Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma

• Published in: BMC gastroenterology Vol. 22; no. 1; pp. 544 - 11
• Main Authors: Yue, Qi, Zhou, Zheyu, Zhang, Xudong, Xu, Xiaoliang, Liu, Yang, Wang, Kun, Liu, Qiaoyu, Wang, Jincheng, Zhao, Yu, Yin, Yin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.12.2022; BioMed Central; BMC
• Subjects: Analysis; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - surgery; Care and treatment; CT imaging; Diagnosis; Hepatectomy; Hepatocellular carcinoma; Hepatoma; Humans; Liver; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - surgery; Metastasis; Microvascular invasion; Neoplasm Invasiveness; Nomogram; Patient outcomes; Preoperative prediction; Prevention; Retrospective Studies; Risk factors; Tomography, X-Ray Computed - methods; Transplantation; Taiwan; Hepatocellular carcinoma; Preoperative prediction; Microvascular invasion; Nomogram
• ISSN: 1471-230X; 1471-230X
• DOI: 10.1186/s12876-022-02586-2

Microvascular invasion (MVI) is important in early recurrence and leads to poor overall survival (OS) in hepatocellular carcinoma (HCC). A number of studies have reported independent risk factors for MVI. In this retrospective study, we designed to develop a preoperative model for predicting the presence of MVI in HCC patients to help surgeons in their surgical decision-making and improve patient management. We developed a predictive model based on a nomogram in a training cohort of 225 HCC patients. We analyzed patients' clinical information, laboratory examinations, and imaging features from contrast-enhanced CT. Mann-Whitney U test and multiple logistic regression analysis were used to confirm independent risk factors and develop the predictive model. Internal and external validation was performed on 75 and 77 HCC patients, respectively. Moreover, the diagnostic performance of our model was evaluated using receiver operating characteristic (ROC) curves. In the training cohort, maximum tumor diameter (> 50 mm), tumor margin, direct bilirubin (> 2.7 µmol/L), and AFP (> 360.7 ng/mL) were confirmed as independent risk factors for MVI. In the internal and external validation cohort, the developed nomogram model demonstrated good diagnostic ability for MVI with an area under the curve (AUC) of 0.723 and 0.829, respectively. Based on routine clinical examinations, which may be helpful for clinical decision-making, we have developed a nomogram model that can successfully assess the risk of MVI in HCC patients preoperatively. When predicting HCC patients with a high risk of MVI, the surgeons may perform an anatomical or wide-margin hepatectomy on the patient.