Comparison of clinically available dynamic susceptibility contrast post processing software to differentiate progression from pseudoprogression in post-treatment high grade glioma

INTRODUCTION

The purpose of this retrospective study was to compare two, widely available software packages for calculation of Dynamic Susceptibility Contrast (DSC) perfusion MRI normalized relative Cerebral Blood Volume (rCBV) values to differentiate tumor progression from pseudoprogression in treated high-grade glioma patients.

MATERIAL AND METHODS

rCBV maps processed by Siemens Syngo.via (Siemens Healthineers) and Olea Sphere (Olea Medical) software packages were co-registered to contrast-enhanced T1 (T1-CE). Regions of interest based on T1-CE were transferred to the rCBV maps. rCBV was calculated using mean values and normalized using contralateral normal- appearing white matter. The Wilcoxon test was performed to assess for significant differences, and software-specific optimal rCBV cutoff values were determined using the Youden index. Interrater reliability was evaluated for two raters using the intraclass correlation coefficient.

RESULTS

41 patients (18 females; median age = 59 years; range 21-77 years) with 49 new or size-increasing post-treatment contrast-enhancing lesions were included (tumor progression = 40 lesions; pseudoprogression = 9 lesions). Optimal rCBV cutoffs of 1.31 (Syngo.via) and 2.40 (Olea) were significantly different, with an AUC of 0.74 and 0.78, respectively. Interrater reliability was 0.85.

DISCUSSION

We demonstrate that different clinically available MRI DSC-perfusion software packages generate significantly different rCBV cutoff values for the differentiation of tumor progression from pseudoprogression in standard-of-care treated high grade gliomas. Physicians may want to determine the unique value of their perfusion software packages on an institutional level in order to maximize diagnostic accuracy when faced with this clinical challenge. Furthermore, combined with implementation of current DSC-perfusion recommendations, multi-center comparability will be improved.

Periventricular Nodular Heterotopia (PNH) associated with a "transmantle band sign" in epilepsy patients

OBJECTIVE

Previous studies using advanced MRI techniques have documented abnormal transmantle bands connecting ectopic nodules to overlying cortex in patients with periventricular nodular heterotopia (PNH). We describe a similar finding using conventional MRI techniques.

METHODS

Patients were identified by means of a full-text search of radiological reports. All scanning was performed using conventional sequences at 3T. Scans were reviewed by three neuroradiologists, and we characterized imaging features based on type of PNH and cortical irregularities associated with the transmantle band.

RESULTS

A total 57 PNH patients were reviewed, of whom 41 demonstrated a 'transmantle band' connecting the nodule to the overlying cortex. One or more periventricular heterotopic nodule was present in all 41 patients - this was bilateral in 29/41 (71%) and unilateral in the remaining 29%. In many cases there was more than one such band, and in some cases this band was nodular. In 19 of the cases, the cortex which the band connected to was abnormal, showing thinning in 4 cases, thickening in 5 cases, and polymicrogyria in another 10.

SIGNIFICANCE

The transmantle band can frequently be seen in both unilateral and bilateral cases of periventricular nodular heterotopia and can be visualized with conventional 3T MRI sequences. It highlights the underlying neuronal migration issues at play in the pathogenesis of this disorder, but it's underlying role in the complex, patient-specific epileptogenic networks in this cohort has yet to be determined and warrants further investigation.

Progressive Supranuclear Palsy Syndrome Associated With a Novel Tauopathy: Case Study

BACKGROUND AND OBJECTIVES

To report a novel tauopathy in a patient with protracted course progressive supranuclear palsy (PC-PSP).

METHODS

This was a clinical follow-up, gene analysis, neuropathologic study.

RESULTS

A 73-year-old man presented with diplopia, slowness, shuffling gait, and falls. Neurologic examination revealed slowed saccades, restricted up-gaze, and mild parkinsonism. Three years after onset, he developed personality changes. Slowly progressive parkinsonism was associated with memory and executive deficits. MRI showed subtle bilateral hippocampal and midbrain tegmentum atrophy and hyperintensity in the brainstem tegmentum and white matter of the medial temporal lobe. The duration of illness was 11 years. There were no pathogenic mutations in 80 genes known to be involved in neurodegeneration, including MAPT (H1/H1 haplotype) and APOE (ε3/ε3 genotype). Neuropathology revealed PSP type pathology together with the pathology described in the novel limbic-predominant neuronal inclusion body 4-repeat tauopathy (LNT) correlating well with the signal alterations seen in MRI.

DISCUSSION

Our observation broadens the spectrum of tau pathology associated with PC-PSP and suggests that memory deficit and hippocampal atrophy may be suggestive of non-Alzheimer disease pathology, including LNT. Understanding the diverse range of tau morphologies may help explain phenotypic heterogeneity seen in PSP.

