ArticleJournal of thoracic disease2020
An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients.
Article in Journal of thoracic disease, 2020. 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.
- Deep learning model for the prediction of lymph node metastasis in esophageal squamous cell carcinoma using MIP FDG-PET images: a retrospective validation study.Japanese journal of radiology · 2026Article
- A Weighted Neural Network Model Based on Laboratory Tests for Identifying Lymph Node Metastases in Esophageal Squamous Cell Carcinomas.Biosensors · 2026Article
- Decoding core genes and intercellular communication in osteosarcoma: bioinformatic investigation and immune cell profiling for diagnostic and therapeutic insights.Discover oncology · 2024Article
- Machine learning in gastrointestinal surgery.Surgery today · 2022Review
- Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.Frontiers in cardiovascular medicine · 2022Article
- Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.Diagnostics (Basel, Switzerland) · 2021Review
- Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients.Frontiers in oncology · 2021Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent preoperative staging for lymph nodal status remains inaccurate. The purpose of this study was to build an artificial neural network (ANN) model to predict pathologic nodal involvement in clinical stage I-II esophageal squamous cell carcinoma (ESCC) patients and then validated the performance of the model.
methodsA total of 523 patients (training set: 350; test set: 173) with clinical staging I-II ESCC who underwent esophagectomy and reconstruction were enrolled in this study. Their post-surgical pathological results were assessed and analysed. An ANN model was established for predicting pathologic nodal positive patients in the training set, which was validated in the test set. A receiver operating characteristic (ROC) curve was also created to illustrate the performance of the predictive model.
resultsOf the enrolled 523 patients with ESCC, 41.3% of the patients were confirmed pathologic nodal positive (216/523). The ANN staging system identified the tumour invasion depth, tumour length, dysphagia, tumour differentiation and lymphovascular invasion (LVI) as predictors for pathologic lymph node metastases. The C-index for the ANN model verified in the test set was 0.852, which demonstrated that the ANN model had a good predictive performance.
conclusionsThe ANN model presented good performance for predicting pathologic lymph node metastasis and added indicators not included in current staging criteria and might help improve the staging strategies.
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