ArticleAnnals of translational medicine2020
Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma.World journal of gastroenterology · 2021Trial
- Three-class radiomic differentiation of hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma on multiphasic contrast-enhanced CT.Abdominal radiology (New York) · 2026Article
- An Updated Review of Combined Hepatocellular Cholangiocarcinoma: A Rare and Poorly Understood Neoplasm.Diagnostics (Basel, Switzerland) · 2026Review
- Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons.Diagnostics (Basel, Switzerland) · 2025Review
- Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Abdominal radiology (New York) · 2024Article
- Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics.Cancers · 2023Article
- Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images.Scientific reports · 2023Article
- The significance of the predominant component in combined hepatocellular-cholangiocarcinoma: MRI manifestation and prognostic value.La Radiologia medica · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- [Advances in imaging diagnosis of mixed-type liver cancer].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2023Review
- Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative.Insights into imaging · 2023Article
- Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend.Frontiers in oncology · 2023Review
- Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.AJR. American journal of roentgenology · 2022Review
- Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.Annals of translational medicine · 2022Article
- Promising role of liver transplantation in patients with combined hepatocellular-cholangiocarcinoma: a propensity score matching analysis.Annals of translational medicine · 2022Article
- Radiomics of Biliary Tumors: A Systematic Review of Current Evidence.Diagnostics (Basel, Switzerland) · 2022Review
- Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.BMC medical imaging · 2022Article
- Artificial intelligence and cholangiocarcinoma: Updates and prospects.World journal of clinical oncology · 2022Review
- Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundCombined hepatocellular and cholangiocarcinoma (CHC) and intrahepatic cholangiocarcinoma (ICC) are hard to identify in clinical practice preoperatively. This study looked to develop and confirm a radiomics-based model for preoperative differentiation CHC from ICC.
methodsThe model was developed in 86 patients with ICC and 46 CHC, confirmed in 37 ICC and 20 CHC, and data were collected from January 2014 to December 2018. The radiomics scores (Radscores) were built from radiomics features of contrast-enhanced computed tomography in 12 regions of interest (ROI). The Radscore and clinical-radiologic factors were integrated into the combined model using multivariable logistic regression. The best-combined model constructed the radiomics-based nomogram, and the performance was assessed concerning its calibration, discrimination, and clinical usefulness.
resultsThe radiomics features extracted from tumor ROI in the arterial phase (AP) with preprocessing were selected to build Radscore and yielded an area under the curve (AUC) of 0.800 and 0.789 in training and validation cohorts, respectively. The radiomics-based model contained Radscore and 4 clinical-radiologic factors showed the best performance (training cohort, AUC =0.942; validation cohort, AUC =0.942) and good calibration (training cohort, AUC =0.935; validation cohort, AUC =0.931).
conclusionsThe proposed radiomics-based model may be used conveniently to the preoperatively differentiate CHC from ICC.
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