A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma

Abstract

Objectives. The postoperative early recurrence (ER) rate of hepatocellular carcinoma (HCC) is 50%, and no highly reliable predictive tool has been developed yet. The aim of this study was to develop and validate a predictive model with radiomics analysis based on multiparametric magnetic resonance (MR) images to predict early recurrence of HCC. Methods. In total, 302 patients (training dataset: n = 211; validation dataset: n = 91) with pathologically confirmed HCC who underwent preoperative MR imaging were enrolled in this study. Three-dimensional regions of interest of the entire lesion were accessed by manually drawing along the tumor margins on the multiple sequences of MR images. Least absolute shrinkage and selection operator Cox regression were then applied to select ER-related radiomics features and construct radiomics signatures. Univariate analysis and multivariate Cox regression analysis were used to identify the significant clinico-radiological factors and establish a clinico-radiological model. A predictive model of ER incorporating the fusion radiomics signature and clinico-radiological risk factors was constructed. The diagnostic performance and clinical utility of this model were measured by receiver-operating characteristic (ROC), calibration curve, and decision curve analyses. Results. The fusion radiomics signature consisting of 6 radiomics features achieved good prediction performance (training dataset: AUC = 0.85, validation dataset: AUC = 0.79). The predictive model of ER integrating clinico-radiological risk factors and the fusion radiomics signature improved the prediction efficacy with AUCs of 0.91 and 0.87 in the training and validation datasets, respectively. Furthermore, the nomogram and ER risk stratification system based on the predictive model demonstrated encouraging predictions of the individualized risk of ER and gave three risk groups with low, intermediate, or high risk of ER. Conclusions. The proposed predictive model incorporating clinico-radiological factors and the fusion radiomics signature derived from multiparametric MR images may be an effective tool for the individualized prediction of postoperative ER in patients with HCC.

Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis

“…The process of literature search and study selection is presented in Figure 1 . After screening according to the pre-defined inclusion/exclusion criteria, a total of 15 publications were identified for this meta-analysis, consisting of six radiomics studies based on CT and nine radiomics studies based on MRI ( 7 , 9 , 10 , 13 , 14 , 19 – 28 ). Most of the studies included in the analysis were retrospective, and all of them were published between 2017 and 2022.…”

“…Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value ( 7 , 9 , 10 , 13 , 14 ). However, the reported results seem quite variable due to the fact that these above studies differed in the diagnostic performance of the preoperative evaluation of ER because the differences in imaging modalities, research methods, sample size, etc.…”

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

“…Early fusion is also known as data-level fusion and is the fusion of multiple modalities’ information before a feature input classifier is implemented [ 171 ]. Li et al [ 172 ] combined the MR image features of different sequences to construct radiomics signatures and then combined these with clinic-radiological risk factors to develop a multi-factor model based on a training set. They found that the multi-factor model had the highest performance.…”

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

“…34 Therefore, nomograms based on CT or MRI features were also used in the study of prognosis of liver cancer, such as homogeneous signal, rim enhancement, peritumoral enhancement, and fusion radiomic signature. [35][36][37] https://doi.org/10.2147/JHC.S417123…”