ArticleEuropean radiology2026
Intratumoral heterogeneity of CT enhancement for component prediction and prognostic significance in combined hepatocellular carcinoma‑cholangiocarcinoma.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma.Insights into imaging · 2026Article
- [Study on preoperative prediction of microvascular invasion based on contrast-enhanced ultrasound habitat imaging combined with peritumoral radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- Diffusion weighted imaging-based tumor growth rate for predicting long-term survival in nasopharyngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Can incorporating preoperative arterial enhancement ratio map into habitat analysis predict TACE refractoriness in unresectable hepatocellular carcinoma?BMC medical imaging · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesTo construct a combined nomogram using CT enhancement ratio-based habitat imaging and radiological features in predicting the main component of combined hepatocellular carcinoma‑cholangiocarcinoma (cHCC-CCA), and to assess its ability for stratifying the prognosis. MATERIALS AND
methodsPatients with pathologically diagnosed cHCC-CCA who underwent contrast-enhanced CT examinations were retrospectively included and randomized into the training and validation cohorts. Tumors were grouped into high hepatocellular carcinoma (HCC) component (high-HCC%) and low-HCC component (low-HCC%) according to pathology. Voxels of tumor from early enhancement ratio and late enhancement ratio maps were clustered into different habitats through the k-means algorithm. The volume fractions of different habitats were quantified. Logistic regression analyses were utilized to identify independent predictors for high-HCC%, construct prediction models, and visualize them as a nomogram. The predictive performance was assessed by receiver operating characteristic analysis. Survival analysis was conducted using the Kaplan-Meier method.
results165 patients were finally included, and 78 (47.27%) patients were grouped as high-HCC%. Four tumor habitats were determined. The fraction of habitat 1 (f
conclusionEnhancement-based CT habitat imaging exhibited potential for predicting the main component cHCC-CCA, and provided a tool for prognosis stratification. KEY POINTS: Question The component of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) significantly affected the prognosis, but there is no effective method for predicting the main component of cHCC-CCA. Findings The combined nomogram integrated habitat parameters and radiological features can predict the main component of cHCC-CCA and help stratify the prognosis after hepatectomy. Clinical relevance The habitat-based combined nomogram offers an effective tool for personalized and appropriate treatment in cHCC-CCA patients.
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