Pattern of Recurrence of Glioblastoma Versus Grade 4 IDH-Mutant Astrocytoma Following Chemoradiation: A Retrospective Matched-Cohort Analysis

Background and Purpose: To quantitatively compare the recurrence patterns of glioblastoma (isocitrate dehydrogenase-wild type) versus grade 4 isocitrate dehydrogenase-mutant astrocytoma (wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase, respectively) following primary chemoradiation. Materials and Methods: A retrospective matched cohort of 22 wild type isocitrate dehydrogenase and 22 mutant isocitrate dehydrogenase patients were matched by sex, extent of resection, and corpus callosum involvement. The recurrent gross tumor volume was compared to the original gross tumor volume and clinical target volume contours from radiotherapy planning. Failure patterns were quantified by the incidence and volume of the recurrent gross tumor volume outside the gross tumor volume and clinical target volume, and positional differences of the recurrent gross tumor volume centroid from the gross tumor volume and clinical target volume. Results: The gross tumor volume was smaller for wild type isocitrate dehydrogenase patients compared to the mutant isocitrate dehydrogenase cohort (mean ± SD: 46.5 ± 26.0 cm³ vs 72.2 ± 45.4 cm³, P = .026). The recurrent gross tumor volume was 10.7 ± 26.9 cm³ and 46.9 ± 55.0 cm³ smaller than the gross tumor volume for the same groups (P = .018). The recurrent gross tumor volume extended outside the gross tumor volume in 22 (100%) and 15 (68%) (P= .009) of wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase patients, respectively; however, the volume of recurrent gross tumor volume outside the gross tumor volume was not significantly different (12.4 ± 16.1 cm³ vs 8.4 ± 14.2 cm³, P = .443). The recurrent gross tumor volume centroid was within 5.7 mm of the closest gross tumor volume edge for 21 (95%) and 22 (100%) of wild type isocitrate dehydrogenase and mutant isocitrate dehydrogenase patients, respectively. Conclusion: The recurrent gross tumor volume extended beyond the gross tumor volume less often in mutant isocitrate dehydrogenase patients possibly implying a differential response to chemoradiotherapy and suggesting isocitrate dehydrogenase status might be used to personalize radiotherapy. The results require validation in prospective randomized trials.

NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING

In contrast to the WHO 2016 guidelines that use genetic alterations to further stratify patients within a designated grade, new recommendations suggest that IDH mutation status, followed by 1p19q-codeletion, should be used before grade when differentiating gliomas. Although most gliomas will be resected and their tissue evaluated with genetic profiling, non-invasive characterization of genetic subgroup can benefit patients where surgery is not otherwise advised or a fast turn-around is required for clinical trial eligibility. Prior studies have demonstrated the utility of using anatomical images and deep learning to distinguish either IDH-mutant from IDH-wildtype tumors or 1p19q-codeleted from non-codeleted lesions separately, but not combined or using the most recent recommendations for stratification. The goal of this study was to evaluate the effects of training strategy and incorporation of Apparent Diffusion Coefficient (ADC) maps from diffusion-weighted imaging on predicting new genetic subgroups with deep learning. Using 414 patients with newly-diagnosed glioma (split 285/50/49 training/validation/test) and optimized training hyperparameters, we found that a 3-class approach with T1-post-contrast, T2-FLAIR, and ADC maps as inputs achieved the best performance for molecular subgroup classification, with overall accuracies of 86.0%[CI:0.839,1.0], 80.0%[CI:0.720,1.0], and 85.7%[CI:0.771,1.0] on training, validation, and test sets, respectively, and final test class accuracies of 95.2%(IDH-wildtype), 88.9%(IDH-mutated,1p19qintact), and 60%(IDHmutated,1p19q-codeleted). Creating an RGB-color image from 3 MRI images and applying transfer learning with a residual network architecture pretrained on ImageNet resulted in an 8% averaged increase in overall accuracy. Although classifying both IDH and 1p19q mutations together was overall advantageous compared with a tiered structure that first classified IDH mutational status, the 2-tiered approach better generalized to an independent multi-site dataset when only anatomical images were used. Including biologically relevant ADC images improved model generalization to our test set regardless of modeling approach, highlighting the utility of incorporating diffusion-weighted imaging in future multi-site analyses of molecular subgroup.

The centrally restricted diffusion sign on MRI for assessment of radiation necrosis in metastases treated with stereotactic radiosurgery

Purpose

Differentiation of radiation necrosis from tumor progression in brain metastases treated with stereotactic radiosurgery (SRS) is challenging. For this, we assessed the performance of the centrally restricted diffusion sign.

