Prediction of genetic subgroups in adult supra tentorial gliomas by pre- and intraoperative parameters

Brainstem Gliomas With Isocitrate Dehydrogenase Mutation: Natural History, Clinical-Radiological Features, Management Strategy, and Long-Term Outcome

BACKGROUND AND OBJECTIVES

This study aimed to investigate the clinical, radiological, pathological features, treatment options, and outcomes of isocitrate dehydrogenase (IDH)-mutant brainstem gliomas (BSG-IDH mut ).

METHODS

A retrospective analysis of 22 patients diagnosed with BSG-IDH mut and treated at our institution from January 2011 to January 2017 was performed. Their clinical, radiological data, and long-term outcomes were collected and analyzed.

RESULTS

The median age of patients was 38.5 years, with a male predominance (63.6%). All patients had IDH1 and TP53 mutations, with noncanonical IDH mutations in 59.1% of cases, 06-methylguanine-DNA methyltransferase promoter methylation in 55.6%, and alpha-thalassemia mental retardation X-linked loss in 63.2%, respectively. Tumors were primarily located in the pontine-medullary oblongata (54.5%) and frequently involved the pontine brachium (50%). Most tumors exhibited ill-defined boundaries (68.2%), no T2-FLAIR mismatch (100%), and no contrast enhancement (86.3%). Two radiological growth patterns were also identified: focal and extensively infiltrative, which were associated with the treatment strategy when tumor recurred. Seven patients (31.8%) received surgery only and 15 (68.2%) surgery plus other therapy. The median overall survival was 124.8 months, with 1-year, 2-year, 5-year, and 10-year survival rates of 81.8%, 68.2%, 54.5%, and 13.6%, respectively. Six patients experienced tumor recurrence, and all retained their radiological growth patterns, with 2 transformed into central nervous system World Health Organization grade 4.

CONCLUSION

BSG-IDH mut represents a unique subgroup of brainstem gliomas with distinctive features and more favorable prognosis compared with other brainstem gliomas. Further research is required to better understand the molecular mechanisms and optimize treatment strategies for this rare and complex disease.

Probe-guided endoscopic system for 5-aminolevulinic acid-based photodynamic diagnosis in cholangiocarcinoma

BACKGROUND AND AIM

The diagnostic accuracy for cholangiocarcinoma (CCA) is inadequate, necessitating the exploration of novel diagnostic approaches. Protoporphyrin IX (Pp IX), a metabolic product of 5-aminolevulinic acid (5-ALA), emits red fluorescence upon blue light exposure. Because it accumulates selectively in cancer cells, photodynamic diagnosis using 5-ALA (5-ALA-PDD) has been integrated into clinical practice for diverse cancer types. Nevertheless, there is currently no device capable of capturing Pp IX-derived fluorescence for real-time 5-ALA-PDD within the biliary tract, largely due to challenges in device miniaturization.

METHODS

To investigate the feasibility of real-time 5ALA-PDD in CCA, we developed two essential components of the cholangioscopy system: a small-diameter flexible camera and a light guide for emitting blue light. We evaluated the detectability of Pp IX fluorescence using these devices in experimental gels and animal models.

RESULTS

Our camera and light guide were smoothly inserted into the lumen of existing cholangioscopes. Incorporating a long-pass filter at the camera tip enabled efficient detection of red fluorescence without significantly impacting white-light observation. The integration of these devices facilitated clear visualization of red fluorescence from gels containing Pp IX at concentrations of 5 μM or higher. Additionally, when observing subcutaneous human CCA tumor models in nude mice treated with 5-ALA, we successfully demonstrated distinct red fluorescence from Pp IX accumulation in tumors compared to peritumoral subcutaneous areas.

