ArticleHepatology international2023
Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study.
Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Computed Tomography-Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.European journal of nuclear medicine and molecular imaging · 2026Article
- Patterns and risk factors of early recurrence after radical resection for intrahepatic cholangiocarcinoma.World journal of gastrointestinal surgery · 2026Article
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Present and future of liver transplantation for cholangiocellular carcinoma: moving toward personalized multiparametric transplantability patterns.Transplant international : official journal of the European Society for Organ Transplantation · 2026Review
- Article
- Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Predictive model for very early recurrence of patients with perihilar cholangiocarcinoma: a machine learning approach.Hepatobiliary surgery and nutrition · 2025Article
- Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algorithms.Frontiers in oncology · 2025Article
- Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis.Oncology letters · 2024Article
- Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis.World journal of gastroenterology · 2024Article
- Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.Frontiers in oncology · 2024Review
- Editorial: Role of imaging in biliary tract cancer: diagnosis, staging, response prediction and image-guided therapeutics.Frontiers in oncology · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma.Insights into imaging · 2023Article
- 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
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16 authors.
Funding
Abstract
objectivesIn this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND
methodsA total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model.
resultsThe combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts.
conclusionsThis AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.
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