CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.

Wang, Yiang; Wang, Mandi; Cao, Peng; Wong, Esther M F; Ho, Grace; Lam, Tina P W; Han, Lujun; Lee, Elaine Y P

Wang, Yiang; Wang, Mandi; Cao, Peng; Wong, Esther M F; Ho, Grace; Lam, Tina P W; Han, Lujun; Lee, Elaine Y P.

Afiliação

Wang Y; Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
Wang M; Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
Cao P; Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
Wong EMF; Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China.
Ho G; Department of Radiology, Queen Mary Hospital, Hong Kong, China.
Lam TPW; Department of Radiology, Queen Mary Hospital, Hong Kong, China.
Han L; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China.
Lee EYP; Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.

Quant Imaging Med Surg ; 13(8): 5218-5229, 2023 Aug 01.

Article em En | MEDLINE | ID: mdl-37581064

Palavras-chave

Ovarian cancer (OC); automated segmentation; computed tomography; deep learning; radiomics

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Quant Imaging Med Surg Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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