1. Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy
- Author
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Xiaohui Duan, Huijun Hu, Chuanmiao Xie, Qiuxia Yang, Lingjie Yang, Xinping Shen, Rong Zhang, Xiaohua Ban, and Cuiping Zhou
- Subjects
medicine.medical_specialty ,Primary (chemistry) ,business.industry ,Medicine ,Radiology ,Partial hepatectomy ,business - Abstract
Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (POR=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI:0.827-0.947).Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.
- Published
- 2021
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