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Objectives <p>This study evaluates the clinical utility of an artificial intelligence (AI)-driven volumetric approach for assessing treatment response in colorectal liver metastases (CRLM) compared to conventional RECIST 1.1 measurements.</p> Methods <p>We developed and validated an AI segmentation pipeline using the nnU-Net framework trained on 476 CT scans from three datasets (LiTS, MetaRec, and MetaBrest). Performance was evaluated on 112 held-out CT scans from MetaBrest. For clinical validation, 157 patients with CRLM from the PRODIGE 9–FFCD clinical trial with baseline and 3-month follow-up CT scans were assessed using both RECIST 1.1 and AI-volumetric methods. Overall survival analysis was performed to compare the prognostic value of both approaches.</p> Results <p>The nnU-Net model achieved a Dice similarity coefficient of 0.775 ± 0.211, with performance varying by lesion size (large: 0.899 ± 0.046; medium: 0.821 ± 0.135; small: 0.566 ± 0.330). In the overall validation cohort, both RECIST 1.1 and volumetric assessment demonstrated significant prognostic value for overall survival (<i>p</i> &lt; 0.0001). In patients with liver-only metastases (<i>n</i> = 43), volumetric assessment showed significant prognostic stratification (<i>p</i> = 0.0150 at −30% threshold; <i>p</i> = 0.0409 at −50% threshold), while RECIST 1.1 failed to achieve statistical significance (<i>p</i> = 0.2088).</p> Conclusions <p>AI-driven volumetric assessment of CRLM provides significant prognostic information that complements or potentially surpasses conventional RECIST 1.1 measurements, particularly in patients with liver-limited metastatic disease. Automation through deep learning makes comprehensive 3D evaluation feasible in clinical routine.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can AI-powered volumetric assessment of colorectal liver metastases improve prognostic stratification for overall survival compared with conventional RECIST 1.1 criteria?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>AI-based volumetric tumor burden showed strong prognostic value for overall survival and provided superior risk stratification compared with RECIST 1.1 in liver-only metastatic disease.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Automated deep learning-based volumetric assessment enables comprehensive 3D evaluation of colorectal liver metastases, overcoming historical barriers and potentially improving prognostic assessment and clinical decision-making.</i></p> Graphical Abstract <p></p>

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AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Marwan Abbas,
  • Gustavo Andrade-Miranda,
  • Vincent Bourbonne,
  • Vincent Jaouen,
  • Côme Lepage,
  • Thomas Aparicio,
  • Dimitris Visvikis,
  • Bogdan Badic,
  • Pierre-Henri Conze

摘要

Objectives

This study evaluates the clinical utility of an artificial intelligence (AI)-driven volumetric approach for assessing treatment response in colorectal liver metastases (CRLM) compared to conventional RECIST 1.1 measurements.

Methods

We developed and validated an AI segmentation pipeline using the nnU-Net framework trained on 476 CT scans from three datasets (LiTS, MetaRec, and MetaBrest). Performance was evaluated on 112 held-out CT scans from MetaBrest. For clinical validation, 157 patients with CRLM from the PRODIGE 9–FFCD clinical trial with baseline and 3-month follow-up CT scans were assessed using both RECIST 1.1 and AI-volumetric methods. Overall survival analysis was performed to compare the prognostic value of both approaches.

Results

The nnU-Net model achieved a Dice similarity coefficient of 0.775 ± 0.211, with performance varying by lesion size (large: 0.899 ± 0.046; medium: 0.821 ± 0.135; small: 0.566 ± 0.330). In the overall validation cohort, both RECIST 1.1 and volumetric assessment demonstrated significant prognostic value for overall survival (p < 0.0001). In patients with liver-only metastases (n = 43), volumetric assessment showed significant prognostic stratification (p = 0.0150 at −30% threshold; p = 0.0409 at −50% threshold), while RECIST 1.1 failed to achieve statistical significance (p = 0.2088).

Conclusions

AI-driven volumetric assessment of CRLM provides significant prognostic information that complements or potentially surpasses conventional RECIST 1.1 measurements, particularly in patients with liver-limited metastatic disease. Automation through deep learning makes comprehensive 3D evaluation feasible in clinical routine.

Key Points

Question Can AI-powered volumetric assessment of colorectal liver metastases improve prognostic stratification for overall survival compared with conventional RECIST 1.1 criteria?

Findings AI-based volumetric tumor burden showed strong prognostic value for overall survival and provided superior risk stratification compared with RECIST 1.1 in liver-only metastatic disease.

Clinical relevance Automated deep learning-based volumetric assessment enables comprehensive 3D evaluation of colorectal liver metastases, overcoming historical barriers and potentially improving prognostic assessment and clinical decision-making.

Graphical Abstract