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Updated: Jun 6, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
CEUS features-based nomogram for predicting early recurrence (ER) of microvascular invasion-negative hepatocellular
Qing Shu1, Yang Liu2, Chuan Pang2
1Department of Interventional Ultrasound, The First Medical Centre, Chinese PLA General Hospital, Beijing, China.
Purpose:
This study aimed to investigate contrast-enhanced ultrasound (CEUS) and clinical features associated with early recurrence (ER) following surgical resection in patients with microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC), and to establish a nomogram-based predictive model.
Methods:
A total of 146 pathologically confirmed MVI-negative HCC patients were retrospectively enrolled between August 2021 and June 2024. Univariable and multivariable Cox regression analyses were performed based on CEUS and clinicopathologic variables to identify predictors and develop a nomogram for 2-year recurrence probability. Model performance and discriminative ability were evaluated using the concordance index (C-index), receiver operating characteristic curve, and decision curve analysis (DCA).
Results:
Multivariable Cox analysis identified smooth morphology, intra-tumoral artery, glypican-3 expression, and albumin-bilirubin (ALBI) grade as independent predictors of ER (all P < 0.05). A nomogram incorporating CEUS and clinicopathologic variables was constructed and demonstrated a favorable C-index of 0.768 (95% confidence interval [CI], 0.711-0.817). The model showed strong discriminative ability, with an area under the curve of 0.870 (95% CI, 0.805-0.952), and DCA indicated good clinical utility. The derived risk score effectively stratified patients into high-risk and low-risk groups. Patients in the low-risk group exhibited significantly better prognosis compared with those in the high-risk group ( P < 0.001).
Conclusions:
This CEUS- and clinicopathology-based prognostic model accurately identifies high-risk MVI-negative HCC patients and may provide valuable information for optimizing surveillance and intervention strategies.