ArticleJournal of clinical medicine2021
Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.
Article in Journal of clinical medicine, 2021. 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.
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Safety and feasibility of laparoscopic radical resection for bismuth types III and IV hilar cholangiocarcinoma: a single-center experience from China.Frontiers in oncology · 2023Article
- Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.Medicina (Kaunas, Lithuania) · 2022Article
- Artificial intelligence and cholangiocarcinoma: Updates and prospects.World journal of clinical oncology · 2022Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77,
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