Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans

被引:0
作者
Ward Hendrix
Nils Hendrix
Ernst T. Scholten
Mariëlle Mourits
Joline Trap-de Jong
Steven Schalekamp
Mike Korst
Maarten van Leuken
Bram van Ginneken
Mathias Prokop
Matthieu Rutten
Colin Jacobs
机构
[1] Radboud University Medical Center,Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department
[2] Jeroen Bosch Hospital,Radiology Department
[3] Jheronimus Academy of Data Science,Radiology Department
[4] Canisius Wilhelmina Hospital,Radiology Department
[5] St. Antonius Hospital,Radiology Department
[6] University Medical Center Groningen,undefined
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Communications Medicine | / 3卷
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摘要
Early-stage lung cancer can be diagnosed after identifying an abnormal spot on a chest CT scan ordered for other medical reasons. These spots or lung nodules can be overlooked by radiologists, as they are not necessarily the focus of an examination and can be as small as a few millimeters. Software using Artificial Intelligence (AI) technology has proven to be successful for aiding radiologists in this task, but its performance is understudied outside a lung cancer screening setting. We therefore developed and validated AI software for the detection of cancerous nodules or non-cancerous nodules that would need attention. We show that the software can reliably detect these nodules in a non-screening setting and could potentially aid radiologists in daily clinical practice.
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