Resection and survival data from a clinical trial of glioblastoma multiforme-specific IRDye800-BBN fluorescence-guided surgery.
Bioengineering & translational medicine
Supra-maximum surgical tumor resection without neurological damage is highly valuable for treatment and prognosis of patients with glioblastoma multiforme (GBM). We developed a GBM-specific fluorescence probe using IRDye800CW (peak absorption/emission, 778/795 nm) and bombesin (BBN), which (IRDye800-BBN) targets the gastrin-releasing peptide receptor, and evaluated the image-guided resection efficiency, sensitivity, specificity, and survivability. Twenty-nine patients with newly diagnosed GBM were enrolled. Sixteen hours preoperatively, IRDye800-BBN (1 mg in 20 ml sterile water) was intravenously administered. A customized fluorescence surgical navigation system was used intraoperatively. Postoperatively, enhanced magnetic resonance images were used to assess the residual tumor volume, calculate the resection extent, and confirm whether complete resection was achieved. Tumor tissues and nonfluorescent brain tissue in adjacent noneloquent boundary areas were harvested and assessed for diagnostic accuracy. Complete resection was achieved in 82.76% of patients. The median extent of resection was 100% (range, 90.6-100%). Eighty-nine samples were harvested, including 70 fluorescence-positive and 19 fluorescence-negative samples. The sensitivity and specificity of IRDye800-BBN were 94.44% (95% CI, 85.65-98.21%) and 88.24% (95% CI, 62.25-97.94%), respectively. Twenty-five patients were followed up (median, 13.5 [3.1-36.0] months), and 14 had died. The mean preoperative and immediate and 6-month postoperative Karnofsky performance scores were 77.9 ± 11.8, 71.3 ± 19.2, and 82.6 ± 14.7, respectively. The median overall and progression-free survival were 23.1 and 14.1 months, respectively. In conclusion, GBM-specific fluorescent IRDye800-BBN can help neurosurgeons identify the tumor boundary with sensitivity and specificity, and may improve survival outcomes.
10.1002/btm2.10182
Diagnostic value of glutamate with 2-hydroxyglutarate in magnetic resonance spectroscopy for IDH1 mutant glioma.
Nagashima Hiroaki,Tanaka Kazuhiro,Sasayama Takashi,Irino Yasuhiro,Sato Naoko,Takeuchi Yukiko,Kyotani Katsusuke,Mukasa Akitake,Mizukawa Katsu,Sakata Junichi,Yamamoto Yusuke,Hosoda Kohkichi,Itoh Tomoo,Sasaki Ryohei,Kohmura Eiji
Neuro-oncology
BACKGROUND:Mutations in the isocitrate dehydrogenase 1 (IDH1) gene that are frequently observed in low-grade glioma are strongly associated with the accumulation of 2-hydroxyglutarate (2HG), which is a valuable diagnostic and prognostic biomarker of IDH1 mutant glioma. However, conventional MR spectroscopy (MRS)-based noninvasive detection of 2HG is challenging. In this study, we aimed to determine the additional value of other metabolites in predicting IDH1 mutations with conventional MRS. METHODS:Forty-seven patients with glioma underwent conventional single voxel short echo time MRS prior to surgery. A stereotactic navigation-guided operation was performed to resect tumor tissues in the center of the MRS voxel. MRS-based measurements of metabolites were validated with gas chromatography-mass spectrometry. We also conducted integrated analyses of glioma cell lines and clinical samples to examine the other metabolite levels and molecular findings in IDH1 mutant gliomas. RESULTS:A metabolomic analysis demonstrated higher levels of 2HG in IDH1 mutant glioma cells and surgical tissues. Interestingly, glutamate levels were significantly decreased in IDH1 mutant gliomas. Through an analysis of metabolic enzyme genes in glutamine pathways, it was shown that the expressions of branched-chain amino acid transaminase 1 were reduced and glutamate dehydrogenase levels were elevated in IDH1 mutant gliomas. Conventional MRS detection of glutamate and 2HG resulted in a high diagnostic accuracy (sensitivity 72%, specificity 96%) for IDH1 mutant glioma. CONCLUSIONS:IDH1 mutations alter glutamate metabolism. Combining glutamate levels optimizes the 2HG-based monitoring of IDH1 mutations via MRS and represents a reliable clinical application for diagnosing IDH1 mutant gliomas.
