ArticleLa Radiologia medica2026
Deep learning model integrating contrast-enhanced ultrasound spatiotemporal imaging with clinical data for the differential diagnosis between hepatocellular carcinoma and intrahepatic cholangiocarcinoma.
Article in La Radiologia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- [Ultrasound technology's new application in the diagnosis and treatment of liver cancer].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
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10 authors.
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Abstract
purposeThis study aimed to develop a deep learning model, capable of extracting both spatial and temporal features from contrast-enhanced ultrasound (CEUS) data and integrating with patient clinical parameters, for the differential diagnosis between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). MATERIALS AND
methodsWe retrospectively analyzed the CEUS data (ultrasound contrast agent: SonoVue®-sulfur hexafluoride microbubbles) from 165 ICC patients and 140 date-matched HCC patients. A deep learning model, namely CEUS-CD-Net, was developed to extract spatial-temporal features from dynamic CEUS data and integrate them with patient clinical parameters for the differential diagnosis between HCC and ICC. The performance of CEUS-CD-Net was evaluated using the area under the receiver operating characteristic curve (AUC), with comparisons against other methods including the single-source data-based models (CEUS-Net and CD-Net, based merely on dynamic CEUS or patient clinical data), CEUS static image-based model (sCEUS-Net), time-intensity curve-based model (TIC-Model), and the assessment by radiologists.
resultsCEUS-CD-Net achieved an AUC of 0.884 (95% CI, 0.794-0.938) on the test cohort, significantly outperforming the single-source data-based models of CEUS-Net (0.827 [0.730-0.896]) and CD-Net (0.812 [0.718-0.887]), as well as sCEUS-Net (0.772 [0.669-0.851]) and TIC-Model (0.731 [0.633-0.823]). In the subset of determinate cases, CEUS-CD-Net achieved an AUC of 0.893 [0.806-0.950], which was better than the one obtained by radiologists' assessment (0.790 [0.683-0.868]). Model visualization results revealed that CEUS-CD-Net surpassed radiologists in discerning subtle patterns reflected by CEUS.
conclusionThe integration of spatial and temporal features of dynamic CEUS data, coupled with clinical parameters of patients in CEUS-CD-Net, significantly improved the differential diagnosis between HCC and ICC.
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