Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence.

Katsuta, Yoshiyuki; Kadoya, Noriyuki; Sugai, Yuto; Katagiri, Yu; Yamamoto, Takaya; Takeda, Kazuya; Tanaka, Shohei; Jingu, Keiichi

Katsuta, Yoshiyuki; Kadoya, Noriyuki; Sugai, Yuto; Katagiri, Yu; Yamamoto, Takaya; Takeda, Kazuya; Tanaka, Shohei; Jingu, Keiichi.

Afiliación

Katsuta Y; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Kadoya N; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Sugai Y; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Katagiri Y; Department of Radiation Oncology, Japan Red Cross Ishinomaki Hospital, Ishinomaki 986-8522, Japan.
Yamamoto T; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Takeda K; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Tanaka S; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.
Jingu K; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan.

Diagnostics (Basel) ; 12(6)2022 May 31.

Article en En | MEDLINE | ID: mdl-35741164

Palabras clave

NSCLC; artificial intelligence; machine learning; radiation pneumonitis; radiotherapy

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Japón

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