ArticleQuantitative imaging in medicine and surgery2026
Value of the DeepSeek-R1 large language model in extracting structured data from magnetic resonance imaging reports of rectal cancer and assisting in tumor staging.
Article in Quantitative imaging in medicine and surgery, 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
Background: Structured magnetic resonance imaging (MRI) reports improve rectal cancer diagnosis and treatment management, whereas free-text reports better describe complex MRI features, leading to divergent preferences and inconsistent acceptance among radiologists. This study aimed to evaluate the potential of DeepSeek-R1 to assist in the analysis of MRI reports for rectal cancer. Methods: This retrospective study analyzed 465 MRI reports of rectal cancer. Sixty reports were used to facilitate structured information extraction, refine reporting reminders and complete preliminary screening of large language models (LLMs). LLMs from the same developers were compared [i.e., GPT-4 Results: Five of the LLMs evaluated, including DSR1-671B, Wenxinyiyan-4, Wenxinyiyan-4Turbo, GPT-4, and GPT-4o, were identified in the preliminary screening by the same developers. All five LLMs processed reports significantly faster than did radiologists (time per report: 22.4-84.6 Conclusions: In an English-language environment with human supervision, DSR1-671B can perform structured information extraction from MRI reports of rectal cancer, generate reporting cues, and enable TN staging based on the improved reports.
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