ArticleHPB : the official journal of the International Hepato Pancreato Biliary Association2022
Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma.
Article in HPB : the official journal of the International Hepato Pancreato Biliary Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- 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
- Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.Cancer cell international · 2026Review
- Predicting perineural invasion of intrahepatic cholangiocarcinoma based on CT: a multicenter study.Abdominal radiology (New York) · 2026Article
- Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts.NPJ digital medicine · 2025Article
- Very Early Recurrence Following Liver Resection for Intrahepatic Cholangiocarcinoma: Is It Predictable by Clinical Preoperative Factors?ANZ journal of surgery · 2025Article
- Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications.Science advances · 2025Article
- Personalized Medical Approach in Gastrointestinal Surgical Oncology: Current Trends and Future Perspectives.Journal of personalized medicine · 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
- Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study.Frontiers in oncology · 2025Article
- Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis.Oncology letters · 2024Article
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- Carcinoembryonic antigen in the diagnosis, treatment, and follow-up of focal liver lesions.World journal of gastrointestinal surgery · 2024Article
- 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
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Prognostic value of integrated morphofunctional imaging methods in inoperable intrahepatic cholangiocarcinoma.Frontiers in medicine · 2023Article
- Prediction of early-stage melanoma recurrence using clinical and histopathologic features.NPJ precision oncology · 2022Article
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Authors and funding
15 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundMost patients recur after resection of intrahepatic cholangiocarcinoma (IHC). We studied whether machine-learning incorporating radiomics and tumor size could predict intrahepatic recurrence within 1-year.
methodsThis was a retrospective analysis of patients with IHC resected between 2000 and 2017 who had evaluable computed tomography imaging. Texture features (TFs) were extracted from the liver, tumor, and future liver remnant (FLR). Random forest classification using training (70.3%) and validation cohorts (29.7%) was used to design a predictive model.
results138 patients were included for analysis. Patients with early recurrence had a larger tumor size (7.25 cm [IQR 5.2-8.9] vs. 5.3 cm [IQR 4.0-7.2], P = 0.011) and a higher rate of lymph node metastasis (28.6% vs. 11.6%, P = 0.041), but were not more likely to have multifocal disease (21.4% vs. 17.4%, P = 0.643). Three TFs from the tumor, FD1, FD30, and IH4 and one from the FLR, ACM15, were identified by feature selection. Incorporation of TFs and tumor size achieved the highest AUC of 0.84 (95% CI 0.73-0.95) in predicting recurrence in the validation cohort.
conclusionThis study demonstrates that radiomics and machine-learning can reliably predict patients at risk for early intrahepatic recurrence with good discrimination accuracy.
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