ArticleWorld journal of surgical oncology2023
Establishment and verification of prediction model of occult peritoneal metastasis in advanced gastric cancer.
Article in World journal of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Clinical Value of Laparoscopic Exploration in Diagnosing and Dynamically Assessing Occult Peritoneal Metastasis in Gastric Cancer.Annals of surgical oncology · 2026Article
- Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM.Journal of gastrointestinal oncology · 2026Article
- Construction of a nomogram for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer based on circulating tumor cells and fibrinogen to albumin ratio.Discover oncology · 2026Article
- Preoperative clinical-imaging nomogram for peritoneal metastasis in gastric cancer: a retrospective cohort study.BMC gastroenterology · 2026Article
- A novel multimodal combining radiomics and tumor-stroma ratio (TSR) improves diagnosis of gastric cancer peritoneal metastasis.Clinical & experimental metastasis · 2026Article
- A risk prediction model for metachronous peritoneal metastasis after radical gastrectomy in gastric cancer based on peripheral blood inflammatory markers and tumor pathological features.American journal of translational research · 2026Article
- Intraperitoneal Co-Delivery of Claudin18.2×41BB and EpCAM×CD3 Bispecific Antibodies via mRNA-LNPs Synergistically Suppresses Gastric Cancer Peritoneal Metastasis Through T Cell Co-Activation.International journal of nanomedicine · 2026Article
- Current and evolving practices of carbohydrate antigens in gastric cancer: a narrative review.Translational cancer research · 2025Review
- Article
- Assessing the Adequacy of a Prediction Model.Indian journal of community medicine : official publication of Indian Association of Preventive & Social MedicineReview
- Predicting early gastric cancer risk using machine learning: A population-based retrospective study.Digital healthArticle
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8 authors.
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Abstract
backgroundTo investigate the risk factors associated with the development of occult peritoneal metastasis in advanced gastric cancer, and establish and externally validate a nomogram for predicting the occurrence of occult peritoneal metastasis in patients with advanced gastric cancer.
methodsA total of 111 patients with advanced gastric cancer who underwent laparoscopic exploration or peritoneal lavage cytology examination at the Affiliated Drum Tower Hospital of Nanjing University Medical School from August 2014 to December 2021 were retrospectively analyzed. The patients diagnosed between 2019 and 2021 were assigned to the training set (n = 64), while those diagnosed between 2014 and 2016 constituted the external validation set (n = 47). In the training set, patients were classified into two groups based on preoperative imaging and postoperative pathological data: the occult peritoneal metastasis group (OPMG) and the peritoneal metastasis negative group (PMNG). In the validation set, patients were classified into the occult peritoneal metastasis group (CY1P0, OPMG) and the peritoneal metastasis negative group (CY0P0, PMNG) based on peritoneal lavage cytology results. A nomogram was constructed using univariate and multivariate analyses. The performance of the nomogram was evaluated using Harrell's C-index, the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plots.
resultsThis study analyzed 22 potential variables of OPM in 111 gastric cancer patients who underwent laparoscopic exploration or peritoneal lavage cytology examination. Logistic regression analysis results showed that Lauren classification, CLDN18.2 score and CA125 were independent risk factors for OPM in patients with gastric cancer. We developed a simple and easy-to-use prediction nomogram of occult peritoneal metastasis in advanced gastric cancer. This nomogram had an excellent diagnostic performance. The AUC of the bootstrap model in the training set was 0.771 and in the validation set was 0.711. This model showed a good fitting and calibration and positive net benefits in decision curve analysis.
conclusionWe have developed a prediction nomogram of OPM for gastric cancer. This novel nomogram has the potential to enhance diagnostic accuracy for occult peritoneal metastasis in gastric cancer patients.
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