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Automated RECIST tumor response classification through prompt-guided large language models.

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Title: Automated RECIST tumor response classification through prompt-guided large language models.
Authors: Mergen M; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany. markus.mergen@tum.de.; Medical Clinic and Polyclinic II, TUM School of Medicine and Health, TUM University Hospital, Technical University Munich (TUM), Ismaningerstr. 22, 81675, Munich, Germany. markus.mergen@tum.de., Busch F; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany., Sauter AP; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany., Pfeiffer D; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany.; Munich Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany., Makowski MR; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany., Spitzl D; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany., Gassert FT; Department of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, 81675, Munich, Germany.
Source: Scientific reports [Sci Rep] 2026 May 27; Vol. 16 (1). Date of Electronic Publication: 2026 May 27.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-54979-y