Trial reportBMC gastroenterology2021
Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine.
Trial report in BMC gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- IFN-γ and IL-6 as Key Predicting Biomarkers for Active TB Among PLWH: Results from Four Machine Learning Methods.International journal of general medicine · 2026Article
- Prediction of Surgical Approach in Mitral Valve Disease by XGBoost Algorithm Based on Echocardiographic Features.Journal of clinical medicine · 2023Article
- Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
introductionGastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients.
methodsWe used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset.
resultsIn the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25-37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST.
conclusionsWe developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.
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