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Brechenmacher, C. ; Kezlarian, B.* ; Weirich, G.* ; Botelho, S.* ; Wangsaroj, B.* ; Buonocore, D.* ; Schüffler, P.J.*

Machine learning predicts adequacy of rapid onsite evaluation in fine needle aspirations in lung cancer cytology.

Am. J. Pathol., DOI: 10.1016/j.ajpath.2026.03.004 (2026)
DOI PMC
Lung cancer is projected to become the leading cause of cancer-related mortality in both smoking and non-smoking populations. Rapid onsite evaluation (ROSE) of fine-needle aspiration specimens is essential for timely diagnosis and procedural decision-making during lung cancer assessment. We developed a machine-learning pipeline for cell-based adequacy assessment and lesion detection that integrates automated cell detection, convolutional neural network-based cell classification, and slide-level aggregation using a random forest model. On held-out test data, binary classifiers for lymphocytes and tumor cells achieved accuracies of 91.5% and 92.7% with recalls of 92.6% and 93.1%, respectively. The end-to-end ROSE system demonstrated class accuracies of 82-85%, comparable to human cytologist performance, and a lesion-focused classifier reached a recall of 92.0%. These findings indicate that machine-learning-based cell analysis can support ROSE by expediting adequacy assessment and improving diagnostic yield during TBNA procedures.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 0002-9440
e-ISSN 1525-2191
Journal American Journal of Pathology, The
Publisher Elsevier
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)