Diagnostic accuracy of proton magnetic resonance spectroscopy and perfusion-weighted imaging in brain gliomas follow-up: a single institutional experience

Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression

PURPOSE

To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas.

PATIENTS AND METHODS

One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR_mean, TBR_max) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR_mean, TBR_max, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis.

RESULTS

Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR_mean and TBR_max resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%).

CONCLUSION

The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Changes in metabolites in the brain of patients with fibromyalgia after treatment with an NMDA receptor antagonist

The aims of this work were to evaluate whether the treatment of patients with fibromyalgia with memantine is associated with significant changes in metabolite concentrations in the brain, and to explore any changes in clinical outcome measures. Magnetic resonance spectroscopy was performed of the right anterior and posterior insula, both hippocampi and the posterior cingulate cortex. Questionnaires on pain, anxiety, depression, global function, quality of life and cognitive impairment were used. Ten patients were studied at baseline and after three months of treatment with memantine. Significant increases were observed in the following areas: N-acetylaspartate (4.47 at baseline vs. 4.71 at three months, p = 0.02) and N-acetylaspartate+N-acetylaspartate glutamate in the left hippocampus (5.89 vs. 5.98; p = 0.007); N-acetylaspartate+N-acetylaspartate glutamate in the right hippocampus (5.31 vs 5.79; p = 0.01) and the anterior insula (7.56 vs. 7.70; p = 0.033); glutamate+glutamine/creatine ratio in the anterior insula (2.03 vs. 2.17; p = 0.022) and the posterior insula (1.77 vs. 2.00; p = 0.004); choline/creatine ratio in the posterior cingulate (0.18 vs. 0.19; p = 0.023); and creatine in the right hippocampus (3.60 vs. 3.85; p = 0.007). At the three-month follow-up, memantine improved cognitive function assessed by the Cognition Mini-Exam (31.50, SD = 2.95 vs. 34.40, SD = 0.6; p = 0.005), depression measured by the Hamilton Depression Scale (7.70, SD = 0.81 vs. 7.56, SD = 0.68; p = 0.042) and severity of illness measured by the Clinical Global Impression severity scale (5.79, SD = 0.96 vs. 5.31, SD = 1.12; p = 0.007). Depression, clinical global impression and cognitive function showed improvement with memantine. Magnetic resonance spectroscopy could be useful in monitoring response to the pharmacological treatment of fibromyalgia.

Brainstem glioma: Prediction of histopathologic grade based on conventional MR imaging

Objective The objective of this article is to investigate the association between specific MR imaging findings and histopathologic grading (low-grade vs. high-grade) of brainstem gliomas (BSGs). Methods Sixty-two males and 34 females (mean (standard deviation, SD) age of 24.61 (17.20) years, range = 3 to 70 years) with histologically diagnosed BSG underwent conventional 1.5 T MR imaging, which included T1-weighted (T1W), T2W, and post-contrast T1W sequences. There were 39 children (mean age of 9.38 years) and 57 adults (mean age of 35 years). A binary logistic regression analysis was used to explore associations between MRI features and histopathological grade of the BSG. Results Binary logistic regression revealed that necrosis (adjusted odds ratio (OR) = 16.07; 95% confidence interval (CI) = 3.20 to 80.52; p = 0.001) and inhomogeneous contrast enhancement (adjusted OR = 8.04; 95% CI = 1.73 to 37.41; p = 0.008) as significant predictors of high-grade BSG. The equation (Nagelkerke R²= 0.575) is Logit ( p high-grade BSG) = (2.77 × necrosis) + (2.08 × heterogeneous contrast enhancement) - 3.13. Sensitivity and specificity values were respectively 66.7% and 96.0% for necrosis and 85.7% and 65.9% for inhomogeneous contrast-enhancing lesions. In the pediatric age group, only inhomogeneous contrast enhancement (adjusted OR = 40; 95% CI = 3.95 to 445.73; p = 0.002) was a significant predictor for high-grade BSG. Conclusion Conventional MR imaging features such as necrosis and inhomogeneous contrast enhancement in adults and heterogeneous contrast enhancement in children suggest high-grade BSG.