ArticleEuropean radiology2026
AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesThis 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.
methodsWe 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.
resultsThe 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).
conclusionsAI-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.
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