Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT.

Obmann, Markus M; Cosentino, Aurelio; Cyriac, Joshy; Hofmann, Verena; Stieltjes, Bram; Boll, Daniel T; Yeh, Benjamin M; Benz, Matthias R

Obmann, Markus M; Cosentino, Aurelio; Cyriac, Joshy; Hofmann, Verena; Stieltjes, Bram; Boll, Daniel T; Yeh, Benjamin M; Benz, Matthias R.

Afiliación

Obmann MM; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland. Markus.Obmann@usb.ch.
Cosentino A; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA. Markus.Obmann@usb.ch.
Cyriac J; Department of Surgical Sciences, Radiology Unit, University of Turin, Turin, Italy.
Hofmann V; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Stieltjes B; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Boll DT; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Yeh BM; Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
Benz MR; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

Abdom Radiol (NY) ; 45(6): 1922-1928, 2020 06.

Article en En | MEDLINE | ID: mdl-31451887

Asunto(s)

Neoplasias Renales; Tomografía Computarizada por Rayos X; Algoritmos; Medios de Contraste; Humanos; Neoplasias Renales/diagnóstico por imagen; Aprendizaje Automático; Interpretación de Imagen Radiográfica Asistida por Computador; Estudios Retrospectivos; Sensibilidad y Especificidad

Palabras clave

Artificial intelligence; Computer-assisted radiographic image interpretation; Dual-energy X-ray computed tomography; Kidney diseases; Supervised machine learning

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Neoplasias Renales Tipo de estudio: Diagnostic_studies / Observational_studies Límite: Humans Idioma: En Revista: Abdom Radiol (NY) Año: 2020 Tipo del documento: Article País de afiliación: Suiza

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