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Machine Learning Predicts Adequacy of Rapid On-site Evaluation in Fine Needle Aspirations in Lung Cancer Cytology.

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Title: Machine Learning Predicts Adequacy of Rapid On-site Evaluation in Fine Needle Aspirations in Lung Cancer Cytology.
Authors: Brechenmacher C; Institute of Pathology, Technical University of Munich, Munich, Germany; Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany. Electronic address: c.brechenmacher@tum.de., Kezlarian B; Memorial Sloan Kettering Cancer Center, New York, New York., Weirich G; Institute of Pathology, Technical University of Munich, Munich, Germany., Botelho S; Memorial Sloan Kettering Cancer Center, New York, New York., Wangsaroj B; Memorial Sloan Kettering Cancer Center, New York, New York., Buonocore D; Memorial Sloan Kettering Cancer Center, New York, New York., Schüffler PJ; Institute of Pathology, Technical University of Munich, Munich, Germany.
Source: The American journal of pathology [Am J Pathol] 2026 Jun; Vol. 196 (6), pp. 1287-1296. Date of Electronic Publication: 2026 Mar 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 0370502 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-2191 (Electronic) Linking ISSN: 00029440 NLM ISO Abbreviation: Am J Pathol Subsets: MEDLINE
Database: MEDLINE Ultimate
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        Value: 10.1016/j.ajpath.2026.03.004
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              Text: 2026 Jun
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