Abstract
PurposeTo develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients.MethodsData 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 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), 11C-methionine (11C-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.ResultsThe 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 18F-FDG, maximum of TBR of 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 diagnosis model was clinically useful.ConclusionsOur developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.
Highlights
This article is part of the Topical Collection on Oncology – BrainElectronic supplementary material The online version of this article contains supplementary material, which is available to authorized users.Glioma is the most common and aggressive malignant brain tumor in adults [1]
There were no significant differences in the patient features between the primary and validation cohorts, either within the tumor recurrence cohort or in the radiation necrosis cohort (Supplemental Tables 1-3)
The difference between the rad-scores of the tumor recurrence and radiation necrosis patients in the primary cohort was significant (p < 0.001), which was confirmed in the validation cohort (p < 0.001)
Summary
This article is part of the Topical Collection on Oncology – Brain. The accurate identification of tumor recurrence in patients with gliomas is crucial for selecting treatment strategies to provide better therapeutic management. Previous studies revealed that 18F-fluorodeoxyglucose (18F-FDG) [2, 3], 11C-methionine (11C-MET) [4], 18Ffluoroethyl-L-tyrosine (18F-FET) [5, 6], and 11C-choline [7] PET, along with MRI, can differentiate between tumor recurrence and radiation necrosis with various levels of diagnostic. Conventional hybrid PET/ MRI studies did not fully perform deep mining of the intrinsic features of the images, which could be further investigated using advanced methodology in a larger cohort [8,9,10,11]
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