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Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most malignant primary tumor in the brain, with poor prognosis and limited effective therapies. Although Bevacizumab (BEV) has shown promise in extending progression-free survival (PFS) treating GBM, there is no evidence for its ability to prolong overall survival (OS). Given the uncertainty surrounding BEV treatment strategies, we aimed to provide an evidence map associated with BEV therapy for recurrent GBM (rGBM). Methods: PubMed, Embase, and the Cochrane Library were searched for the period from January 1, 1970, to March 1, 2022, for studies reporting the prognoses of patients with rGBM receiving BEV. The primary endpoints were overall survival (OS) and quality of life (QoL). The secondary endpoints were PFS, steroid use reduction, and risk of adverse effects. A scoping review and an evidence map were conducted to explore the optimal BEV treatment (including combination regimen, dosage, and window of opportunity). Results: Patients with rGBM could gain benefits in PFS, palliative, and cognitive advantages from BEV treatment, although the OS benefits could not be verified with high-quality evidence. Furthermore, BEV combined therapy (especially with lomustine and radiotherapy) showed higher efficacy than BEV monotherapy in the survival of patients with rGBM. Specific molecular alterations (IDH mutation status) and clinical features (large tumor burden and double-positive sign) could predict better responses to BEV administration. A low dosage of BEV showed equal efficacy to the recommended dose, but the optimal opportunity window for BEV administration remains unclear. Conclusions: Although OS benefits from BEV-containing regimens could not be verified in this scoping review, the PFS benefits and side effects control supported BEV application in rGBM. Combining BEV with novel treatments like tumor-treating field (TTF) and administration at first recurrence may optimize the therapeutic efficacy. rGBM with a low apparent diffusion coefficient (ADCL), large tumor burden, or IDH mutation is more likely to benefit from BEV treatment. High-quality studies are warranted to explore the combination modality and identify BEV-response subpopulations to maximize benefits. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas. Methods: Fifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models. Results: The AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973). Conclusion: The predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment. [ABSTRACT FROM AUTHOR]