Radiomic advances in the transarterial chemoembolization related therapy for hepatocellular carcinoma

Abstract

Radiomics is a hot topic in the research on customized oncology treatment, efficacy evaluation, and tumor prognosis prediction. To achieve the goal of mining the heterogeneity information within the tumor tissue, the image features concealed within the tumoral images are turned into quantifiable data features. This article primarily describes the research progress of radiomics and clinical-radiomics combined model in the prediction of efficacy, the choice of treatment modality, and survival in transarterial chemoembolization (TACE) and TACE combination therapy for hepatocellular carcinoma.

Keywords: Computed tomography; Hepatocellular carcinoma; Magnetic resonance imaging; Radiomics; Transarterial chemoembolization.

Figure 1

Radiomics flowchart. Based on the imaging data obtained through scanning, the region of interest is delineated on the image, corresponding imaging features are extracted, features are filtered and dimensioned, and imageomics labels are constructed to obtain the rad-score of the image. Then, combined with clinical features, a joint prediction model is established, and a series of clinical applications are conducted by constructing nomogram, calibration curve, and receiver operating characteristic curve. ROI: Region of interest; ROC: Receiver operating characteristic; FPT: Ferroptosis; MRI: Magnetic resonance imaging.