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Preoperative diagnosis and prediction of microvascular invasion in hepatocellularcarcinoma by ultrasound elastography

BACKGROUND: To assess the values of two elastography techniques combined with serological examination and clinical features in preoperative diagnosis of microvascular invasion in HCC patients. METHODS: A total of 74 patients with single Hepatocellular carcinoma (HCC) were included in this study. She...

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Detalles Bibliográficos
Autores principales: Xu, Chengchuan, Jiang, Dong, Tan, Bibo, Shen, Cuiqin, Guo, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107229/
https://www.ncbi.nlm.nih.gov/pubmed/35562688
http://dx.doi.org/10.1186/s12880-022-00819-0
Descripción
Sumario:BACKGROUND: To assess the values of two elastography techniques combined with serological examination and clinical features in preoperative diagnosis of microvascular invasion in HCC patients. METHODS: A total of 74 patients with single Hepatocellular carcinoma (HCC) were included in this study. Shear wave measurement and real-time tissue elastography were used to evaluate the hardness of tumor-adjacent tissues and tumor tissues, as well as the strain rate ratio per lesion before surgery. According to the pathological results, the ultrasound parameters and clinical laboratory indicators related to microvascular invasion were analyzed, and the effectiveness of each parameter in predicting the occurrence of microvascular invasion was compared. RESULTS: 33/74 patients exhibited microvascular invasion. Univariate analysis showed that the hardness of tumor-adjacent tissues (P = 0.003), elastic strain rate ratio (P = 0.032), maximum tumor diameter (P < 0.001), and alpha-fetoprotein (AFP) level (P = 0.007) was significantly different in the patients with and without microvascular invasion. The binary logistic regression analysis showed that the maximum tumor diameter (P = 0.001) was an independent risk factor for predicting microvascular invasion, while the hardness of tumor-adjacent tissues (P = 0.028) was a protective factor. The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) of the hardness of tumor-adjacent tissues, the maximum diameter of the tumor, and the predictive model Logit(P) in predicting the occurrence of MVI was 0.718, 0.775 and 0.806, respectively. CONCLUSION: The hardness of tumor-adjacent tissues, maximum tumor diameter, and the preoperative prediction model predict the occurrence of MVI in HCC patients.