CONCLUSION

The integration of our device combination holds promise for real-time 5-ALA-PDD in human CCA, potentially enhancing the diagnostic accuracy for this complex condition.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Applied Fence-Post Techniques Using Deep Electrodes Instead of Catheters for Resection of Glioma Complicated with Frequent Epileptic Seizures: A Case Report

Fence-post catheter techniques are used to use tumor margins when resecting gliomas. In the present study, deep electrodes instead of catheters were used as fence-posts. The case of a 25-year-old female patient whose magnetic resonance images (MRI) revealed a tumor in the left cingulate gyrus is presented in this study. She underwent daily seizures without loss of consciousness under the administration of anti-seizure medications. Despite video electroencephalography (EEG) monitoring, the scalp inter-ictal EEG did not show obvious epileptiform discharges. We were consequently uncertain whether such frequent seizures were epileptic seizures or not. As a result, deep electrodes were used as fence-posts: three deep electrodes were inserted into the tumor’s anterior, lateral, and posterior margins using a navigation-guided method. The highest epileptic discharge was detected from the anterior deep electrode. As a result, ahead of the tumor was extendedly resected, and epileptic discharges were eliminated using EEG. The postoperative MRI revealed that the tumor was resected. The patient has never experienced seizures after the surgery. In conclusion, when supratentorial gliomas complicated by frequent seizures are resected, intraoperative EEG monitoring using deep electrodes as fence-posts is useful for estimating epileptogenic areas.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Detection improvement of gliomas in hyperspectral imaging of protoporphyrin IX fluorescence - in vitro comparison of visual identification and machine thresholds

BACKGROUND

5-aminolevulinic acid (5-ALA) - precursor of protoporphyrin IX (PpIX) - is utilized in fluorescence guided surgery (FGS) of high-grade gliomas. PpIX is used to identify traces of glioma during resection. Visual inspection of the fluorescence seems inaccurate in comparison to optic techniques such as hyperspectral imaging (HSI).

AIM

To characterize the limits of PpIX fluorescence detection of (i) visual evaluation and (ii) HSI analysis and to (iii) develop a classification system for visible and non-visible PpIX fluorescence.

METHODS

Samples with increasing concentrations (C) of PpIX and non-fluorescent controls were evaluated using a surgical microscope under blue light illumination. Similar samples were imaged with a HSI system tuned to PpIX fluorescence peak wavelength (635 nm) and control (RGB) channels. Samples' intensities were defined, leading to 96 analysed pixels after batching.

RESULTS

Three expert neurosurgeons assessed the PpIX samples (n = 16) and controls (n = 8) with unanimous decisions (ICC = 0.704), resulting in 63% recognition rate, 48% sensitivity, 92% specificity, 92% positive predictive value (PPV) and 47% negative predictive value (NPV). HSI image analysis, comparing mean relative values, resulted in 96%, 100%, 86%, 94%, 100%, respectively. Minimum PpIX concentration detection for experts was 0.6-1.8 μmol/l and HSI's 0.03-0.15 μmol/l.

CONCLUSIONS

PpIX concentrations of low-grade gliomas, and those reported on glioblastoma infiltration zones, are below experts' detection threshold. HSI analysis exceeds the performance of expert's visual inspection nearly by 20-fold. Hybrid FGS-HSI systems should be investigated in parallel to long-term outcomes. Described methods are applicable as a standard for calibration, testing and development of subvisual FGS techniques.

High Myoinositol on Proton MR Spectroscopy Could Be a Potential Signature of Papillary Tumors of the Pineal Region-Case Report of Two Patients

Papillary tumor of the pineal region (PTPR) is an uncommon entity in which a presurgical suspicion may be crucial for patient management. Maximal safe neurosurgical resection is of choice when PTPR is suspected, whereas non-surgical approaches can be considered in other tumors of the pineal region, such as pineocytoma or concrete subtypes of germ-cell tumors. In general terms, imaging features of tumors of the pineal region have been reported to be unspecific. Nevertheless, in this report, we describe two pathology-confirmed PTPRs in which presurgical proton MR spectroscopy demonstrated extremely high myoinositol, a pattern which drastically differs from that of other pineal tumors. We hypothesize that this high myoinositol may be related to PTPR’s known ependymal component, and that it could be used as a specific non-invasive diagnostic signature.

Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics

Background and Purpose

Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas.

Methods

Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDH^mut vs. IDH^wt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis.

Results

Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%.

Conclusion

MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.

Magnetic resonance imaging criteria for prediction of isocitrate dehydrogenase (IDH) mutation status in patients with grade II-III astrocytoma and oligodendroglioma

BACKGROUND

IDH mutation status is an important prognostic marker for glial tumors, which is detected immunohistochemically after surgery. Since this method is invasive, easy and noninvasive magnetic resonance imaging (MRI) methods have recently been used in predicting the IDH mutation status. However, there is currently no standard MRI technique to predict IDH mutation. We analyzed the value of conventional MRI to predict IDH mutation and its effect on survival among grade II-III astrocytoma and oligodendroglioma patients.

