ArticleInternational journal of surgery (London, England)2025
Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.
Article in International journal of surgery (London, England), 2025. 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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3 citing papers in PubMed.
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Expert consensus on multidisciplinary management of laparoscopic-endoscopic cooperative surgery combined with sentinel lymph node navigation surgery for early gastric cancer (2026 edition).Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Development and Validation of an Individualized Nomogram to Identify Undifferentiated-Predominant Mixed-Type Early Gastric Cancer.Journal of digestive diseasesObservational
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18 authors.
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Abstract
backgroundRecent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LNM) in early gastric cancer (EGC), yet tools for prediction grounded in the Lauren classification remain scarce.
methodsData of 6468 patients diagnosed with EGC from fifteen Chinese high-volume cancer centers between January 2005 and December 2015 were retrospectively analyzed. Utilizing multivariate logistic regression analysis and the multilayer perceptron (MLP) prediction algorithm, a nomogram and an artificial neural network (ANN) model were developed and validated, respectively, for predicting the likelihood of LNM in non-intestinal EGC cases. The models' performances were evaluated and a comparative analysis of their parameters was undertaken. Subsequently, in-depth risk stratification analyses were performed around the two models.
resultsNon-intestinal type EGC demonstrated an elevated LNM rate and inferior prognosis compared to the intestinal type. Both nomogram and ANN model were developed and performed well in discrimination, calibration and clinical utility. Notably, the ANN model surpassed the nomogram in specificity (95.8% vs. 71.3%, P < 0.001), positive predictive value (PPV) (62.0% vs. 36.2%, P < 0.001) and overall accuracy (82.7% vs. 70.5%, P < 0.001). Patients with different risk strata derived from the nomogram, ANN model, and their combined application exhibited significantly different outcomes. The extent of lymph node dissection significantly influenced prognoses in high-risk patients identified by the combined model, whereas the anatomical location of metastatic lymph nodes did not.
conclusionThe ANN model established in this study can screen the patients at high risk of LNM in non-intestinal type EGC more accurately. Considering the high extragastric LNM rate observed in the high-risk stratum, radical gastrectomy combined with D2 lymph node dissection is strongly recommended.
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