biostudies-literatureQu HFundamental Research Funds for the Central UniversitiesNIAAA NIH HHSNational High-tech Research and Development Program992096https://www.ebi.ac.uk/biostudies/studies/S-EPMC9939899biostudies-literatureUnknown13<h4>Background and objective</h4>For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.<h4>Methods</h4>In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this rFrontiers in oncologyDynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.PMC9939899R01 AA022701Zhai HChen WCui XQu HZhong HZhang SfalseDynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.<h4>Background and objective</h4>For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.<h4>Methods</h4>In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this r2023-01-01T00:00:00Z20232025-04-04T20:09:53.395Z2025-04-04T20:09:53.395ZS-EPMC99398993681481210.3389/fonc.2023.992096