10.1093/neuonc/now090
2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas.
Choi Changho,Ganji Sandeep K,DeBerardinis Ralph J,Hatanpaa Kimmo J,Rakheja Dinesh,Kovacs Zoltan,Yang Xiao-Li,Mashimo Tomoyuki,Raisanen Jack M,Marin-Valencia Isaac,Pascual Juan M,Madden Christopher J,Mickey Bruce E,Malloy Craig R,Bachoo Robert M,Maher Elizabeth A
Nature medicine
Mutations in isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) have been shown to be present in most World Health Organization grade 2 and grade 3 gliomas in adults. These mutations are associated with the accumulation of 2-hydroxyglutarate (2HG) in the tumor. Here we report the noninvasive detection of 2HG by proton magnetic resonance spectroscopy (MRS). We developed and optimized the pulse sequence with numerical and phantom analyses for 2HG detection, and we estimated the concentrations of 2HG using spectral fitting in the tumors of 30 subjects. Detection of 2HG correlated with mutations in IDH1 or IDH2 and with increased levels of D-2HG by mass spectrometry of the resected tumors. Noninvasive detection of 2HG may prove to be a valuable diagnostic and prognostic biomarker.
10.1038/nm.2682
Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging.
Tobisch Alexandra,Schultz Thomas,Stirnberg Rüdiger,Varela-Mattatall Gabriel,Knutsson Hans,Irarrázaval Pablo,Stöcker Tony
NMR in biomedicine
Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.
10.1002/nbm.4055
Preoperative Two-Dimensional Size of Glioblastoma is Associated with Patient Survival.
Leu Severina,Boulay Jean-Louis,Thommen Sarah,Bucher Heiner C,Stippich Christoph,Mariani Luigi,Bink Andrea
World neurosurgery
BACKGROUND:Although tumor size affects survival of patients with lower-grade glioma, a prognostic effect on patients with glioblastoma remains to be established. METHODS:We performed a retrospective analysis of 61 patients using volumetric data of tumor compartments of 61 patients obtained by preoperative magnetic resonance images using the visual ABC/2 method. Preoperative enhancing, nonenhancing, necrosis, and edema volume, the preoperative tumor area (TA) as a product of the 2 largest tumor diameters perpendicular to each other on axial T1-weighted postcontrast images, as well as postoperative enhancing residual volumes, were measured. Multivariable Cox proportional hazard models were used to associate these parameters with overall survival, adjusting for potential confounders. RESULTS:The median preoperative enhancing tumor volume was 18.2 mL (interquartile range, 8.2-41.7 mL); the median remnant tumor volume was 1.3% (interquartile range, 0.0%-42.9%). During follow-up, 59 patients (92%) died; median survival time and median follow-up time were both 404 days. We found a statistically significant multiplicative effect of TA on survival: the hazard ratio (HR) was increased by 1.096 per unit increase of 200 mm (95% confidence interval [CI], 1.027-1.170; P < 0.01). The effect of remnant tumor on HR increased multiplicatively by 1.013 (95% CI, 1.001-1.026; P = 0.04) per unit increase of 1 log (day) and 1% in tumor remnant. HR associated with age at surgery increased by 1.503 per 5 years of age (95% CI, 1.243-1.817; P < 0.01). CONCLUSIONS:Preoperative TA proved to be the only glioblastoma size parameter that affects patient survival.
10.1016/j.wneu.2018.04.067
Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.
Choi Kyu Sung,Choi Seung Hong,Jeong Bumseok
Neuro-oncology
BACKGROUND:The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI. METHODS:Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2* susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model. RESULTS:The IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2* susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype. CONCLUSIONS:We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.
10.1093/neuonc/noz095