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Peer-Reviewed Publication
JNCI Cancer Spectr2024;8(5)September 2, 2024Journal Article

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

Roger Y Kim1, Clarisa Yee2, Sana Zeb1, Jennifer Steltz1, Andrew J Vickers3, Katharine A Rendle4, Nandita Mitra5, Lyndsey C Pickup6, David M DiBardino1, Anil Vachani1
1Division of Pulmonary, Allergy and Critical Care, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
2NYU Langone Health, New York City, NY, USA.
3Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
4Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
6Optellum Ltd, Oxford, UK.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospecti…

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