Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals

IMPORTANCE: Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening. OBJEC...

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Detalles Bibliográficos
Autores principales: Landy, Rebecca, Wang, Vivian L., Baldwin, David R., Pinsky, Paul F., Cheung, Li C., Castle, Philip E., Skarzynski, Martin, Robbins, Hilary A., Katki, Hormuzd A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2023
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020880/
https://www.ncbi.nlm.nih.gov/pubmed/36929398
http://dx.doi.org/10.1001/jamanetworkopen.2023.3273

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020880/
https://www.ncbi.nlm.nih.gov/pubmed/36929398
http://dx.doi.org/10.1001/jamanetworkopen.2023.3273

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