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| Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram |
| Jing-Wei Zhao a , b ,# , Xin Shu c ,# , Xiao-Xia Chen b ,# , Jia-Xiong Liu b , Mu-Qing Liu b , Ju Ye b , Hui-Jie Jiang d , Gui-Sheng Wang b ,∗ |
a Department of Radiology, the Third Medical Center, Chinese PLA General Hospital, The Training Site for Postgraduate of Jinzhou Medical University, Beijing 100039, China
b Department of Radiology, the Third Medical Center, Chinese PLA General Hospital, Beijing 100039, China
c Medical School of Chinese PLA, Beijing 100853, China d Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
∗Corresponding author.
E-mail address: wanggs1996@tom.com (G.-S. Wang) .
# Contributed equally. |
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Abstract Background: Early recurrence results in poor prognosis of patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). This study aimed to explore the value of computed tomography (CT)-based radiomics nomogram in predicting early recurrence of patients with HCC after LT.
Methods: A cohort of 151 patients with HCC who underwent LT between December 2013 and July 2019 were retrospectively enrolled. A total of 1218 features were extracted from enhanced CT images. The least absolute shrinkage and selection operator algorithm (LASSO) logistic regression was used for dimension reduction and radiomics signature building. The clinical model was constructed after the analysis of clinical factors, and the nomogram was constructed by introducing the radiomics signature into the clinical model. The predictive performance and clinical usefulness of the three models were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively. Calibration curves were plotted to assess the calibration of the nomogram.
Results: There were significant differences in radiomics signature among early recurrence patients and non-early recurrence patients in the training cohort (P < 0.001) and validation cohort (P < 0.001). The nomogram showed the best predictive performance, with the largest area under the ROC curve in the training (0.882) and validation (0.917) cohorts. Hosmer-Lemeshow testing confirmed that the nomogram showed good calibration in the training (P = 0.138) and validation (P = 0.396) cohorts. DCA showed if the threshold probability is within 0.06-1, the nomogram had better clinical usefulness than the clinical model.
Conclusions: Our CT-based radiomics nomogram can preoperatively predict the risk of early recurrence in patients with HCC after LT.
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2022, Vol. 21 
