Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

Yin, Ruo-Han; Yang, You-Chang; Tang, Xiao-Qiang; Shi, Hai-Feng; Duan, Shao-Feng; Pan, Chang-Jie

Yin, Ruo-Han; Yang, You-Chang; Tang, Xiao-Qiang; Shi, Hai-Feng; Duan, Shao-Feng; Pan, Chang-Jie.

Afiliación

Yin RH; Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
Yang YC; Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
Tang XQ; Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
Shi HF; Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
Duan SF; Precision Health Institution, GE Healthcare (China), Shanghai, China.
Pan CJ; Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.

J Xray Sci Technol ; 29(6): 1149-1160, 2021.

Article en En | MEDLINE | ID: mdl-34657848

Asunto(s)

Carcinoma de Células Renales; Neoplasias Renales; Teorema de Bayes; Carcinoma de Células Renales/diagnóstico por imagen; Humanos; Neoplasias Renales/diagnóstico por imagen; Aprendizaje Automático; Estudios Prospectivos; Estudios Retrospectivos; Tomografía Computarizada por Rayos X/métodos

Palabras clave

Radiomics; X-ray computed tomography; machine learning; renal clear cell carcinoma

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Neoplasias Renales Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article

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