Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

Mao, Liting; Chen, Huan; Liang, Mingzhu; Li, Kunwei; Gao, Jiebing; Qin, Peixin; Ding, Xianglian; Li, Xin; Liu, Xueguo

Mao, Liting; Chen, Huan; Liang, Mingzhu; Li, Kunwei; Gao, Jiebing; Qin, Peixin; Ding, Xianglian; Li, Xin; Liu, Xueguo.

Afiliação

Mao L; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Chen H; Department of Radiology, The Second Affiliated Hosptial of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510120, China.
Liang M; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Li K; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Gao J; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Qin P; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Ding X; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Li X; Department of Radiology, The 5 Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China.
Liu X; GE Healthcare, Guangzhou 510000, China.

Quant Imaging Med Surg ; 9(2): 263-272, 2019 Feb.

Article em En | MEDLINE | ID: mdl-30976550

Palavras-chave

CT; Radiomics; prediction; solid pulmonary nodules

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Quant Imaging Med Surg Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

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