Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

Transarterial radioembolization (TARE) is one of the most promising therapeutic options for hepatic masses. Radiomics features, which are quantitative numeric features extracted from medical images, are considered to have potential in predicting treatment response in TARE. This article aims to provide meta-analytic evidence and critically appraise the methodology of radiomics studies published in this regard. A systematic search was performed on PubMed, Scopus, Embase, and Web of Science. All relevant articles were retrieved, and the characteristics of the studies were extracted. The Radiomics Quality Score and Checklist for Evaluation of Radiomics Research were used to assess the methodologic quality of the studies. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve in predicting objective response were determined. The systematic review included 15 studies. The average Radiomics Quality Score of these studies was 11.4 ± 2.1, and the average Checklist for Evaluation of Radiomics Research score was 33± 6.7. There was a notable correlation (correlation coefficient = 0.73) between the two metrics. Adherence to quality measures differed considerably among the studies and even within different components of the same studies. The pooled sensitivity and specificity of the radiomics models in predicting complete or partial response were 83.5% (95% confidence interval 76%-88.9%) and 86.7% (95% confidence interval 78%-92%), respectively. Radiomics models show great potential in predicting treatment response in TARE of hepatic lesions. However, the heterogeneity seen between the methodologic quality of studies may limit the generalizability of the results. Future initiatives should aim to develop radiomics signatures using multiple external datasets and adhere to quality measures in radiomics methodology.