MATERIAL AND METHODS

We included WHO grade II-III astrocytoma and oligodendroglioma patients who underwent surgery at Bahcesehir University Goztepe Medical Park Hospital. All patients were analyzed according to their immunohistochemical IDH mutation status. Preoperative conventional MRI studies with respect to their location, diffusion restriction, contrast enhancement, calcification and hemorrhage on susceptibility-weighted image (SWI) or T2*- weighted imaging (T2*WI), and T2 -FLAIR mismatch properties were retrospectively assessed by a neuroradiologist. The relation between MRI characteristics and IDH mutation was analyzed using a chi-square test. The sensitivity and specificity of radiological IDH mutation were determined by ROC analysis. The impact of IDH mutation on survival was also analyzed by Kaplan-Meier tests.

RESULTS

IDH mutation was found to be positive in 82.5% of tumors histopathologically and 54.4% radiologically. The sensitivity and specificity were 63.8% and 90%, respectively (Area under the curve/AUC = 0.369, p = 0.08). IDH wild gliomas were predominantly diffusion-restricted tumors. IDH mutant tumors were less likely to have contrast enhancement and had lower grades compared to the IDH wild tumors. The median survival time could not be reached and the overall survival was not related to any tumor characteristics or IDH mutation.

CONCLUSIONS

Conventional MRI predicts IDH-mutation status in Grade II-III astrocytoma and oligodendroglioma. Contrast-enhancement and restricted diffusion were strongly associated with grade III astrocytoma and oligodendroglioma, IDH-wild type. Location, T2-FLAIR mismatch, and SWI did not contribute to making a decision on the IDH mutation status. There was no significant difference between the survival times of patients and their IDH status.

Myoinositol to Total Choline Ratio in Glioblastomas as a Potential Prognostic Factor in Preoperative Magnetic Resonance Spectroscopy

Isocitrate dehydrogenase (IDH) wild-type diffuse astrocytic tumors tend to be pathologically diagnosed as glioblastomas (GBMs). We previously reported that myoinositol to total choline (Ins/Cho) ratio in GBMs on magnetic resonance (MR) spectroscopy was significantly lower than that in IDH-mutant gliomas. We then hypothesized that a low Ins/Cho ratio is a poor prognosis factor in patients with GBMs, IDH-wild-type. In the present study, we calculated the Ins/Cho ratios of patients with GBMs and investigated their progression-free survival (PFS) and overall survival (OS) to determine their utility as prognostic marker. We classified patients with GBMs harboring wild-type IDH (n = 27) into two groups based on the Ins/Cho ratio, and compared patient backgrounds, pathological findings, PFS, OS, and copy number aberrations between the high and low Ins/Cho groups. Patients with GBMs in the low Ins/Cho ratio group indicated shorter PFS (P = 0.021) and OS (P = 0.048) than those in the high Ins/Cho group. Multivariate analysis demonstrated that the Ins/Cho ratio was significantly correlated with PFS (hazard ratio 0.24, P = 0.028). In conclusion, the preoperative Ins/Cho ratio can be used as a novel potential prognostic factor for GBM, IDH-wild-type.

Association of preoperative seizures with tumor metabolites quantified by magnetic resonance spectroscopy in gliomas

Seizures are common in patients with gliomas; however, the mechanisms of epileptogenesis in gliomas have not been fully understood. This study hypothesized that analyzing quantified metabolites using magnetic resonance spectroscopy (MRS) might provide novel insights to better understand the epileptogenesis in gliomas, and specific metabolites might be indicators of preoperative seizures in gliomas. We retrospectively investigated patient information (gender, age at diagnosis of tumor, their survival time) and tumor information (location, histology, genetic features, and metabolites according to MRS) in patients with gliomas. The data were correlated with the incidence of seizure and analyzed statistically. Of 146 adult supratentorial gliomas, isocitrate dehydrogenase (IDH) mutant tumors significantly indicated higher incidence of preoperative seizures than IDH wild-type gliomas. However, MRS study indicated that glutamate concentration in IDH wild-type gliomas was higher than that in IDH mutant gliomas. Glutamate was not associated with high frequency of preoperative seizures in patients with gliomas. Instead, increased total N-acetyl-L-aspartate (tNAA) was significantly associated with them. Moreover, multivariable analysis indicated that increased level of tNAA was an independent predictor of preoperative seizures. According to MRS analysis, tNAA, rather than glutamate, might be a useful to detect preoperative seizures in patient with supratentorial gliomas.

