ArticleRadiology advances2026
Multicenter AI- versus expert-assisted RECIST target lesion measurements in follow-up body CT of patients with cancer.
Article in Radiology advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
32 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Manual lesion measurements remain the standard for assessing oncologic treatment response, despite being time-consuming and prone to substantial interreader variability, which may lead to inconsistent response classification and subsequent variability in treatment decisions. Purpose: To evaluate the impact of an artificial intelligence (AI) system for assisted lesion measurement on reading time and measurement consistency in follow-up CT examinations using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1). Methods and Materials: In this retrospective reader study, follow-up chest-abdomen-pelvis CT examinations from 212 oncology patients collected at 2 Dutch hospitals were assessed by 23 readers (15 radiologists, 8 residents) recruited from 11 international institutions under 3 conditions: unassisted, AI-assisted, and expert-assisted (using a prior radiologist's unassisted measurement). To prevent bias related to the source of the measurements, readers were informed that all support was AI-derived. Primary outcomes were reading time to completion and interobserver measurement variability. For each outcome, a Bayesian generalized linear mixed model was used to analyze the results. Results: AI assistance significantly reduced per-patient reading time versus unassisted reading (-36.0 s; 95% CI, -53.0 to -22.1). At the lesion level, it was associated with a small increase in variability relative to the expert-derived reference standard (1.32 mm; 95% CI, 0.83-1.91). At the patient level, AI assistance did not meaningfully affect change in the sum of longest diameters (-0.38 mm; 95% CI, -1.57 to 0.72), but increased RECIST outcome agreement by 7.7% (95% CI, 2.8-12.7) compared with unassisted reading. Expert-assisted reading yielded even higher interreader agreement (13.3%; 95% CI, 8.6-18.1). Conclusion: AI assistance reduced reading time and improved patient-level RECIST agreement, despite a small increase in lesion-level measurement variability. These findings suggest that AI-assisted RECIST assessment may improve workflow and response classification consistency, while also providing a benchmark from expert-assisted reading for future AI development.
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