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

Abstract

BackgroundPatients 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.MethodsThe 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.ResultsA 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.ConclusionsThe 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.

Progress of MRI Radiomics in Hepatocellular Carcinoma

“…Improving the ability to preoperatively identify these high-risk patients will guide surgical management, postoperative monitoring, and treatment intervention ( 127 , 128 ). The radiomic model based on preoperative MRI can be used as a new tool to predict early relapse ( 18 , 19 , 129 – 134 ), relapse-free survival ( 135 ) and overall survival (OS) ( 136 , 137 ) in patients with HCC after surgery. Hui et al ( 130 ) used preoperative MRI to extract 290 texture parameters to predict the relapse of HCC patients within 730 days after surgical resection.…”

“…Improving the ability to preoperatively identify these high-risk patients will guide surgical management, postoperative monitoring, and treatment intervention (127,128). The radiomic model based on preoperative MRI can be used as a new tool to predict early relapse (18,19,(129)(130)(131)(132)(133)(134), relapsefree survival (135) and overall survival (OS) (136,137)…”

“…Radiomics, a new technology, can transform the potential histopathological and physiological information in images into high-dimensional quantitative image features that can be mined (6,7).The study of radiomics will contribute to the early diagnosis and treatment of HCC and ultimately improve survival (8,9). In recent years, many studies have confirmed the application values of magnetic resonance imaging (MRI) radiomics in the diagnosis and differentiation (10,11), histological grading (12,13), microvascular invasion (MVI) assessment (14,15), radiogenomics (16,17),prediction of relapse and prognosis after surgical resection (18)(19)(20), response to transarterial chemoembolization(TACE) (21,22) and systemic treatment efficacy of HCC (23).…”

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma

“…Radiomics is defined as the automated quantification of the radiological phenotype using data-characterization algorithms ( 13 , 14 ). It is a vital imaging technology useful for differential diagnoses, assessment of therapeutic responses, prognosis prediction, etc., thereby providing valuable information for personalized medicine ( 13 , 15 , 16 ). Radiomic strategies have shown great predictive potential by incorporating radiological features related to various diseases and clinical and/or pathological factors into a single fusion model ( 17 , 18 ).…”

ABDOMEN/BECKEN – No-Touch-Radiofrequenzablation überzeugt bei kleinen hepatozellulären Karzinomen

“…Dennoch wird die RFA immer wieder kontrovers diskutiert, insbesondere mit dem Hinweis auf höhere Raten an Rezidiven und geringerem Langzeitüberleben im Vergleich zur Resektion [6][7][8][9][10]. Lee et al nehmen in ihrer Studie diese "Problematik" auf.…”