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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 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. 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 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. CONCLUSIONS:Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.

SUBMITTER: Wang K

PROVIDER: S-EPMC7188738 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Wang Kai K Qiao Zhen Z Zhao Xiaobin X Li Xiaotong X Wang Xin X Wu Tingfan T Chen Zhongwei Z Fan Di D Chen Qian Q Ai Lin L

European journal of nuclear medicine and molecular imaging 20191126 6

<h4>Purpose</h4>To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients.<h4>Methods</h4>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 <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG) positron emission tomography (PET), <sup>11</sup>C-methionine (<sup>11</sup>C-MET) PET, and magnetic r ...[more]

PMID: 31773234

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Project description:BackgroundThe radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients.ObjectiveThis research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans.MethodsIn this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons.ResultsDNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy).ConclusionsDeep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

Dataset Information

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

Publications

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

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