ArticleCancers2022
Machine Learning Improves the Prediction Rate of Non-Curative Resection of Endoscopic Submucosal Dissection in Patients with Early Gastric Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer.Journal of oral microbiology · 2025Article
- Risk factors analysis for pathological upgrade after endoscopic submucosal dissection in patients with gastric intraepithelial neoplasia: a single-center retrospective study.Therapeutic advances in gastroenterology · 2025Article
- Risk factors for pathological upgrading and noncurative resection in patients with gastric mucosal lesions after endoscopic submucosal dissection.BMC gastroenterology · 2024Article
- Early gastric cancer recurrence after endoscopic submucosal dissection: Not to be ignored!World journal of gastrointestinal oncology · 2024Article
- Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods.Cancer research and treatment · 2023Article
- Research trends in endoscopic applications in early gastric cancer: A bibliometric analysis of studies published from 2012 to 2022.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-curative resection (NCR) of early gastric cancer (EGC) after endoscopic submucosal dissection (ESD) can increase the burden of additional treatment and medical expenses. We aimed to develop a machine-learning (ML)-based NCR prediction model for EGC prior to ESD. We obtained data from 4927 patients with EGC who underwent ESD between January 2006 and February 2020. Ten clinicopathological characteristics were selected using extreme gradient boosting (XGBoost) and were used to develop a ML-based model. Dataset was divided into the training and internal validation sets and verified using an external validation set. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were evaluated. The performance of each model was compared by using the Delong test. A total of 1100 (22.1%) patients were identified as being treated non-curatively with ESD. Seven ML-based NCR prediction models were developed. The performance of NCR prediction was highest in the XGBoost model (AUROC, 0.851; 95% confidence interval, 0.837-0.864). When we compared the prediction performance by the Delong test, XGBoost (
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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.