Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

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.

FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation

“…Despite the promising performance of the developed radiomics model in the test dataset, further validation of the developed model in a large multicentric dataset is necessary. Since some studies have shown a synergistic effect by combining PET and MRI radiomics [ 24 , 72 ], the combination of FET PET radiomics with structural, as well as advanced, MRI radiomics should also be further investigated, especially in the light of the growing number of hybrid PET/MR scanners. This pilot study results are promising and suggest an important role for FET PET radiomics in neurooncology.…”

Artificial intelligence applications for oncological positron emission tomography imaging

“…Diagnosis: glioma [24] LASSO was suitable for a relatively small data set and could avoid overfitting with a L1 regularization term [24,34,56]. Genetic status: non-small cell lung cancer [34] Side effect: xerostomia [46] LASSO method used numerous iterations to link the non-zero contributory parameters to the chosen prognosis [64].…”

“…Thus, the diagnostic efficiency was improved with the hybrid nomogram combing manual diagnosis. And, four radiomic features were extracted by LASSO method [24]. LASSO is suitable for a relatively small data set and can avoid overfitting.…”

Feature-based PET/MRI radiomics in patients with brain tumors

“…Wang et al used FDG PET, MET PET, and structural MRI images from 160 glioma patients for the development of a model to reliably diagnose tumor recurrence. 31 Before further processing, the patient cohort was divided into a training cohort ( n = 112) and a test cohort ( n = 48). The LASSO regression model was used for feature selection, and the model for predicting tumor recurrence was built using multivariable logistic regression analysis.…”