Radiomics-Based Outcome Prediction in Transarterial Radioembolization (TARE) of Hepatic Malignancies: A Systematic Review and Quality Appraisal

Background: Transarterial radioembolization (TARE) with Yttrium-90 (90Y) microspheres is an established locoregional treatment for primary and secondary hepatic malignancies. Treatment response is heterogeneous, and accurate pre-procedural prediction of outcome remains a clinical challenge. Radiomics, defined as the high-throughput extraction of quantitative imaging features, may capture subvisual tumour heterogeneity that might be predictive of response, therefore enabling individualised patient selection and treatment planning. Objectives: To systematically review the current evidence on radiomics-based models derived from pre- and post-procedural imaging for predicting treatment response and survival outcomes in patients with primary and secondary hepatic lesions that were treated with TARE, and to evaluate the methodological quality of included studies. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, and Web of Science. Studies reporting radiomics, texture analysis, dosiomics, or machine learning applied to imaging in patients with hepatic lesions treated with TARE, with at least one reported performance metric, were included. Methodological quality was assessed using the Radiomics Quality Score (RQS) and risk of bias and applicability using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Due to substantial heterogeneity, a narrative synthesis was performed. Results: Twenty-one studies encompassing 1734 patients were included (up to 73 patients may be counted twice because two cohorts overlap). Studies evaluated patients with hepatocellular carcinoma (HCC; n = 12), intrahepatic cholangiocarcinoma (iCC; n = 2), colorectal cancer liver metastases (CLMs; n = 3), and mixed tumour cohorts (n = 4). Imaging modalities included MRI (n = 9), CT (n = 5), 18F-FDG PET or PET/CT (n = 3), 90Y PET or SPECT (n = 2), cone-beam CT (n = 1), and CT perfusion (n = 1). All studies were retrospective, including two post hoc analyses of randomised trial cohorts, and none used external validation. Eighteen studies reported an area under the curve (AUC). The highest reported AUC ranged from 0.71 to 0.90 in eight studies with no validation, from 0.73 to 1.00 in six studies using internal cross-validation, and from 0.80 to 0.90 in four studies using an internal hold-out set. The median RQS was 2 (interquartile range 1 to 5; maximum 36), and all 21 studies had a high overall risk of bias on PROBAST. Conclusions: Radiomic models for predicting TARE outcomes show moderate to high apparent discrimination, but the evidence comes from small retrospective cohorts, is at high risk of bias, and has not been externally validated. Adding clinical variables to radiomic features produced only small gains. Prospective, multi-centre studies with standardised segmentation, harmonised radiomics pipelines, and robust external validation are needed before routine clinical adoption.