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A Diagnostic Nomogram Based on 18F-FDG PET/CT for Bone Metastasis of Gastric Cancer.
Wu, Chunhui; Lin, Xiaoping; Li, Zhoulei; Chen, Zhifeng; Xie, Wenhui; Zhang, Xiangsong; Wang, Xiaoyan.
Afiliação
  • Wu C; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Lin X; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Li Z; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Chen Z; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Xie W; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zhang X; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Wang X; Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Front Cell Dev Biol ; 9: 783466, 2021.
Article em En | MEDLINE | ID: mdl-34970546
Purpose: To develop an effective diagnostic model for bone metastasis of gastric cancer by combining 18F-FDG PET/CT and clinical data. Materials and Methods: A total of 212 gastric cancer patients with abnormal bone imaging scans based on 18F-FDG PET/CT were retrospectively enrolled between September 2009 and March 2020. Risk factors for bone metastasis of gastric cancer were identified by multivariate logistic regression analysis and used to create a nomogram. The performance of the nomogram was evaluated by using receiver operating characteristic curves and calibration plots. Results: The diagnostic power of the binary logistic regression model incorporating skeleton-related symptoms, anemia, the SUVmax of bone lesions, bone changes, the location of bone lesions, ALP, LDH, CEA, and CA19-9 was significantly higher than that of the model using only clinical factors (p = 0.008). The diagnostic model for bone metastasis of gastric cancer using a combination of clinical and imaging data showed an appropriate goodness of fit according to a calibration test (p = 0.294) and good discriminating ability (AUC = 0.925). Conclusions: The diagnostic model combined with the 18F-FDG PET/CT findings and clinical data showed a better diagnosis performance for bone metastasis of gastric cancer than the other studied models. Compared with the model using clinical factors alone, the additional 18F-FDG PET/CT findings could improve the diagnostic efficacy of identifying bone metastases in gastric cancer.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cell Dev Biol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cell Dev Biol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China