-up of gallbladder cancer after surgical resection.Annals of medicine · 2024Article
- Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study.World journal of surgical oncology · 2024Article
- A novel nomogram based on inflammatory-nutritional biomarkers for gallbladder cancer after surgical resection.BMC gastroenterology · 2024Article
- Preoperative Fibrinogen Albumin Ratio is an Effective Biomarker for Prognostic Evaluation of Gallbladder Carcinoma After Radical Resection: A 10-Year Retrospective Study at a Single Center.Journal of inflammation research · 2023Article
- A novel nomogram based on log odds of positive lymph nodes to predict survival for non-metastatic gallbladder adenocarcinoma after surgery.Scientific reports · 2022Article
- The Significance of Tumor Budding and Immunohistochemical Axl Expression in Gallbladder AdenocarcinomasBalkan medical journal · 2022Article
- Prognostic Significance of Tumour Budding and Tumour-infiltrating Lymphocytes in Gallbladder Carcinoma.Journal of the West African College of SurgeonsArticle
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGallbladder cancer (GBC), which accounts for more than 80% of biliary tract malignancies, has a poor prognosis with an overall 5-year survival less than 10%. The study aimed to identify risk factors and develop a predictive model for GBC following surgical resection.
methods98 GBC patients who underwent surgical resection from Guangdong Provincial People's Hospital were enrolled in the study. Cox-regression analysis was performed to identify significant prognostic factors. A nomogram was constructed and Harrell's concordance index, calibration plot, and decision cure analysis were used to evaluate the discrimination and calibration of the nomogram.
resultsLiver resection, tumor size, perineural invasion, surgical margin, and liver invasion were identified as independent risk factors for overall survival (OS) in GBC patients who underwent surgical resection. Based on the selected risk factors, a novel nomogram was constructed. The C-index of the nomogram was 0.777, which was higher than the American Joint Committee on Cancer (AJCC) staging system (0.724) and Nevin staging system (0.659). Decision cure analysis revealed that the nomogram had a better net benefit and the calibration curves for the 1-, 3-, and 5-year survival probabilities were also well matched with the actual survival rates. Lastly, high-risk GBC were stratified based on the scores of the nomogram and we found high-risk GBC were associated with both worse OS and disease-free survival (DFS).
conclusionWe developed a nomogram showing a better predictive capacity for patients' survival of resected GBC than the AJCC staging systems. The established model may help to stratify high-risk GBC and facilitate decision-making in the clinic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.