Wu Chunhui, Lin Xiaoping, Li Zhoulei, Chen Zhifeng, Xie Wenhui, Zhang Xiangsong, Wang Xiaoyan
Department of Nuclear Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Front Cell Dev Biol. 2021 Dec 14;9:783466. doi: 10.3389/fcell.2021.783466. eCollection 2021.
To develop an effective diagnostic model for bone metastasis of gastric cancer by combining F-FDG PET/CT and clinical data. A total of 212 gastric cancer patients with abnormal bone imaging scans based on F-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. 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 ( = 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 ( = 0.294) and good discriminating ability (AUC = 0.925). The diagnostic model combined with the F-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 F-FDG PET/CT findings could improve the diagnostic efficacy of identifying bone metastases in gastric cancer.
通过结合F-FDG PET/CT与临床数据,开发一种有效的胃癌骨转移诊断模型。回顾性纳入2009年9月至2020年3月期间基于F-FDG PET/CT进行骨成像扫描异常的212例胃癌患者。通过多因素逻辑回归分析确定胃癌骨转移的危险因素,并用于创建列线图。使用受试者工作特征曲线和校准图评估列线图的性能。纳入骨骼相关症状、贫血、骨病变的SUVmax、骨改变、骨病变位置、碱性磷酸酶(ALP)、乳酸脱氢酶(LDH)、癌胚抗原(CEA)和糖类抗原19-9的二元逻辑回归模型的诊断效能显著高于仅使用临床因素的模型(P = 0.008)。根据校准检验,使用临床和影像数据相结合的胃癌骨转移诊断模型显示出良好的拟合优度(P = 0.294)和良好的鉴别能力(曲线下面积[AUC]=0.925)。与其他研究模型相比,结合F-FDG PET/CT结果和临床数据的诊断模型对胃癌骨转移显示出更好的诊断性能。与仅使用临床因素的模型相比,额外的F-FDG PET/CT结果可提高胃癌骨转移的诊断效能。