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

Purpose

Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs.

Materials and Methods

A total of 102 LGG patients were allocated to training (n = 67) and validation (n = 35) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Results

After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879.

Conclusion

The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Neuro-Oncology and Radiogenomics: Time to Integrate?

Radiogenomics aims to predict genetic markers based on imaging features. The critical importance of molecular markers in the diagnosis and management of intracranial gliomas has led to a rapid growth in radiogenomics research, with progressively increasing complexity. Despite the advances in the techniques being examined, there has been little translation into the clinical domain. This has resulted in a growing disconnect between cutting-edge research and assimilation into clinical practice, though the fundamental goal is for these techniques to improve patient care. The goal of this review, therefore, is to discuss possible clinical scenarios in which the addition of radiogenomics may aid patient management. This includes facilitating patient counseling, determining optimal patient management when complete molecular characterization is not possible, reclassifying tumors, and overcoming some of the limitations of histologic assessment. The review also discusses considerations for selecting relevant radiogenomic features based on the clinical setting.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Volume-based histogram analysis of dynamic contrast-enhanced MRI for estimation of gliomas IDH1 mutation status

PURPOSE

The study aimed to investigate whether isocitrate dehydrogenase 1 (IDH1) mutation status in gliomas can be estimated by volume-based histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

Preoperative DCE-MRI data of 85 pathologically confirmed glioma patients including 33 carrying IDH1 mutant type (IDH1^mut) and 52 with IDH1 wildtype (IDH1^wt) were reviewed in a retrospective approach. Regions of interest (ROI) covering entire tumor volume were manually delineated using O.K. software (OmniKinetics, GE Healthcare, China). Histogram parameters of volume transfer constant (K^trans) and volume of extravascular /extracellular space per unit volume of tissue (V_e) derived from DCE-MRI were obtained. Mann-Whitney U tests were made to compare the differences in histogram parameters of K^trans and V_e between IDH1^mut and IDH1^wt in all gliomas and high-grade gliomas (HGGs, grade III and IV). Receiver operator characteristic (ROC) analysis were implemented to assess the diagnostic performance.

RESULTS

In histogram parameters of K^trans and V_e, pairwise comparisons demonstrated statistically significant differences in mean, standard deviation (SD), 90th and 95th percentiles (90%, 95%) values between IDH1^mut and IDH1^wt in all cases of gliomas and HGGs (P < 0.05, respectively). The ROC analysis revealed that the cut-off values of 95% value of K^trans (0.097 min^-1) and mean value of V_e (0.099) provided the best combination of sensitivity and specificity to distinguish all gliomas with IDH1^mut from IDH1^wt. In HGGs, the cut-off values of mean value of K^trans and V_e (0.044 min^-1, 0.099) played similar role.

CONCLUSION

Volume-based histogram analysis of DCE-MRI performs well in identification of IDH1^mut gliomas.

Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI

BACKGROUND AND PURPOSE

Image-based classification of lower-grade glioma molecular subtypes has substantial prognostic value. Diffusion tensor imaging has shown promise in lower-grade glioma subtyping but currently requires lengthy, nonstandard acquisitions. Our goal was to investigate lower-grade glioma classification using a machine learning technique that estimates fractional anisotropy from accelerated diffusion MR imaging scans containing only 3 diffusion-encoding directions.

MATERIALS AND METHODS

Patients with lower-grade gliomas (n = 41) (World Health Organization grades II and III) with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status were imaged preoperatively with DTI. Whole-tumor volumes were autodelineated using conventional anatomic MR imaging sequences. In addition to conventional ADC and fractional anisotropy reconstructions, fractional anisotropy estimates were computed from 3-direction DTI subsets using DiffNet, a neural network that directly computes fractional anisotropy from raw DTI data. Differences in whole-tumor ADC, fractional anisotropy, and estimated fractional anisotropy were assessed between IDH-wild-type and IDH-mutant lower-grade gliomas with and without 1p/19q codeletion. Multivariate classification models were developed using whole-tumor histogram and texture features from ADC, ADC + fractional anisotropy, and ADC + estimated fractional anisotropy to identify the added value provided by fractional anisotropy and estimated fractional anisotropy.

