Dataset Information

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

ABSTRACT:

Background and purpose

MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels.

Materials and methods

Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquisition of sodium-MR images, we applied density-adapted 3D radial projection reconstruction pulse sequences. Proton-MR imaging data were acquired by using a 3T whole-body system.

Results

We demonstrated that the initial sodium signal of a treatment-naïve brain tumor is a significant predictor of isocitrate dehydrogenase (IDH) mutation status (P < .001). Moreover, independent of this correlation, the Cox proportional hazards model confirmed the sodium signal of treatment-naïve brain tumors as a predictor of progression (P = .003). Compared with the molecular signature of IDH mutation status, information criteria of model comparison revealed that the sodium signal is even superior to IDH in progression prediction. In addition, sodium-MR imaging provides a new approach to noninvasive tumor classification. The sodium signal of contrast-enhancing tumor portions facilitates differentiation among most glioma types (P < .001).

Conclusions

The information of sodium-MR imaging may help to classify neoplasias at an early stage, to reduce invasive tissue characterization such as stereotactic biopsy specimens, and overall to promote improved and individualized patient management in neuro-oncology by novel imaging signatures of brain tumors.

SUBMITTER: Biller A

PROVIDER: S-EPMC7960203 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Publications

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

Biller A A Badde S S Nagel A A Neumann J-O JO Wick W W Hertenstein A A Bendszus M M Sahm F F Benkhedah N N Kleesiek J J

AJNR. American journal of neuroradiology 20151022 1

<h4>Background and purpose</h4>MR imaging in neuro-oncology is challenging due to inherent ambiguities in proton signal behavior. Sodium-MR imaging may substantially contribute to the characterization of tumors because it reflects the functional status of the sodium-potassium pump and sodium channels.<h4>Materials and methods</h4>Sodium-MR imaging data of patients with treatment-naïve glioma WHO grades I-IV (n = 34; mean age, 51.29 ± 17.77 years) were acquired by using a 7T MR system. For acquis ...[more]

PMID: 26494691

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Project description:Background: Deep learning (DL) could predict isocitrate dehydrogenase (IDH) mutation status from MRIs. Yet, previous work focused on CNNs with refined tumor segmentation. To bridge the gap, this study aimed to evaluate the feasibility of developing a Transformer-based network to predict the IDH mutation status free of refined tumor segmentation. Methods: A total of 493 glioma patients were recruited from two independent institutions for model development (TCIA; N = 259) and external test (AHXZ; N = 234). IDH mutation status was predicted directly from T2 images with a Swin Transformer and conventional ResNet. Furthermore, to investigate the necessity of refined tumor segmentation, seven strategies for the model input image were explored: (i) whole tumor slice; (ii-iii) tumor mask and/or not edema; (iv-vii) tumor bounding box of 0.8, 1.0, 1.2, 1.5 times. Performance comparison was made among the networks of different architectures along with different image input strategies, using area under the curve (AUC) and accuracy (ACC). Finally, to further boost the performance, a hybrid model was built by incorporating the images with clinical features. Results: With the seven proposed input strategies, seven Swin Transformer models and seven ResNet models were built, respectively. Based on the seven Swin Transformer models, an averaged AUC of 0.965 (internal test) and 0.842 (external test) were achieved, outperforming 0.922 and 0.805 resulting from the seven ResNet models, respectively. When a bounding box of 1.0 times was used, Swin Transformer (AUC = 0.868, ACC = 80.7%), achieved the best results against the one that used tumor segmentation (Tumor + Edema, AUC = 0.862, ACC = 78.5%). The hybrid model that integrated age and location features into images yielded improved performance (AUC = 0.878, Accuracy = 82.0%) over the model that used images only. Conclusions: Swin Transformer outperforms the CNN-based ResNet in IDH prediction. Using bounding box input images benefits the DL networks in IDH prediction and makes the IDH prediction free of refined glioma segmentation feasible.

Project description:PurposeThis prospective clinical trial investigated sodium (23Na) MRI at 7 Tesla (T) field strength as biomarker for tumor extent, isocitrate dehydrogenase (IDH) mutation and O6-methylguanine DNA methyltransferase (MGMT) promotor methylation in glioma patients.Methods28 glioma patients underwent 23Na MRI on a 7T scanner (Siemens Healthcare, Erlangen, Germany) parallel to standard 3T MRI before chemoradiation. Areas of Gadolinium-contrast enhancement (gdce), non-enhancing T2-hyperintensity (regarded as edema), necrosis, and normal-appearing white matter (nawm) were segmented on 3T MRI imaging and were co-registered with the 23Na images. The median total 23Na concentrations of all areas were compared by pairwise t-tests. Furthermore, areas of gdce and edema were merged to yield the whole tumor area without necrosis. Subsequently, the difference in median of the 23Na concentration of this whole tumor area was compared between IDH-mutated and IDH wild-type gliomas as well as MGMT methylated and MGMT not-methylated glioblastomas using Whitney-Mann U-tests. All p-values were corrected after the Bonferroni-Holm procedure.ResultsThe 23Na concentration increased successively from nawm to necrotic areas (mean ± sd: nawm = 37.84 ± 5.87 mM, edema = 54.69 ± 10.64 mM, gdce = 61.72 ± 12.95 mM, necrosis = 81.88 ± 17.53 mM) and the concentrations differed statistically significantly between all regarded areas (adjusted p-values for all pairwise comparisons < 0.05). Furthermore, IDH-mutated gliomas showed significantly higher 23Na concentrations than IDH wild-type gliomas (median [interquartile range]: IDH wild-type = 52.37 mM [45.98 - 58.56 mM], IDH mutated = 65.02 mM [58.87-67.05 mM], p = 0.039). Among the glioblastomas, there was a trend towards increased 23Na concentration in MGMT methylated tumors that did not reach statistical significance (median [interquartile range]: MGMT methylated = 57.59 mM [50.70 - 59.17 mM], MGMT not methylated = 48.78 mM [45.88 - 53.91 mM], p = 1.0).Conclusions23Na MRI correlates with the IDH mutation status and could therefore enhance image guidance towards biopsy sites as wells as image-guided surgery and radiotherapy. Furthermore, the successive decrease of 23Na concentration from central necrosis to normal-appearing white matter suggests a correlation with tumor infiltration.

