ArticleWorld journal of gastrointestinal surgery2022
Development of an innovative nomogram of risk factors to predict postoperative recurrence of gastrointestinal stromal tumors.
Article in World journal of gastrointestinal surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Survival Prediction Tools in Patients With Gastrointestinal Stromal Tumors: A Systematic Review and Meta-Analysis.Journal of surgical oncology · 2026Pooled it
- Development and validation of a prognostic nomogram for gastric GIST patients under 65 years of age.Discover oncology · 2025Article
- The prognostic role of gastrointestinal bleeding in patients with a primary gastrointestinal stromal tumor: a long-term follow-up study.Journal of gastrointestinal oncology · 2023Article
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7 authors.
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Abstract
backgroundThere are many staging systems for gastrointestinal stromal tumors (GISTs), and the risk indicators selected are also different; thus, it is not possible to quantify the risk of recurrence among individual patients.
aimTo develop and internally validate a model to identify the risk factors for GIST recurrence after surgery.
methodsThe least absolute shrinkage and selection operator (LASSO) regression model was performed to identify the optimum clinical features for the GIST recurrence risk model. Multivariable logistic regression analysis was used to develop a prediction model that incorporated the possible factors selected by the LASSO regression model. The index of concordance (C-index), calibration curve, receiver operating characteristic curve (ROC), and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the predictive model. Internal validation of the clinical predictive capability was also evaluated by bootstrapping validation.
resultsThe nomogram included tumor site, lesion size, mitotic rate/50 high power fields, Ki-67 index, intracranial necrosis, and age as predictors. The model presented perfect discrimination with a reliable C-index of 0.836 (95%CI: 0.712-0.960), and a high C-index value of 0.714 was also confirmed by interval validation. The area under the curve value of this prediction nomogram was 0.704, and the ROC result indicated good predictive value. Decision curve analysis showed that the predicting recurrence nomogram was clinically feasible when the recurrence rate exceeded 5% after surgery.
conclusionThis recurrence nomogram combines tumor site, lesion size, mitotic rate, Ki-67 index, intracranial necrosis, and age and can easily predict patient prognosis.
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