Methods

Patients with brain metastases treated with SRS who underwent a subsequent intervention (biopsy/resection) for a ring-enhancing lesion on preoperative MRI between 2000 and 2020 were included. Excluded were lesions containing increased susceptibility limiting assessment of DWI. Two neuroradiologists classified the location of the diffusion restriction with respect to the post-contrast T1 images as centrally within the ring-enhancement (the centrally restricted diffusion sign), peripherally correlating to the rim of contrast enhancement, both locations, or none. Measures of diagnostic accuracy and 95% CI were calculated for the centrally restricted diffusion sign. Cohen's kappa was calculated to identify the interobserver agreement.

Results

Fifty-nine patients (36 female; mean age 59, range 40 to 80) were included, 36 with tumor progression and 23 with radiation necrosis based on histopathology. Primary tumors included 34 lung, 12 breast, 5 melanoma, 3 colorectal, 2 esophagus, 1 head and neck, 1 endometrium, and 1 thyroid. The centrally restricted diffusion sign was seen in 19/23 radiation necrosis cases (sensitivity 83% (95% CI 63 to 93%), specificity 64% (95% CI 48 to 78%), PPV 59% (95% CI 42 to 74%), NPV 85% (95% CI 68 to 94%)) and 13/36 tumor progression cases (difference p < 0.001). Interobserver agreement was substantial, at 0.61 (95% CI 0.45 to 70.8).

Conclusion

We found a low probability of radiation necrosis in the absence of the centrally restricted diffusion sign.

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging

BACKGROUND

Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.

METHODS

Our dataset consisted of 384 patients with newly-diagnosed gliomas who underwent preoperative MR imaging with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.

RESULTS

The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI:[77.1,100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5-17.5%, and models that included diffusion-weighted imaging were 5-8.8% more accurate than those that used only anatomical imaging.

CONCLUSION

Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then1p19q-codeletion. Including ADC, a surrogate marker of cellularity, more accurately captured differences between subgroups.

Transformational Role of Medical Imaging in (Radiation) Oncology

Simple Summary

Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints.

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.

Recurrent tumor and treatment-induced effects have different MR signatures in contrast enhancing and non-enhancing lesions of high-grade gliomas

BACKGROUND

Differentiating treatment-induced injury from recurrent high-grade glioma is an ongoing challenge in neuro-oncology, in part due to lesion heterogeneity. This study aimed to determine whether different MR features were relevant for distinguishing recurrent tumor from the effects of treatment in contrast-enhancing lesions (CEL) and non-enhancing lesions (NEL).

METHODS

This prospective study analyzed 291 tissue samples (222 recurrent tumor, 69 treatment-effect) with known coordinates on imaging from 139 patients who underwent preoperative 3T MRI and surgery for a suspected recurrence. 8 MR parameter values were tested from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location for association with histopathological outcome using generalized estimating equation models for CEL and NEL tissue samples. Individual cutoff values were evaluated using receiver operating characteristic curve analysis with 5-fold cross-validation.

RESULTS

In tissue samples obtained from CEL, elevated relative cerebral blood volume (rCBV) was associated with the presence of recurrent tumor pathology (P < 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were associated with the presence of recurrent tumor pathology in NEL tissue samples (P < 0.008). A mean CNI cutoff value of 2.7 had the highest performance, resulting in mean sensitivity and specificity of 0.61 and 0.81 for distinguishing treatment-effect from recurrent tumor within the NEL.

CONCLUSION

Although our results support prior work that underscores the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion, we found that metabolic parameters may be better at differentiating recurrent tumor from treatment-related changes in the NEL of high-grade gliomas.

Simultaneous multi-slice spin- and gradient-echo dynamic susceptibility-contrast perfusion-weighted MRI of gliomas

Although combined spin- and gradient-echo (SAGE) dynamic susceptibility-contrast (DSC) MRI can provide perfusion quantification that is sensitive to both macrovessels and microvessels while correcting for T₁ -shortening effects, spatial coverage is often limited in order to maintain a high temporal resolution for DSC quantification. In this work, we combined a SAGE echo-planar imaging (EPI) sequence with simultaneous multi-slice (SMS) excitation and blipped controlled aliasing in parallel imaging (blipped CAIPI) at 3 T to achieve both high temporal resolution and whole brain coverage. Two protocols using this sequence with multi-band (MB) acceleration factors of 2 and 3 were evaluated in 20 patients with treated gliomas to determine the optimal scan parameters for clinical use. ΔR₂ *(t) and ΔR₂ (t) curves were derived to calculate dynamic signal-to-noise ratio (dSNR), ΔR₂ *- and ΔR₂ -based relative cerebral blood volume (rCBV), and mean vessel diameter (mVD) for each voxel. The resulting SAGE DSC images acquired using MB acceleration of 3 versus 2 appeared visually similar in terms of image distortion and contrast. The difference in the mean dSNR from normal-appearing white matter (NAWM) and that in the mean dSNR between NAWM and normal-appearing gray matter were not statistically significant between the two protocols. ΔR₂ *- and ΔR₂ -rCBV maps and mVD maps provided unique contrast and spatial heterogeneity within tumors.

