Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.

Moon, Chung-Man; Lee, Yun Young; Kim, Doo-Young; Yoon, Woong; Baek, Byung Hyun; Park, Jae-Hyun; Heo, Suk-Hee; Shin, Sang-Soo; Kim, Seul Kee

Moon, Chung-Man; Lee, Yun Young; Kim, Doo-Young; Yoon, Woong; Baek, Byung Hyun; Park, Jae-Hyun; Heo, Suk-Hee; Shin, Sang-Soo; Kim, Seul Kee.

Afiliación

Moon CM; Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea.
Lee YY; Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea.
Kim DY; Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of Korea.
Yoon W; Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea.
Baek BH; Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea.
Park JH; Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea.
Heo SH; Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea.
Shin SS; Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea.
Kim SK; Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea.

Front Oncol ; 13: 1138069, 2023.

Article en En | MEDLINE | ID: mdl-37287921

Palabras clave

Ki-67; machine learning; meningioma; p53; radiomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2023 Tipo del documento: Article Pais de publicación: Suiza

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