ArticleFrontiers in oncology2023
Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical Value of Vascular Cancer Thrombus and Myometrial Invasion Combined with Tumor Markers in Predicting Sentinel Lymph Node Metastasis of Endometrial Cancer.Cancer management and research · 2025Article
- Endoscopic diagnosis and management of gallbladder carcinoma in minimally invasive era: New needs, new models.World journal of gastrointestinal oncology · 2024Article
- Research progress on prognostic factors of gallbladder carcinoma.Journal of cancer research and clinical oncology · 2024Review
- Prophylactic Hyperthermic Intraperitoneal Chemotherapy for Patients at High Risk of Developing Gallbladder Cancer Peritoneal Metastases: Case Report and Rationale for a Prospective Clinical Trial.Journal of clinical medicine · 2024Review
- New trends in diagnosis and management of gallbladder carcinoma.World journal of gastrointestinal oncology · 2024Review
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Authors and funding
7 authors.
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Abstract
Objectives: It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC. Methods: A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset. Results: The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ Conclusions: The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.
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