NIMG-36. AUTOMATIC STRATIFICATION OF ENHANCING AND NON-ENHANCING GLIOMAS INTO GENETIC SUBTYPES USING DEEP NEURAL NETWORKS AND DIFFUSION-WEIGHTED IMAGING

INTRODUCTION

Current WHO guidelines emphasize classification of diffuse gliomas by genetic alterations into three subgroups: 1) IDH-wildtype; 2) IDH-mutant, 1p/19q-codeleted; and 3) IDH-mutant, 1p/19q-non-codeleted. Non-invasive genetic characterization can benefit patients with inoperable lesions or who are administered molecularly-targeted therapy before surgery. Prior studies that use anatomical images and convolutional neural networks (CNNs) to distinguish either IDH-mutant from IDH-wildtype tumors, or 1p/19q-codeleted from non-codeleted tumors have resulted in misclassification of nonenhancing IDH-wildtype and enhancing IDH-mutant tumors. This study investigated the benefit of a priori separation of enhancing from nonenhancing lesions and the inclusion of ADC maps from diffusion MRI to genetic subgroup classification.

METHODS

3D T2-weighted, T2-FLAIR, and post-contrast T1-weighted images were acquired preoperatively from 254 patients with newly-diagnosed gliomas. IDH1R132H mutations[VJ1] [CJ2], 1p19q-codeletions, ATRX alterations, and p53 mutations were assessed from the resected tissue to determine subtype stratification: IDH-wildtype (n=95), IDH-mutant, 1p/19q-codeleted (n=62), and IDH-mutant, non-codeleted (n=97). 3-channel input images were constructed for each patient using T2-FLAIR, T1-post-contrast, and either T2-weighted or ADC images. Three VGG-16 CNNs pre-trained on ImageNet were re-trained for: 1) lesions without enhancement, 2) enhancing lesions, and 3) all lesions together[VJ3].

RESULTS

A network trained on only enhancing lesions predicted the IDH-wildtype subtype with the highest class accuracy (ADC 94%, T2-weighted 100%) compared to using all lesions combined (ADC 90%, T2-weighted 90%). Models trained using non-enhancing lesions and ADC yielded the highest accuracy classifying 1p/19q-codeleted/non-codeleted subgroups (87%/90% for the non-enhancing network vs 83%/81% for combined network).

CONCLUSIONS

Our results support a strategy that first considers whether a lesion is enhancing when predicting molecular subgroup and includes ADC if the lesion is non-enhancing. Analysis is underway to test this model framework on independent TCIA data.

Centrally Reduced Diffusion Sign for Differentiation between Treatment-Related Lesions and Glioma Progression: A Validation Study

BACKGROUND AND PURPOSE

Differentiating between treatment-related lesions and tumor progression remains one of the greatest dilemmas in neuro-oncology. Diffusion MR imaging characteristics may provide useful information to help make this distinction. The aim of the study was to assess the diagnostic accuracy of the centrally reduced diffusion sign for differentiation of treatment-related lesions and true tumor progression in patients with suspected glioma recurrence.

MATERIALS AND METHODS

The images of 231 patients who underwent an operation for suspected glioma recurrence were reviewed. Patients with susceptibility artifacts or without central necrosis were excluded. The final diagnosis was established according to histopathology reports. Two neuroradiologists classified the diffusion patterns on preoperative MR imaging as the following: 1) reduced diffusion in the solid component only, 2) reduced diffusion mainly in the solid component, 3) no reduced diffusion, 4) reduced diffusion mainly in the central necrosis, and 5) reduced diffusion in the central necrosis only. Diagnostic accuracy metrics and the area under the receiver operating characteristic curve were estimated for the diffusion patterns.

RESULTS

One hundred three patients were included (22 with treatment-related lesions and 81 with tumor progression). The diagnostic accuracy results for the centrally reduced diffusion pattern as a predictor of treatment-related lesions ("mainly central" and "exclusively central" patterns versus all other patterns) were as follows: 64% sensitivity (95% CI, 41%-83%), 84% specificity (95% CI, 74%-91%), 52% positive predictive value (95% CI, 37%-66%), and 89% negative predictive value (95% CI, 83%-94%).

CONCLUSIONS

The centrally reduced diffusion sign is associated with the presence of treatment effect. The probability of a histologic diagnosis of a treatment-related lesion is low (11%) in the absence of centrally reduced diffusion.

Treatment-induced lesions in newly diagnosed glioblastoma patients undergoing chemoradiotherapy and heat-shock protein vaccine therapy

OBJECTIVES

Treatment-induced lesions represent a great challenge in neuro-oncology. The aims of this study were (i) to characterize treatment induced lesions in glioblastoma patients treated with chemoradiotherapy and heat-shock protein (HSP) vaccine and (ii) to evaluate the diagnostic accuracy of diffusion weighted imaging for differentiation between treatment-induced lesions and tumor progression.