Project description:BackgroundSurgical tumor resection is the primary treatment option for diffuse glioma, the most common malignant brain cancer. The intraoperative diagnosis of gliomas from tumor core samples can be improved by use of molecular diagnostics. Further, residual tumor at surgical margins is a primary cause of tumor recurrence and malignant progression. This study evaluates a desorption electrospray ionization mass spectrometry (DESI-MS) system for intraoperative isocitrate dehydrogenase (IDH) mutation assessment, estimation of tumor cell infiltration as tumor cell percentage (TCP), and disease status. This information could be used to enhance the extent of safe resection and so potentially improve patient outcomes.MethodsA mobile DESI-MS instrument was modified and used in neurosurgical operating rooms (ORs) on a cohort of 49 human subjects undergoing craniotomy with tumor resection for suspected diffuse glioma. Small tissue biopsies (ntotal = 203) from the tumor core and surgical margins were analyzed by DESI-MS in the OR and classified using univariate and multivariate statistical methods.ResultsAssessment of IDH mutation status using DESI-MS/MS to measure 2-hydroxyglutarate (2-HG) ion intensities from tumor cores yielded a sensitivity, specificity, and overall diagnostic accuracy of 89, 100, and 94%, respectively (ncore = 71). Assessment of TCP (categorized as low or high) in tumor margin and core biopsies using N-acetyl-aspartic acid (NAA) intensity provided a sensitivity, specificity, and accuracy of 91, 76, and 83%, respectively (ntotal = 203). TCP assessment using lipid profile deconvolution provided sensitivity, specificity, and accuracy of 76, 85, and 81%, respectively (ntotal = 203). Combining the experimental data and using PCA-LDA predictions of disease status, the sensitivity, specificity, and accuracy in predicting disease status are 63%, 83%, and 74%, respectively (ntotal = 203).ConclusionsThe DESI-MS system allowed for identification of IDH mutation status, glioma diagnosis, and estimation of tumor cell infiltration intraoperatively in a large human glioma cohort. This methodology should be further refined for clinical diagnostic applications.

Project description:PurposeIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status are important for managing glioma patients. However, current practice dictates invasive tissue sampling for histomolecular classification. We investigated the current value of dynamic susceptibility contrast (DSC) MR perfusion imaging as a tool for the non-invasive identification of these biomarkers.MethodsA systematic search of PubMed, Medline, and Embase up to 2023 was performed, and meta-analyses were conducted. We removed studies employing machine learning models or using multiparametric imaging. We used random-effects standardized mean difference (SMD) and bivariate sensitivity-specificity meta-analyses, calculated the area under the hierarchical summary receiver operating characteristic curve (AUC) and performed meta-regressions using technical acquisition parameters (e.g., time to echo [TE], repetition time [TR]) as moderators to explore sources of heterogeneity. For all estimates, 95% confidence intervals (CIs) are provided.ResultsSixteen eligible manuscripts comprising 1819 patients were included in the quantitative analyses. IDH mutant (IDHm) gliomas had lower rCBV values compared to their wild-type (IDHwt) counterparts. The highest SMD was observed for rCBVmean, rCBVmax, and rCBV 75th percentile (SMD≈ - 0.8, 95% CI ≈ [- 1.2, - 0.5]). In meta-regression, shorter TEs, shorter TRs, and smaller slice thicknesses were linked to higher absolute SMDs. When discriminating IDHm from IDHwt, the highest pooled specificity was observed for rCBVmean (82% [72, 89]), and the highest pooled sensitivity (i.e., 92% [86, 93]) and AUC (i.e., 0.91) for rCBV 10th percentile. In the bivariate meta-regression, shorter TEs and smaller slice gaps were linked to higher pooled sensitivities. In IDHm, 1p19q codeletion was associated with higher rCBVmean (SMD = 0.9 [0.2, 1.5]) and rCBV 90th percentile (SMD = 0.9 [0.1, 1.7]) values.ConclusionsIdentification of vascular signatures predictive of IDH and 1p19q status is a novel promising application of DSC perfusion. Standardization of acquisition protocols and post-processing of DSC perfusion maps are warranted before widespread use in clinical practice.

Dataset Information

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

Background and purpose

Materials and methods

Results

Conclusions

Publications

Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression.

Similar Datasets

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