Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the
Zuhir Bodalal1,2, Francisco Javier Mendoza Ferradás3, Olga Maxouri4,5
1GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands. z.elkarghali@maastrichtuniversity.nl.
Objectives:
Transarterial chemoembolization (TACE) is a promising locoregional therapy for unresectable colorectal liver metastases, but patient selection remains challenging. We aimed to develop and validate prognostic radiomics-based machine learning models in a multicenter, prospectively collected drug-eluting microsphere TACE cohort.
Materials And Methods:
We retrospectively analyzed 76 patients (176 lesions) from the prospective CIREL registry trial. Radiomic features were extracted from each lesion. We tested three types of imaging markers: general radiomics, intensity-based features, and lesion volume. For each, we derived baseline and delta features, reflecting the difference in feature vector values between baseline and first follow-up. Using a center-based split, we trained genetic/evolutionary machine learning models to predict survival and lesion-level response.
Results:
The median age of the final study population with baseline imaging was 66 years (IQR, 59-71), with 67.1% (n = 51) of patients identifying as male. On external validation, the baseline intensity algorithm was the only significant survival-prediction model (AUC = 0.79, 95% CI = 0.57-0.95; p = 0.011), outperforming baseline radiomics (AUC = 0.69, 95% CI = 0.47-0.86; p = 0.100) and baseline volume (AUC = 0.56, 95% CI = 0.37-0.74; p = 0.574). Radiomic prediction models stratified patients into distinct overall survival risk groups, with low-risk patients showing a median survival of 696 days versus 453 days (log-rank p = 0.0267). Integrating imaging features with laboratory variables improved lesion-level response assessment (AUC = 0.86, 95% CI = 0.66-0.99; p = 0.006), but did not enhance OS prediction. Lesion-level response was best identified by delta radiomics (AUC = 0.83, 95% CI = 0.63-0.97; p = 0.008).
Conclusion:
Radiomics-based machine learning models could predict overall survival in patients treated with irinotecan-TACE, offering a potential tool for patient selection.
Key Points:
Question Can radiomics and machine learning predict outcomes in patients with colorectal liver metastases treated with irinotecan-TACE, aiding in patient stratification and selection? Findings Baseline intensity features predicted overall survival (AUC = 0.79), while delta radiomics identified lesion response (AUC = 0.83) in a multicenter cohort. Clinical relevance These models can help identify patients likely to benefit from irinotecan-TACE and lesions most responsive to treatment. Further development would enable personalized therapy that may improve survival and reduce unnecessary interventions in non-responders.