METHODS

Twenty-seven patients with newly diagnosed glioblastoma treated with HSP vaccine and chemoradiotherapy were included. Serial magnetic resonance imaging evaluation was performed to detect treatment-induced lesions and assess their growth. Quantitative analysis of the apparent diffusion coefficient (ADC) was performed to discriminate treatment-induced lesions from tumor progression. Mann-Whitney U-test and receiver operating characteristic (ROC) curves were used for analysis.

RESULTS

Thirty-three percent of patients developed treatment-induced lesions. Five treatment-related lesions appeared between end of radiotherapy and the first vaccine administration; 4 lesions within the first 4 months from vaccine initiation and 1 at 3.5 years. Three patients with pathology proven treatment-induced lesions showed a biphasic growth pattern progressed shortly after. ADC ratio between the peripheral enhancing rim and central necrosis showed an accuracy of 0.84 (95% CI 0.63-1) for differentiation between progression and treatment-induced lesions.

CONCLUSION

Our findings do not support the iRANO recommendation of a 6-month time window in which progressive disease should not be declared after immunotherapy initiation. A biphasic growth pattern of pathologically proven treatment-induced lesions was associated with a dismal prognosis. The presence of lower ADC values in the central necrotic portion of the lesions compared to the enhancing rim shows high specificity for detection of treatment-induced lesions.

NIMG-33. TUMOR GROWTH AND MRI TRAJECTORIES IN GRADE II AND III GLIOMA

INTRODUCTION

The purpose of this work was to investigate the trajectory of tumor growth and multiparametric MRI characteristics in grade II and III gliomas leading up to the onset of new contrast enhancement or the expansion of FLAIR signal abnormality.

METHODS

Fifty-one grade II and III glioma patients (39 grade II, 12 grade III) underwent serial MR imaging at standard of care timepoints. Multiparametric MR imaging was performed and maps of the apparent diffusion coefficient (ADC) and percentiles (10th, 50th, 90th) from histograms of normalized signal intensities were calculated. Regions of Interest (ROIs) were defined for the entire T2/FLAIR lesion, and when present, the contrast-enhancing (CE) lesion. Serial imaging parameters were acquired for each of these ROIs. Linear rates of change were calculated for CE volume, T2/FLAIR volume, normalized T2 FSE intensity within the FLAIR volume, normalized T2/FLAIR intensity within the FLAIR volume, and normalized median, 10th percentile and 90th% ADC values within the FLAIR volume.

RESULTS

Thirty-nine patients progressed while on study (7 by CE growth, 32 by T2/FLAIR growth), and 12 patients remained stable, according to RANO criteria. In progressed patients, T2/FLAIR volume and T2 FSE intensity increased over time to progression. The rate of T2/FLAIR volume growth was significantly greater in progressed than stable patients (mean = .42, -.07 mL/week, respectively). The rate of normalized T2/FLAIR signal intensity change was also significantly different in progressed than stable patients (mean = -.002, .0035 mL/week). There were no significant interactions with grade or molecular subgroup by WHO criteria. Additionally, 44% (17/39) of progressed patients by RANO criteria were characterized as having stable disease by radiologic assessment.

CONCLUSION

Tumor volumes remain the gold standard for response assessment in lower grade gliomas and there exists a need for standardized measurement as visual assessment for determination of tumor status is inconsistent.

NIMG-51. THE CENTRALLY RESTRICTED DIFFUSION SIGN FOR DIFFERENTIATION BETWEEN TREATMENT-RELATED LESIONS AND TUMOR PROGRESSION IN GLIOMA PATIENTS: A VALIDATION STUDY

BACKGROUND

The aim of the study was to compare the diagnostic accuracy of different diffusion patterns for differentiation between treatment-related lesions and true tumor progression in patients with suspected glioma recurrence.

METHODS

A retrospective analysis of a prospective cohort was conducted. The images of 240 consecutive patients who underwent surgery for suspected glioma recurrence were reviewed for potential inclusion. Exclusion criteria were lack of a ring-enhancing lesion and presence of susceptibility artifact. Final diagnosis was established upon review of histopathology reports. Lesions showing treatment-related changes and less than 25% of viable tumor were considered treatment-related lesions. More than 25% of viable tumor was considered recurrent tumor. A neuroradiologist, blinded to the diagnosis, evaluated the diffusion patterns on preoperative MRI. ROC curve analysis was performed.

