Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI

Abstract

Background In unresectable hepatocellular carcinoma (HCC), methods to predict patients at increased risk of progression are required. Purpose To investigate the feasibility of radiomics model in predicting early progression of unresectable HCC after transcatheter arterial chemoembolization (TACE) therapy using preoperative multiparametric magnetic resonance imaging (MP‐MRI). Study Type Retrospective. Population A total of 84 patients with BCLC B stage HCC from one medical center. According to the modified response evaluation criteria in solid tumors, patients who progressed at 6 months after TACE therapy were assigned as the progressive disease (PD) group (n = 32). Patients whose MRI was performed on four devices were divided into a training cohort (n = 67). Patients whose MRI was performed on other than the previous four devices were used as the testing set (n = 17). Field Strength/Sequence 3.0T, 1.5T axial T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI, b = 0, 500 s/mm2), and apparent diffusion coefficient (ADC) Assessment PD was confirmed via imaging studies with MRI. Risk factors, including age, alpha fetoprotein (AFP), size, and radiomic‐related features of PD were assessed. In addition, the discrimination ability of each radiomics signature was tested on an independent testing set. Statistical Tests The area under the receiver‐operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and testing sets. The results indicated that the MP‐MRI model achieved the greatest benefit. Results In the testing set, the model based on DWI features presented an AUC of (b = 0, 0.786; b = 500, 0.729), followed by T2WI features (0.729) and ADC (0.714). The AUC of the MP‐MRI signature was increased to 0.800 compared to any single MRI signature. The multivariate logistic analysis identified the radiomics signature as independent parameters of PD, while clinical information such as age, AFP, size, etc., had no significance in the PD group. Data Conclusion Preoperative MP‐MRI has the potential to predict the outcome of TACE therapy for unresectable HCC. In addition, these image features may be complementary to the current staging systems of HCC patients. Level of Evidence 2. Technical Efficacy Stage 3. J. Magn. Reson. Imaging 2020;52:1083–1090.

Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features

“…The value of radiomics in HCC treatment has been steadily increasing. [15][16][17][18]27 Several recent studies that combined radiomics with clinical features reported success in predicting the ORR for TACE in HCC patients. A model that combined T1WI and T2WI MRI-based radiomics with clinical features, including sex, tumor margin, peritumoral enhancement, BCLC stage, and AFP level, was generated to predict the recurrence-free survival (RFS) of HCC patients after TACE treatment, and the C-index was 0.681-0.861.…”

“…13,14 In 2020, it has been reported that ultrasound or magnetic resonance imaging (MRI) radiomics might be an effective predictor for short-term HCC prognosis after TACE. [15][16][17] Deep learning technology for CT images can also improve prediction of therapeutic response to TACE. 18 This study was the first to use a single image of non-contrast computed tomography (NC-CT) radiomics combined with features selected from clinical characteristics, laboratory data, and general CT imaging to generate a new compound model for the prediction of TACE response.…”

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response

“…Multiple studies investigated the role of radiomics at baseline imaging as predictor of response to locoregional treatment, mainly before TACE, and showed that several radiomics features (extracted from pre-treatment CT/MRI) correlated with the future response to locoregional treatment. In the recent study by Sun et al a model based on radiomics features extracted from pretreatment DWI, ADC, and T2-weighted MRI had AUC of 0.8 (p < 0.001) for prediction of progressive disease after TACE [60]. Two other studies showed correlation between pre-treatment contrast-enhanced-CT-derived texture features and mid-term (at 6 month) response to drug-eluting bead TACE [57,61].…”

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

“…Another study with 84 HCC patients from China revealed that the model based on diffusion-weighted image features showed an AUC (b = 0, 0.786; b = 500, 0.729), followed by T2-weighted image features (0.729) and apparent diffusion coefficient images (0.714). Additionally, the radiomics signature was an independent parameter of progressive disease (PD), while clinical information had no significance in the PD group [54].…”