ArticleWorld journal of gastroenterology2019
Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer.
Article in World journal of gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Two decades of progress in gastric cancer peritoneal metastasis: a bibliometric perspective on molecular mechanisms and therapeutic innovations.Frontiers in oncology · 2025Pooled it
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.BMC cancer · 2025Article
- Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.Cancer medicine · 2024Article
- The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024Article
- Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer.Oncology letters · 2023Article
- Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer.BMC cancer · 2023Article
- Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis-A Pilot Comparative Study.Journal of personalized medicine · 2023Article
- Machine learning in gastrointestinal surgery.Surgery today · 2022Review
- Identification of specific role of SNX family in gastric cancer prognosis evaluation.Scientific reports · 2022Article
- [Technical innovations and future perspectives].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2022Review
- Review
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Artificial intelligence in gastric cancer: a translational narrative review.Annals of translational medicine · 2021Review
Corrections and comments
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Authors and funding
17 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundBecause of the powerful abilities of self-learning and handling complex biological information, artificial neural network (ANN) models have been widely applied to disease diagnosis, imaging analysis, and prognosis prediction. However, there has been no trained preoperative ANN (preope-ANN) model to preoperatively predict the prognosis of patients with gastric cancer (GC).
aimTo establish a neural network model that can predict long-term survival of GC patients before surgery to evaluate the tumor condition before the operation.
methodsThe clinicopathological data of 1608 GC patients treated from January 2011 to April 2015 at the Department of Gastric Surgery, Fujian Medical University Union Hospital were analyzed retrospectively. The patients were randomly divided into a training set (70%) for establishing a preope-ANN model and a testing set (30%). The prognostic evaluation ability of the preope-ANN model was compared with that of the American Joint Commission on Cancer (8
resultsWe used the variables that were statistically significant factors for the 3-year overall survival as input-layer variables to develop a preope-ANN in the training set. The survival curves within each score of the preope-ANN had good discrimination (
conclusionThe preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to that of pTNM stage.
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