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Objectives <p>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.</p> Materials and methods <p>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.</p> Results <p>The median age of the final study population with baseline imaging was 66 years (IQR, 59–71), with 67.1% (<i>n</i> = 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; <i>p</i> = 0.011), outperforming baseline radiomics (AUC = 0.69, 95% CI = 0.47–0.86; <i>p</i> = 0.100) and baseline volume (AUC = 0.56, 95% CI = 0.37–0.74; <i>p</i> = 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 <i>p</i> = 0.0267). Integrating imaging features with laboratory variables improved lesion-level response assessment (AUC = 0.86, 95% CI = 0.66–0.99; <i>p</i> = 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; <i>p</i> = 0.008).</p> Conclusion <p>Radiomics-based machine learning models could predict overall survival in patients treated with irinotecan-TACE, offering a potential tool for patient selection.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> Can radiomics and machine learning predict outcomes in patients with colorectal liver metastases treated with irinotecan-TACE, aiding in patient stratification and selection?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> Baseline intensity features predicted overall survival (AUC = 0.79), while delta radiomics identified lesion response (AUC = 0.83) in a multicenter cohort.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> 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.</i></p> Graphical Abstract <p></p>

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Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Zuhir Bodalal,
  • Francisco Javier Mendoza Ferradás,
  • Olga Maxouri,
  • Roberto Iezzi,
  • Aleksandar Gjoreski,
  • Stavros Spiliopoulos,
  • Zoltan Bansaghi,
  • Belarmino Gonçalves,
  • Bleranda Zeka,
  • Nathalie Kaufmann,
  • Julien Taieb,
  • Regina Beets-Tan,
  • Philippe L. Pereira,
  • Fernando Gómez Muñoz

摘要

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.

Graphical Abstract