ArticleTechnology in cancer research & treatment
Risk Factors for Early Recurrence After Radical Resection of Hepatocellular Carcinoma Based on Preoperative Contrast-Enhanced Ultrasound and Clinical Features.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024.World journal of gastrointestinal surgery · 2025Article
- Deep Learning with Contrast-Enhanced Ultrasound for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma After Hepatectomy.Journal of multidisciplinary healthcare · 2025Article
- Development and validation of a model for predicting who can benefit from multiple TACE in HCC patients.Clinical and experimental medicine · 2024Article
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8 authors.
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Abstract
objectivesTo investigate risk factors for the early recurrence (ER) of hepatocellular carcinoma (HCC) after radical resection based on preoperative contrast-enhanced ultrasound (CEUS) and clinical features to provide guidance for clinical treatment.
methodsThe retrospective analysis selected 130 HCC patients who underwent radical tumor resection from October 2019 to November 2021. All patients underwent preoperative routine ultrasound examination and CEUS, and the pathology was confirmed as HCC after surgery. The patients were divided into two groups based on whether there is an ER, namely the ER group and the non-ER group. The general clinical, routine and CEUS data of patients were collected, and the factors were selected by using the least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to screen the independent influencing factors of ER. Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal validation was evaluated.
resultsMultivariate logistic regression identified several independent factors influencing ER after radical HCC resection. Significant factors included early wash-out phase (95%CI = 0.003-0.206, P = 0.001), liver cirrhosis (95%CI = 2.835-221.224, P = 0.004), incomplete envelope (95%CI = 5.247-1056.130,P = 0.001), multiple lesions (95%CI = 1.110-135.424,P = 0.041), Albumin <40 g/L (95%CI = 2.496-127.223,P = 0.004), and Golgi Protein 73 (GP73) ≥ 85 ng/mL (95%CI = 1.594-30.002, P = 0.010), with all P-values <0.05. The nomogram prediction model constructed based on the results of multivariate logistic regression, demonstrated a ROC curve AUC of 0.879, a sensitivity of 93.5%, a specificity of 66.7%, and a C-index of 0.602, indicating superior diagnostic efficiency compared to independent influencing factors. The ER nomogram prediction model confirmed good discrimination and calibration in internal validation.
conclusionThe CEUS-Clinical combined model effectively monitors the risk of ER in high-risk populations following radical resection of HCC, timely interventions to improve patient prognosis.
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