ArticleEuropean radiology2025
Noninvasive identification of proliferative hepatocellular carcinoma on multiphase dynamic CT: quantitative and LI-RADS lexicon-based evaluation.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Preoperative Gd-EOB-DTPA-enhanced MRI may help predict macrotrabecular-massive subtype and postoperative recurrence in small hepatocellular carcinoma (≤ 3 cm).European radiology · 2026Article
- A clinical and contrast-enhanced CT-based model for preoperative prediction of CK19- and GPC3-positive dual-phenotype hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Establishment of prediction model for breast lesion using automated breast ultrasound system.Quantitative imaging in medicine and surgery · 2026Article
- Augmenting conventional criteria: a CT-based deep learning radiomics nomogram for early recurrence risk stratification in hepatocellular carcinoma after liver transplantation.Insights into imaging · 2025Article
- CT-based deep learning radiomics model for predicting proliferative hepatocellular carcinoma: application in transarterial chemoembolization and radiofrequency ablation.BMC medical imaging · 2025Article
- Intra- and Peritumoral Radiomic Signatures on CECT: Prediction of Aggressive Hepatocellular Carcinoma Subtypes and 2-Year Recurrence.Journal of hepatocellular carcinoma · 2025Article
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Abstract
objectiveTo identify proliferative hepatocellular carcinoma (HCC) preoperatively using quantitative measurements combined with the updated standard 2021 LI-RADS universal lexicon-based qualitative features on multiphase dynamic CT (MDCT).
methodsWe retrospectively analyzed 273 patients (102 proliferative HCCs) who underwent preoperative MDCT with surgically confirmed HCC in two medical centers. Imaging features were evaluated according to the updated 2021 LI-RADS universal lexicon, and quantitative measurements were analyzed. All MDCT findings and clinical factors were compared. Four predictive models (clinical, CT quantitative-clinical, CT qualitative-clinical, and combinational models) were developed and validated in an external cohort for identifying proliferative HCC. ROC analysis was used to assess model performances. All models were tested in a subgroup of patients with a single lesion ≤ 5 cm (n = 124).
resultsBoth the CT quantitative-clinical and CT qualitative-clinical models effectively identified proliferative HCC in the training and external validation cohorts (all AUCs > 0.79). The combinational model, integrating one clinical (AFP ≥ 200 ng/mL), three qualitative (rim arterial phase hyperenhancement (APHE), non-smooth tumor margin, and incomplete or absent capsule), and one quantitative feature (standardized tumor-to-aorta density ratio in portal venous phase ≤ (- 0.13), showed significant improvement in the training cohort (AUC 0.871) and comparable performance in the validation cohort (AUC 0.870). Additionally, AFP ≥ 200 ng/mL and Rim APHE were significantly associated with HCC recurrence (p < 0.05).
conclusionsThe combinational model, integrating clinical, CT quantitative, and qualitative features, shows potential for the noninvasively preoperative prediction of proliferative HCC. Further validation is needed to establish its broader clinical utility. KEY POINTS: Question Preoperative identification of proliferative HCC could influence patient treatment and prognosis, yet there is no CT-based universally applicable model to identify this subtype. Findings The updated standard 2021 LI-RADS universal lexicon-based features, in combination with quantitative MDCT measurements, could aid in the noninvasive detection of proliferative HCC. Clinical relevance The updated standard 2021 LI-RADS universal lexicon-based CT qualitative features and quantitative measurements may aid in identifying proliferative HCC and tumor recurrence, offering potential guidance for personalized treatment. Further studies are required to assess their generalizability to different clinical scenarios.
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