Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases - Qu, Hui; Zhai, Huan; Zhang, Shuairan; Chen, Wenjuan; Zhong, Hongshan; Cui, Xiaoyu (Frontiers in Oncology, 2023)
Background and Objective: For patients with advanced 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. Methods: 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 treatment efficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and CECT scans were performed before treatment. Patients with multiple primary lesions and missing clinical or imaging information were excluded. AUC and accuracy were used to evaluate model performance. ROIs were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and PFS. Results: For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the RCR feature performed best among all features and best in linear discriminant analysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan–Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P <0.0001 for survival analysis). Conclusions: This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.