RT Journal Article
SR Electronic
T1 Dr.
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1505
OP 1505
VO 60
IS supplement 1
A1 Kai Wang
A1 Zhen Qiao
A1 Xiaobin Zhao
A1 Xiaotong Li
A1 Xin Wang
A1 Tingfan Wu
A1 Zhongwei Chen
A1 Di Fan
A1 Qian Chen
A1 Lin Ai
YR 2019
UL http://jnm.snmjournals.org/content/60/supplement_1/1505.abstract
AB 1505Purpose: To develop and validate an integrated model for predicting tumor recurrence in glioma patients. Patients and Methods: Data from a total of 160 patients with pathologically confirmed gliomas who received surgical resection in the period from April 2015 to March 2018 were analyzed in the study. The diagnostic model was developed in a training cohort consisting of 112 out of 160 patients. Textural features were extracted from postoperative 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), 11C-methionine (11C-MET) PET, and contrast-enhanced T1-weighted magnetic resonance images. The latest absolute shrinkage and selection operator regression model was used for data dimension reduction, 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 training and validation cohorts). Predictors contained in the individualized prediction model included the radiomics signature, quantitative uptake parameters of both 18F-FDG and 11C-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 prediction model was clinically useful. Conclusions: Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.