ArticleScientific reports2025
Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.Clinical and experimental medicine · 2025Pooled it
- Radiomics for Detection and Differentiation of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 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
- Preoperative MVI prediction in intrahepatic cholangiocarcinoma via deep learning analysis of intratumoral and peritumoral features on multi-sequence MRI.BMC medical imaging · 2025Article
- Radiomics models with baseline MRI and clinical data to predict target therapy response and high-risk mortality in metastatic GIST.Scientific reports · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Review
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Authors and funding
9 authors.
Funding
Abstract
Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomics analysis, performed using computed tomography (CT) and magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT and MRI examinations was enrolled. Univariate and multivariate analysis was used to identify the significant factors of radiological findings for diagnosing iCCA. DL radiomics analysis was applied to CT and MRI images, respectively. We constructed and evaluated six distinct models: CT DL radiomics (DLRS
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.