ArticleThe British journal of surgery2021
Prediction of long-term survival after gastrectomy using random survival forests.
Article in The British journal of surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 33 citations in OpenAlex.
- Comparative contributions of visceral adiposity and muscle-related parameters to postoperative complications after gastrectomy: a domain-based analysis.BMC cancer · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.NPJ precision oncology · 2026Article
- Machine learning-driven dynamic prognosis for primary colorectal lymphoma.Scientific reports · 2026Article
- Development and validation of a prognostic nutritional-inflammatory-immune score and web-based dynamic nomogram for gastric cancer following radical gastrectomy: a multicenter study.Frontiers in nutrition · 2026Article
- Comparison of random survival forest and Cox regression for long-term prognosis of pediatric Wilms tumor: based on the SEER database and external validation cohort from China.Discover oncology · 2025Article
- Identification of Crucial Genes Associated With MYCN-Driven Neuroblastoma Based on Single-Cell Analysis and Machine Learning.Cancer medicine · 2025Article
- Predicting gastric cancer survival using machine learning: A systematic review.World journal of gastrointestinal oncology · 2025Article
- Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.BMC gastroenterology · 2025Article
- Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers.BMC cancer · 2025Article
- Article
- Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma.Discover oncology · 2025Article
- 5-hydroxymethylcytosines from circulating cell-free DNA as noninvasive prognostic markers for gastric cancer.PloS one · 2025Article
- Random survival forest model in patients with epithelial ovarian cancer: a study based on SEER database and single center data.American journal of cancer research · 2025Article
- Outcomes after gastrectomy according to the Gastrectomy Complications Consensus Group (GCCG) in the Dutch Upper GI Cancer Audit (DUCA).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.Scientific reports · 2024Article
- Cachexia index for prognostication in surgical patients with locally advanced oesophageal or gastric cancer: multicentre cohort study.The British journal of surgery · 2024Article
- Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers.Frontiers in oncology · 2024Article
- Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundNo well validated and contemporaneous tools for personalized prognostication of gastric adenocarcinoma exist. This study aimed to derive and validate a prognostic model for overall survival after surgery for gastric adenocarcinoma using a large national dataset.
methodsNational audit data from England and Wales were used to identify patients who underwent a potentially curative gastrectomy for adenocarcinoma of the stomach. A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and discrimination (time-dependent area under the receiver operator curve (tAUC)) assessed.
resultsThe median survival of the cohort was 69 months, with a 5-year survival of 53.2 per cent. Ten variables were found to influence survival significantly and were included in the final model, with the most important being lymph node positivity, pT stage and achieving an R0 resection. Patient characteristics including ASA grade and age were also influential. On validation the model achieved excellent performance with a 5-year tAUC of 0.80 (95 per cent c.i. 0.78 to 0.82) and good agreement between observed and predicted survival probabilities. A wide spread of predictions for 3-year (14.8-98.3 (i.q.r. 43.2-84.4) per cent) and 5-year (9.4-96.1 (i.q.r. 31.7-73.8) per cent) survival were seen.
conclusionsA prognostic model for survival after a potentially curative resection for gastric adenocarcinoma was derived and exhibited excellent discrimination and calibration of predictions.
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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.