A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation

Liting Wen; Shuping Weng; Chuan Yan; Rongping Ye; Yuemin Zhu; Lili Zhou; Lanmei Gao; Yueming Li

doi:10.3389/fonc.2021.657039

A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation

Front Oncol. 2021 Apr 29:11:657039. doi: 10.3389/fonc.2021.657039. eCollection 2021.

Authors

Liting Wen¹, Shuping Weng², Chuan Yan¹, Rongping Ye¹, Yuemin Zhu¹, Lili Zhou¹, Lanmei Gao¹, Yueming Li^{1

3}

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
² Department of Radiology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China.
³ Key Laboratory of Radiation Biology, Fujian Medical University, Fujian Province University, Fuzhou, China.

Abstract

Background: Patients with small hepatocellular carcinoma (HCC) (3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (2 years) of small HCC.

Methods: The study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years. Radiomic features were extracted from the entire tumor by using the MaZda software. The least absolute shrinkage and selection operator (LASS0) method was applied for feature selection, and radiomics signature construction. A rad-score was then calculated. Multivariable logistic regression analysis was used to establish a prediction model including independent clinical risk factors, radiologic features and rad-score, which was ultimately presented as a radiomics nomogram. The predictive ability of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve and internal validation was performed via bootstrap resampling and 5-fold cross-validation method.

Results: A total of 53 (53/111, 47.7%) patients had confirmed ER according to the final clinical outcomes. In univariate logistic regression analysis, cirrhosis and hepatitis B infection (P=0.015 and 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), the preoperative platelet count (P=0.003), and rad-score (P<0.001) were correlated with ER. However, after multivariate logistic regression analysis, only the preoperative platelet count and rad-score were included as predictors in the final model. The area under ROC curve (AUC) of the radiomics nomogram to predict ER of small HCC was 0.981 (95% CI: 0.957, 1.00), while the AUC verified by bootstrap is 0.980 (95% CI: 0.962, 1.00), indicating the goodness-of-fit of the final model.

Conclusions: The radiomics nomogram containing the clinical risk factors and rad-score can be used as a quantitative tool to preoperatively predict individual probability of ER of small HCC.

Keywords: hepatocellular carcinoma; magnetic resonance imaging; nomogram; radiomics; recurrence.