医学
无线电技术
胶质瘤
磁共振成像
置信区间
队列
逻辑回归
正电子发射断层摄影术
放射科
曲线下面积
肿瘤科
核医学
接收机工作特性
内科学
癌症研究
作者
Kai Wang,Zhen Qiao,Xiaobin Zhao,Xiaotong Li,Xin Wang,Tingfan Wu,Zhongwei Chen,Di Fan,Chen Qian,Aiming Lin
标识
DOI:10.1007/s00259-019-04604-0
摘要
Abstract Purpose To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients. Methods Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort ( n = 112). Textural features were extracted from postoperative 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET), 11 C-methionine ( 11 C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients. Results The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence ( p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of 18 F-FDG, maximum of TBR of 11 C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975–1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881–0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful. Conclusions Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.
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