Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Abstract

Background and objectiveFor 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.MethodsIn 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 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 contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (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 progression-free survival (PFS).ResultsFor the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (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).ConclusionsThis 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.

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

“…Similarly, Qu et al demonstrated that dynamic radiomics features, i.e. delta radiomics features calculated between different acquisition phases, better predicted the efficacy of immunotherapy in patients with colorectal liver metastases compared to conventional radiomics [62] . An additional benefit may derive from the combination of the two approaches, as shown by Chen et al [63] who obtained more reliable results in the prediction of treatment response in patients with metastatic melanoma when merging conventional and delta radiomics.…”

Prediction of Response to Anti-Angiogenic Treatment for Advanced Colorectal Cancer Patients: From Biological Factors to Functional Imaging

“…Qu et al evaluated a dynamic radiomics-based model for predicting the efficacy of antiangiogenic agents in mCRC patients with liver metastasis in order to help physicians in diagnosis and treatment. The application of dynamic radiomics variables performed better in predicting anti-angiogenic treatment response than conventional radiomic variables, appearing as a promising predicting tool worth of further research [101].…”

Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

“…Antiangiogenic drugs are increasingly combined with chemotherapy in CLM patients [69]. Qu et al [24] used a dynamic radiomics feature extraction method to construct a CT radiomics model for predicting the efficacy of antiangiogenic therapy in CLM patients, achieving a promising AUC of 0.945 and accuracy of 0.855. To identify CLM patients sensitive to therapy targeting the anti-epidermal growth factor pathway, Dercle et al [48] built a CT radiomics model using a deep learning and machine learning framework, achieving an AUC of 0.800 for predicting sensitivity.…”