RESULTS

ADC (P = .008), fractional anisotropy (P < .001), and estimated fractional anisotropy (P < .001) significantly differed between IDH-wild-type and IDH-mutant lower-grade gliomas. ADC (P < .001) significantly differed between IDH-mutant gliomas with and without codeletion. ADC-only multivariate classification predicted IDH mutation status with an area under the curve of 0.81 and codeletion status with an area under the curve of 0.83. Performance improved to area under the curve = 0.90/0.94 for the ADC + fractional anisotropy classification and to area under the curve = 0.89/0.89 for the ADC + estimated fractional anisotropy classification.

CONCLUSIONS

Fractional anisotropy estimates made from accelerated 3-direction DTI scans add value in classifying lower-grade glioma molecular status.

Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis

OBJECTIVES

To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.

METHODS

A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for 'glioma', 'IDH mutation' and 'MRI'. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.

RESULTS

Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%-91%) and the summary specificity was 87% (95% CI, 78-92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

CONCLUSIONS

IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.

KEY POINTS

• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value. • The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79-91%) and the summary specificity was 87% (95% CI, 78-92%). • In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

Spectral Radiance of Protoporphyrin IX Fluorescence and Its Histopathological Implications in 5-Aminolevulinic Acid-Guided Surgery for Glioblastoma

OBJECTIVE

This study is intended to objectively clarify the relationship between the fluorescence intensity emitted by protoporphyrin IX (PpIX), which is a metabolite of 5-aminolevulinic acid (ALA), and histological findings during glioblastoma surgery.

BACKGROUND

ALA is widely used for the intraoperative detection of tumors. There are several reports about the fluorescence of PpIX and the histological findings of tumors, but judgments about the fluorescence intensity depend largely on the subjective sense of each surgeon.

METHODS

We quantified the PpIX fluorescence intensity emitted from tissue specimens using a spectroradiometer and evaluated the relationship between a spectral radiance of 635 nm and the histopathological features of surgical specimens of glioblastoma. Surgical samples from glioblastoma patients consist of a strongly fluorescent area (SFA) or vaguely fluorescent area (VFA). Hematoxylin and eosin staining, immunohistochemical Ki-67, and CD31 staining were performed to evaluate the cell density, MIB-1 index, and vascularity, respectively. The fluorescence intensities of each sample were compared with each histopathological parameter.

RESULTS

Cell density, MIB-1 index, and total vascular area were significantly correlated with PpIX fluorescence radiance. 87.5% of SFA were judged to be tumor bulk consisting mostly of tumor cells and 12.5% peritumoral invaded brain. In the VFA, 100% of specimens were judged to be peritumoral invaded brain.

CONCLUSIONS

ALA-induced PpIX fluorescence has quantitatively correlated well with histopathological malignant features both in SFA and VFA. These findings suggest that not only SFA but also VFA should be removed to the highest extent that does not cause neurological symptoms.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Remote intracranial recurrence of IDH mutant gliomas is associated with TP53 mutations and an 8q gain

Most IDH mutant gliomas harbor either 1p/19q co-deletions or TP53 mutation; 1p/19q co-deleted tumors have significantly better prognoses than tumors harboring TP53 mutations. To investigate the clinical factors that contribute to differences in tumor progression of IDH mutant gliomas, we classified recurrent tumor patterns based on MRI and correlated these patterns with their genomic characterization. Accordingly, in IDH mutant gliomas (N = 66), 1p/19 co-deleted gliomas only recurred locally, whereas TP53 mutant gliomas recurred both locally and in remote intracranial regions. In addition, diffuse tensor imaging suggested that remote intracranial recurrence in the astrocytomas, IDH-mutant with TP53 mutations may occur along major fiber bundles. Remotely recurrent tumors resulted in a higher mortality and significantly harbored an 8q gain; astrocytomas with an 8q gain resulted in significantly shorter overall survival than those without an 8q gain. OncoScan^® arrays and next-generation sequencing revealed specific 8q regions (i.e., between 8q22 and 8q24) show a high copy number. In conclusion, only tumors with TP53 mutations showed patterns of remote recurrence in IDH mutant gliomas. Furthermore, an 8q gain was significantly associated with remote intracranial recurrence and can be considered a poor prognostic factor in astrocytomas, IDH-mutant.