ArticleFrontiers in oncology2020
A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World Data.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma.Annals of surgical oncology · 2023Trial
- An interpretable machine learning model for diagnostic classification of liver cancer using multivariable clinical data.American journal of cancer research · 2026Article
- Machine Learning-Based Model to Predict Survival in Resectable Intrahepatic Cholangiocarcinoma.Cancer management and research · 2026Article
- Advances in Endoscopic Diagnosis and Management of Cholangiocarcinoma.Journal of clinical medicine · 2025Review
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Article
- ASO Author Reflections: Use of Machine Learning to Predict Early Recurrence After Resection of Intrahepatic Cholangiocarcinoma.Annals of surgical oncology · 2023Article
- Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2023Article
- Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma.Scientific reports · 2023Article
- Artificial intelligence and cholangiocarcinoma: Updates and prospects.World journal of clinical oncology · 2022Review
- Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.Frontiers in oncology · 2022Article
- Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment.Journal of clinical medicine · 2021Article
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9 authors.
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Abstract
BACKGROUND AND
objectivesCurrently, the prognostic performance of the staging systems proposed by the 8th edition of the American Joint Committee on Cancer (AJCC 8th) and the Liver Cancer Study Group of Japan (LCSGJ) in resectable intrahepatic cholangiocarcinoma (ICC) remains controversial. The aim of this study was to use machine learning techniques to modify existing ICC staging strategies based on clinical data and to demonstrate the accuracy and discrimination capacity in prognostic prediction. PATIENTS AND
methodsThis is a retrospective study based on 1,390 patients who underwent surgical resection for ICC at Eastern Hepatobiliary Surgery Hospital from 2007 to 2015. External validation was performed for patients from 2015 to 2017. The ensemble of three machine learning algorithms was used to select the most important prognostic factors and stepwise Cox regression was employed to derive a modified scoring system. The discriminative ability and predictive accuracy were assessed using the Concordance Index (C-index) and Brier Score (BS). The results were externally validated through a cohort of 42 patients operated on from the same institution.
resultsSix independent prognosis factors were selected and incorporated in the modified scoring system, including carcinoembryonic antigen, carbohydrate antigen 19-9, alpha-fetoprotein, prealbumin, T and N of ICC staging category in 8th edition of AJCC. The proposed scoring system showed a more favorable discriminatory ability and model performance than the AJCC 8th and LCSGJ staging systems, with a higher C-index of 0.693 (95% CI, 0.663-0.723) in the internal validation cohort and 0.671 (95% CI, 0.602-0.740) in the external validation cohort, which was then confirmed with lower BS (0.103 in internal validation cohort and 0.169 in external validation cohort). Meanwhile, machine learning techniques for variable selection together with stepwise Cox regression for survival analysis shows a better prognostic accuracy than using stepwise Cox regression method only.
conclusionsThis study put forward a modified ICC scoring system based on prognosis factors selection incorporated with machine learning, for individualized prognosis evaluation in patients with ICC.
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