F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.
[PURPOSE] Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization (TARE) based on pre-procedural fluorine-18-fluoro
- 95% CI 0.71-1
APA
Topcuoglu OM, Tuncer O, et al. (2026). F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.. Japanese journal of radiology. https://doi.org/10.1007/s11604-026-01949-z
MLA
Topcuoglu OM, et al.. "F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.." Japanese journal of radiology, 2026.
PMID
41604060
Abstract
[PURPOSE] Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization (TARE) based on pre-procedural fluorine-18-fluoro-deoxy glucose positron emission tomography (F-FDG PET) radiomics and clinical information.
[MATERIALS AND METHODS] Patients with CRCLM who underwent TARE, between March 2015 and May 2025, were consecutively included. Largest tumors were segmented semiautomatically using pre-procedural F-FDG PET images. Radiomics features were extracted, clinical information were collected. Two datasets were created comprising radiomics-only and clinico-radiomic features. Datasets were divided 60:40 for training and testing. Top 5 features were selected based on feature importances. Random Forest, Extreme Gradient Boosting, Logistic Regression models were trained. Test-set area under the curves (AUCs) for predicting post-treatment target lesion local progression were calculated and compared using DeLong's test. Sensitivity, specificity, accuracy and F1 scores were calculated at the optimal cut-offs.
[RESULTS] Seventy-four patients out of 96 patients were included. Top five selected features in the radiomics-only dataset were Coarseness, IMC1, Zone Entropy, Size-Zone Non-Uniformity, and Strength. In the clinico-radiomic dataset, AST and ALT levels were substituted among the top five features. Radiomics-only features demonstrated AUCs ranging from 0.90 (95% CI 0.71-1) to 0.81 (95% CI 0.51-1) in the test-set while clinico-radiomics dataset AUCs varied between 0.88 (95% CI 0.51-1) and 0.84 (0.62-1).
[CONCLUSION] F-FDG PET radiomics based models can predict the local response to TARE in patients with CRCLM, in this series.
[MATERIALS AND METHODS] Patients with CRCLM who underwent TARE, between March 2015 and May 2025, were consecutively included. Largest tumors were segmented semiautomatically using pre-procedural F-FDG PET images. Radiomics features were extracted, clinical information were collected. Two datasets were created comprising radiomics-only and clinico-radiomic features. Datasets were divided 60:40 for training and testing. Top 5 features were selected based on feature importances. Random Forest, Extreme Gradient Boosting, Logistic Regression models were trained. Test-set area under the curves (AUCs) for predicting post-treatment target lesion local progression were calculated and compared using DeLong's test. Sensitivity, specificity, accuracy and F1 scores were calculated at the optimal cut-offs.
[RESULTS] Seventy-four patients out of 96 patients were included. Top five selected features in the radiomics-only dataset were Coarseness, IMC1, Zone Entropy, Size-Zone Non-Uniformity, and Strength. In the clinico-radiomic dataset, AST and ALT levels were substituted among the top five features. Radiomics-only features demonstrated AUCs ranging from 0.90 (95% CI 0.71-1) to 0.81 (95% CI 0.51-1) in the test-set while clinico-radiomics dataset AUCs varied between 0.88 (95% CI 0.51-1) and 0.84 (0.62-1).
[CONCLUSION] F-FDG PET radiomics based models can predict the local response to TARE in patients with CRCLM, in this series.