RESULTS

One hundred and ten patients were included (26 with treatment-related lesions and 84 with tumor progression). Of 110, 35 showed no reduced diffusion. Fifty patients had reduced diffusion in the solid lesion component (84%, recurrent tumor and 16%, treatment-related lesions). Twenty-five patients showed reduced diffusion within the central necrosis (48%, recurrent tumor and 52%, treatment-related lesions). Most cases with reduced diffusion in the central necrotic region showed mixed pathology with concurrent treatment effect and viable tumor. The AUC for the combined diffusion pattern (reduced diffusion in the solid lesion component vs in the necrotic region) was 0.68 (95%CI=0.55–0.81). The AUC for the traditional diffusion approach (reduced diffusion in the solid lesion component vs no reduced diffusion) was 0.59 (95%CI=0.49–070). No significant differences were found in AUC (p=0.3).

CONCLUSION

Although the existence of centrally reduced diffusion seems to be associated with the presence of treatment effect, it does not significantly increase the diagnostic accuracy of the traditional diffusion evaluation approach. This is most likely related to the high incidence of concurrent recurrent tumor and treatment effect.

NIMG-42. RECURRENT TUMOR AND TREATMENT-INDUCED EFFECTS HAVE DIFFERENT MR SIGNATURES IN CONTRAST ENHANCING AND NON-ENHANCING LESIONS OF HIGH-GRADE GLIOMAS

INTRODUCTION

It is estimated that 25 to 35% of patients experience treatment-induced effects that can mimic recurrent high-grade gliomas. This diagnostic challenge is complicated by the coexistence of treatment-related changes and recurrent tumor within the same lesion which limits the accuracy of classification based on summary metrics of multi-parametric MRI. This study aimed to determine whether different MR features were relevant for distinguishing pathological features of recurrent tumor from the effects of treatment in the contrast enhancing and non-enhancing lesions of recurrent high-grade gliomas.

METHODS

Leveraging our unique dataset of image-guided tissue samples that directly maps pathology to MR characteristics, we analyzed 291 tissue samples (222 recurrent tumor; 69 treatment effect) with known coordinates on imaging from 139 patients that underwent preoperative 3T MRI and surgery for a suspected high-grade recurrent tumor. 8 MR parameter values from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location were tested for association with histopathological outcome using univariate and multivariate generalized estimating equation models for enhancing and non-enhancing tissue samples. Individual cutoff values were determined and evaluated using ROC-Curve analysis with 5-fold cross-validation.

RESULTS

In tissue samples obtained from contrast-enhancing lesions, elevated relative cerebral blood volume (rCBV) was significantly associated with the presence of recurrent tumor (p< 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were significantly associated with the presence of recurrent tumor in non-enhancing tissue samples (p< 0.008). Cutoff values of 1.6(rCBV), 2.7(CNI), and 2.1(nCho) had the highest performance.

CONCLUSION

Our results confirm the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion. We report a novel finding that metabolic parameters can differentiate recurrent tumor from treatment-related changes in the non-enhancing lesion of high-grade gliomas. These results will help improve future management of patients with suspected recurrence.

Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival

Background

We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes.

Methods

Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan–Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset.

Results

Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01–16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1–3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47–5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone.

Conclusions

Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.

Spinal Cord Atrophy in Multiple Sclerosis: A Systematic Review and Meta-Analysis

BACKGROUND AND PURPOSE

Spinal cord atrophy (SCA) is an important emerging outcome measure in multiple sclerosis (MS); however, there is limited consensus on the magnitude and rate of atrophy. The objective of this study was to synthesize the available data on measures of SCA in MS.

METHODS

Using published guidelines, relevant literature databases were searched between 1977 and 2017 for case-control or cohort studies reporting a quantitative measure of SCA in MS patients. Random-effects models pooled cross-sectional measures and longitudinal rates of SCA in MS and healthy controls (HCs). Student's t-test assessed differences between pooled measures in patient subgroups. Heterogeneity was assessed using DerSimonian and Laird's Q-test and the I ² -index.

RESULTS

A total of 1,465 studies were retrieved including 94 that met inclusion and exclusion criteria. Pooled estimates of mean cervical spinal cord (SC) cross-sectional area (CSA) in all MS patients, relapsing-remitting MS (RRMS), all progressive MS, secondary progressive MS (SPMS), primary-progressive MS (PPMS), and HC were: 73.07 mm² (95% CI [71.52-74.62]), 78.88 mm² (95% CI [76.92-80.85]), 69.72 mm² (95% CI [67.96-71.48]), 68.55 mm² (95% CI [65.43-71.66]), 70.98 mm² (95% CI [68.78-73.19]), and 80.87 mm² (95% C I [78.70-83.04]), respectively. Pooled SC-CSA was greater in HC versus MS (P < .001) and RRMS versus progressive MS (P < .001). SCA showed moderate correlations with global disability in cross-sectional studies (r-value with disability score range [-.75 to -.22]). In longitudinal studies, the pooled annual rate of SCA was 1.78%/year (95%CI [1.28-2.27]).

