Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.

Wang, Jia; Chen, Jingjing; Zhou, Ruizhi; Gao, Yuanxiang; Li, Jie

Wang, Jia; Chen, Jingjing; Zhou, Ruizhi; Gao, Yuanxiang; Li, Jie.

Afiliación

Wang J; Department of Ultrasound, Qingdao Women and Children Hospital, Shandong, Qingdao, China.
Chen J; Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China.
Zhou R; Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China.
Gao Y; Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China.
Li J; Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China. lijie7737@163.com.

BMC Cancer ; 22(1): 420, 2022 Apr 19.

Article en En | MEDLINE | ID: mdl-35439946

Asunto(s)

Imágenes de Resonancia Magnética Multiparamétrica; Neoplasias del Recto; Quimioradioterapia; Humanos; Aprendizaje Automático; Imagen por Resonancia Magnética/métodos; Terapia Neoadyuvante/métodos; Neoplasias del Recto/diagnóstico por imagen; Neoplasias del Recto/terapia; Estudios Retrospectivos

Palabras clave

Machine learning; Neoadjuvant chemoradiotherapy; Radiomics; Rectal cancer; Response

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias del Recto / Imágenes de Resonancia Magnética Multiparamétrica Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Cancer Asunto de la revista: NEOPLASIAS Año: 2022 Tipo del documento: Article País de afiliación: China

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