Trial reportWorld journal of gastroenterology2024
Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer.
Trial report in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 18 citations in OpenAlex.
- Clinical significance of intraoperative dissection time and machine learning-based prediction in laparoscopic distal gastrectomy for gastric cancer.Surgical endoscopy · 2026Article
- Development and validation of an interpretable machine learning model for predicting postoperative complications after transanal total mesorectal excision: a multicenter study.Techniques in coloproctology · 2026Article
- Artificial Intelligence Applications in Gastric Cancer Surgery: Bridging Early Diagnosis and Responsible Precision Medicine.Journal of clinical medicine · 2026Review
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer.World journal of gastrointestinal surgery · 2025Article
- Predictive factors for successful conversion therapy in gastric cancer with peritoneal metastasis: a case-control study.Journal of gastrointestinal oncology · 2025Article
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- Predictive role of a novel nutrition and inflammation-based model in the short-term clinical outcomes and health-related quality of life of patients undergoing radical gastrectomy: a retrospective cohort study.Discover oncology · 2025Article
- Impact of visceral fat distribution on postoperative complications in high-aged patients undergoing gastric cancer surgery: A cross-sectional study.World journal of gastroenterology · 2025Article
- Review
- Development and validation of a nomogram model for predicting infection after radical resection of gastric cancer.Pakistan journal of medical sciences · 2025Article
- Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer.Frontiers in oncology · 2025Article
- Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients.Frontiers in surgery · 2025Article
- Effect of preoperative nutritional risk index on 30-day postoperative complications in patients with gastric cancer: a retrospective cohort study.Frontiers in oncology · 2025Article
- Inflammatory Bowel Disease Mediates the Causal Relationship Between Gut Microbiota and Colorectal Cancer: Identification of Therapeutic Targets and Predictive Modeling.Journal of Cancer · 2025Article
- Development and validation of a machine learning model for predicting early postoperative complications after radical gastrectomy.Frontiers in oncology · 2025Article
- Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.World journal of gastroenterology · 2024Article
Corrections and comments
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Authors and funding
21 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLaparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue.
aimTo develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications.
methodsIn total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model, and 398 patients were included in the validation group. The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected. Three machine learning methods, lasso regression, random forest, and artificial neural networks, were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy, and their prediction efficacy and accuracy were evaluated.
resultsThe constructed complication model, particularly the random forest model, could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy. It exhibited stable performance in external validation and is worthy of further promotion in more centers.
conclusionUsing the risk factors identified in multicenter datasets, highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established. We hope to facilitate the diagnosis and treatment of preoperative and postoperative decision-making by using these models.
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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.