CONCLUSIONS

The SC is atrophied in MS. The magnitude of SCA is greater in progressive versus relapsing forms and correlates with clinical disability. The pooled estimate of annual rate of SCA is greater than reported rates of brain atrophy in MS. These results demonstrate that SCA is highly relevant as an imaging outcome in MS clinical trials.

Quantitative spinal cord MRI in radiologically isolated syndrome

Objectives

To assess whether quantitative spinal cord MRI (SC-MRI) measures, including atrophy, and diffusion tensor imaging (DTI) and magnetization transfer imaging metrics were different in radiologically isolated syndrome (RIS) vs healthy controls (HCs).

Methods

Twenty-four participants with RIS and 14 HCs underwent cervical SC-MRI on a 3T magnet. Manually segmented regions of interest circumscribing the spinal cord cross-sectional area (SC-CSA) between C3 and C4 were used to extract SC-CSA, fractional anisotropy, mean, perpendicular, and parallel diffusivity (MD, λ_⊥, and λ_||) and magnetization transfer ratio (MTR). Spinal cord (SC) lesions, SC gray matter (GM), and SC white matter (WM) areas were also manually segmented. Multivariable linear regression was performed to evaluate differences in SC-MRI measures in RIS vs HCs, while controlling for age and sex.

Results

In this cross-sectional study of participants with RIS, 71% had lesions in the cervical SC. Of quantitative SC-MRI metrics, spinal cord MTR showed a trend toward being lower in RIS vs HCs (p = 0.06), and there was already evidence of brain atrophy (p = 0.05). There were no significant differences in SC-DTI metrics, GM, WM, or CSA between RIS and HCs.

Conclusion

The SC demonstrates minimal microstructural changes suggestive of demyelination and inflammation in RIS. These findings are in contrast to established MS and raise the possibility that the SC may play an important role in triggering clinical symptomatology in MS. Prospective follow-up of this cohort will provide additional insights into the role the SC plays in the complex sequence of events related to MS disease initiation and progression.

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning

BACKGROUND AND PURPOSE

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma.

MATERIALS AND METHODS

Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15.

RESULTS

The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 (P = .021), reader 2 (P = .035), and reader 3 (P = .007).

CONCLUSIONS

Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

Meta-analysis of timing of endovascular aneurysm treatment in subarachnoid haemorrhage: inconsistent results of early treatment within 1 day

BACKGROUND AND PURPOSE

To systematically review and meta-analyse the data on impact of timing of endovascular treatment in aneurysmal subarachnoid haemorrhage (SAH) to determine if earlier treatment is associated with improved clinical outcomes and reduced case fatality.

METHODS

We searched MEDLINE, Cochrane database, EMBASE and Web of Science to identify studies for inclusion. The measures of effect utilised were unadjusted/adjusted ORs. Effect estimates were combined using random effects models for each outcome (poor outcome, case fatality); heterogeneity was assessed using the I² index. Subgroup and sensitivity analyses were performed to account for heterogeneity and risk of bias.

RESULTS

16 studies met the inclusion criteria. Treatment <1 day was associated with a reduced odds of poor outcome compared with treatment >1 day (OR=0.40 (95% CI 0.28 to 0.56; I²=0%)) but not when compared with treatment at 1-3 days (OR=1.16 (95% CI 0.47 to 2.90; I²=81%)). Treatment at <2 days and at <3 days were associated with similar odds of poor outcome compared with later treatment (OR=1.20 (95% CI 0.70 to 2.05; I²=73%; OR=0.71 (95% CI 0.36 to 1.37; I²=71%)). Early treatment was associated with similar odds of case fatality compared with later treatment, regardless of how early/late treatment were defined (OR=1.80 (95% CI 0.88 to 3.67; I²=34%) for treatment <1 day vs days 1-3; OR=1.71 (95% CI 0.72 to 4.03; I²=54%) for treatment <2 days vs later; OR=0.90 (95% CI 0.31 to 2.68; I²=48%) for treatment <3 days vs later).

CONCLUSIONS

In only 1 of the analyses was there a statistically significant result, which favoured treatment <1 day. The inconsistent results and heterogeneity within most analyses highlight the lack of evidence for best timing of endovascular treatment in SAH patients.

Comparison of Sagittal FSE T2, STIR, and T1-Weighted Phase-Sensitive Inversion Recovery in the Detection of Spinal Cord Lesions in MS at 3T

BACKGROUND AND PURPOSE

Determining the diagnostic accuracy of different MR sequences is essential to design MR imaging protocols. The purpose of the study was to compare 3T sagittal FSE T2, STIR, and T1-weighted phase-sensitive inversion recovery in the detection of spinal cord lesions in patients with suspected or definite MS.

MATERIALS AND METHODS

We performed a retrospective analysis of 38 patients with suspected or definite MS. Involvement of the cervical and thoracic cord segments was recorded on sagittal FSE T2, STIR, and T1-weighted phase-sensitive inversion recovery sequences independently by 2 readers. A consensus criterion standard read was performed with all sequences available. Sensitivity, specificity, and interobserver agreement were calculated for each sequence.

