Discrimination between low-grade oligodendrogliomas and diffuse astrocytoma with the aid of 11C-methionine positron emission tomography

Purpose The purpose of this prospective study was to determine whether chemical shift gradient-echo magnetic resonance imaging (MRI) could predict glioma grade. Materials and methods A total of 69 patients with 69 gliomas were prospectively included. There were 41 men and 28 women with a mean age of 50 ± (SD) years (range: 16–82 years). All patients had MRI of the brain including chemical shift gradient-echo sequence, further referred to as in- and out-of phase sequence (IP/OP). Intravoxel fat content was estimated by signal loss ratio (SLR = [IP-OP]/2IP), between in- and out-of-phase images, using a region of interest placed on the viable portion of the gliomas. Association between SLR and glioma grade was searched for using Wilcoxon and Mann–Whitney U tests and diagnostic capabilities using area under the receiver operating characteristic (AUROC) curves. Results A significant association was found between SLR value and glioma grade (P   9‰ allowed complete discrimination between grade III and grade II glioma with 100% specificity (95% CI: 85–100%), 100% sensitivity (95% CI: 78–100%) and 100% accuracy (95% CI: 90–100%) (AUROC = 1). A SLR > 20‰ allowed discriminating between grade IV and grade III glioma with 75% specificity (95% CI: 57–89%), 73% sensitivity (95% CI: 45–92%) and 72% accuracy (95% CI: 57–84%) (AUC = 0.825, 95% CI: 0.702–0.948). The AUROC for the diagnosis of high-grade glioma (grade III and IV vs. grade II) was 1. Conclusion Chemical shift gradient echo MRI provides accurate grading of gliomas. This simple method should be used as a biomarker to predict glioma grade.

Glioma-grade diagnosis using in-phase and out-of-phase T1-weighted magnetic resonance imaging: A prospective study.

Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.

/pdf/advanced-diagnosis-of-glioma-by-using-emerging-magnetic-5tmehvm5cg.pdf

Advanced Diagnosis of Glioma by Using Emerging Magnetic Resonance Sequences.

OBJECTIVE This study aimed to determine whether inflow-based vascular-space-occupancy (iVASO) MRI could reproducibly quantify skeletal muscle perfusion and differentiate patients with dermatomyositis (DM) from healthy subjects MATERIALS AND METHODS A total of 25 patients with DM and 22 healthy volunteers underwent iVASO MRI in a 3-T MRI scanner Maximum and mean arteriolar muscle blood volume (MBV) values of four subgroups of muscles (normal muscles, morphologically normal-appearing muscles, edematous muscles, and atrophic or fat-infiltrated muscles) were obtained Maximum and mean arteriolar MBV values were compared among the different subgroups, and repeat testing was performed in 20 subjects to assess reproducibility RESULTS Compared with normal muscles in healthy subjects, morphologically normal-appearing muscles, edematous muscles, and atrophic or fat-infiltrated muscles in patients with DM showed a significant decrease of both maximum and mean arteriolar MBV (p < 001) Both parameters were significantly lower in atrophic or fat-infiltrated muscles than in morphologically normal-appearing and edematous muscles (p < 001) ROC AUCs for discriminating patients with DM from healthy volunteers were 0842 and 0812 for maximum and mean arteriolar MBV values, respectively As a measure of test-retest studies, the intraclass correlation coefficients (ICCs) were 0990 (95% CI, 0986-0993) and 0990 (95% CI, 0987-0993) for maximum and mean arteriolar MBV, respectively For interobserver reproducibility, the ICCs were 0989 (95% CI, 0986-0991) and 0980 (95% CI, 0975-0983), respectively CONCLUSION iVASO MRI can reproducibly quantify arteriolar MBV in the thigh and discriminate between healthy volunteers and patients with DM

Decreased Muscular Perfusion in Dermatomyositis: Initial Results Detected by Inflow-Based Vascular-Space-Occupancy MRI.

Differential detection of metastatic and inflammatory lymph nodes using inflow-based vascular-space-occupancy (iVASO) MR imaging.

BACKGROUND Inflow-based vascular-space-occupancy (iVASO) MRI is a noninvasive perfusion technique that does not require administration of exogenous contrast agents. Arteriolar cerebral blood volume (CBVa) obtained from iVASO MRI is hypothesized to be an indicator of tumor microvasculature. PURPOSE To assess the diagnostic performance of iVASO MRI implemented at 3T in predicting histologic grades of cerebral gliomas. STUDY TYPE Retrospective. SUBJECTS Forty-five patients (31 males) consisting of 14 WHO grade IV glioblastoma multiformes, 14 grade III, and 17 grade II gliomas. FIELD STRENGTH/SEQUENCE At 3T we acquired CBVa data using an iVASO sequence. ASSESSMENT The maximum and mean CBVa (CBVa_max and CBVa_mean) values were calculated in the tumor and normalized to the contralateral thalamus (nCBVa_max and nCBVa_mean). STATISTICAL TESTS Kruskal-Wallis test, Mann-Whitney test, and receiver operating characteristics (ROC) curve were used for statistical analysis. RESULTS Both CBVa_max and nCBVa_max increased with tumor grade (P 0.78 ml / 100 ml and nCBVa_max >1.20 in 13/14 cases, and CBVa_max 2.06 ml / 100 ml in 9/14 cases and nCBVa_max >2.33 in 13/14 cases. The areas under the ROC curve, sensitivity, and specificity were 0.839 (P < 0.001), 92.9% (26/28), and 64.7% (11/17) for CBVa_max, and 0.883 (P < 0.001), 92.9% (26/28), and 70.6% (12/17) for nCBVa_max in the discrimination between grade II and high-grade (grade III and grade IV) tumors, respectively. DATA CONCLUSION: iVASO MRI might be used to help determine and predict glioma grade. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1817-1823.

Association of Glioma Grading With Inflow‐Based Vascular‐Space‐Occupancy MRI: A Preliminary Study at 3T

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Association of Glioma Grading With Inflow‐Based Vascular‐Space‐Occupancy MRI: A Preliminary Study at 3T