Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model asses...

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Veröffentlicht in:	Journal of clinical oncology Jg. 41; H. 12; S. 2191
Hauptverfasser:	Mikhael, Peter G, Wohlwend, Jeremy, Yala, Adam, Karstens, Ludvig, Xiang, Justin, Takigami, Angelo K, Bourgouin, Patrick P, Chan, PuiYee, Mrah, Sofiane, Amayri, Wael, Juan, Yu-Hsiang, Yang, Cheng-Ta, Wan, Yung-Liang, Lin, Gigin, Sequist, Lecia V, Fintelmann, Florian J, Barzilay, Regina
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 20.04.2023
Schlagworte:	Deep Learning Early Detection of Cancer - methods Humans Lung Lung Neoplasms - diagnostic imaging Mass Screening - methods Tomography, X-Ray Computed
ISSN:	1527-7755, 1527-7755
Online-Zugang:	Weitere Angaben
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