RESULTS

In the cervical cord, the sensitivity of T1-weighted phase-sensitive inversion recovery (96.2%) and STIR (89.6%) was significantly higher (P < .05) than that of FSE T2 (50.9%), but no significant difference was found between T1-weighted phase-sensitive inversion recovery and STIR. In the thoracic cord, sensitivity values were 93.8% for STIR, 71.9% for FSE T2, and 50.8% for T1-weighted phase-sensitive inversion recovery. Significant differences were found for all comparisons (P < .05). No differences were detected in specificity. Poor image quality and lower sensitivity of thoracic T1-weighted phase-sensitive inversion recovery compared with the other 2 sequences were associated with a thicker back fat pad.

CONCLUSIONS

The use of an additional sagittal sequence other than FSE T2 significantly increases the detection of cervical and thoracic spinal cord lesions in patients with MS at 3T. In the cervical segment, both STIR and T1-weighted phase-sensitive inversion recovery offer high sensitivity and specificity, whereas in the thoracic spine, STIR performs better than T1-weighted phase-sensitive inversion recovery, particularly in patients with a thick dorsal fat pad.

Magnetic resonance susceptibility weighted imaging in neurosurgery: current applications and future perspectives

Susceptibility weighted imaging (SWI) is a relatively new imaging technique. Its high sensitivity to hemorrhagic components and ability to depict microvasculature by means of susceptibility effects within the veins allow for the accurate detection, grading, and monitoring of brain tumors. This imaging modality can also detect changes in blood flow to monitor stroke recovery and reveal specific subtypes of vascular malformations. In addition, small punctate lesions can be demonstrated with SWI, suggesting diffuse axonal injury, and the location of these lesions can help predict neurological outcome in patients. This imaging technique is also beneficial for applications in functional neurosurgery given its ability to clearly depict and differentiate deep midbrain nuclei and close submillimeter veins, both of which are necessary for presurgical planning of deep brain stimulation. By exploiting the magnetic susceptibilities of substances within the body, such as deoxyhemoglobin, calcium, and iron, SWI can clearly visualize the vasculature and hemorrhagic components even without the use of contrast agents. The high sensitivity of SWI relative to other imaging techniques in showing tumor vasculature and microhemorrhages suggests that it is an effective imaging modality that provides additional information not shown using conventional MRI. Despite SWI's clinical advantages, its implementation in MRI protocols is still far from consistent in clinical usage. To develop a deeper appreciation for SWI, the authors here review the clinical applications in 4 major fields of neurosurgery: neurooncology, vascular neurosurgery, neurotraumatology, and functional neurosurgery. Finally, they address the limitations of and future perspectives on SWI in neurosurgery.

Pixel-by-Pixel Comparison of Volume Transfer Constant and Estimates of Cerebral Blood Volume from Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast-Enhanced MR Imaging in High-Grade Gliomas

BACKGROUND AND PURPOSE

Estimates of blood volume and volume transfer constant are parameters commonly used to characterize hemodynamic properties of brain lesions. The purposes of this study were to compare values of volume transfer constant and estimates of blood volume in high-grade gliomas on a pixel-by-pixel basis to comprehend whether they provide different information and to compare estimates of blood volume obtained by dynamic contrast-enhanced MR imaging and dynamic susceptibility contrast-enhanced MR imaging.

MATERIALS AND METHODS

Thirty-two patients with biopsy-proved grade IV gliomas underwent dynamic contrast-enhanced MR imaging and dynamic susceptibility contrast-enhanced MR imaging, and parametric maps of volume transfer constant, plasma volume, and CBV maps were calculated. The Spearman rank correlation coefficients among matching values of CBV, volume transfer constant, and plasma volume were calculated on a pixel-by-pixel basis. Comparison of median values of normalized CBV and plasma volume was performed.

RESULTS

Weak-but-significant correlation (P < .001) was noted for all comparisons. Spearman rank correlation coefficients were as follows: volume transfer constant versus CBV, ρ = 0.113; volume transfer constant versus plasma volume, ρ = 0.256; CBV versus plasma volume, ρ = 0.382. We found a statistically significant difference (P < .001) for the estimates of blood volume obtained by using dynamic contrast-enhanced MR imaging (mean normalized plasma volume, 13.89 ± 11.25) and dynamic susceptibility contrast-enhanced MR imaging (mean normalized CBV, 4.37 ± 4.04).

CONCLUSIONS

The finding of a very weak correlation between estimates of microvascular density and volume transfer constant suggests that they provide different information. Estimates of blood volume obtained by using dynamic contrast-enhanced MR imaging are significantly higher than those obtained by dynamic susceptibility contrast-enhanced MR imaging in human gliomas, most likely due to the effect of contrast leakage.