Reliability of Semi-Automated Segmentations in Glioblastoma

Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment

OBJECTIVE

Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively.

METHODS

The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95).

RESULTS

In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs.

CONCLUSIONS

The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma's MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences.

Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions' subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP

PURPOSE

Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm.

METHODS

42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk.

RESULTS

Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value.

CONCLUSION

Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

Impact of extent of resection on outcome from glioblastoma using the RANO resect group classification system: a retrospective, population-based cohort study

Background

Extent of resection (EOR) is associated with survival in glioblastoma. A standardized classification for EOR was lacking until a system was recently proposed by the response assessment in neuro-oncology (RANO) resect group. We aimed to assess EOR in an unselected glioblastoma cohort and use this classification system to evaluate the impact on survival in a real-world setting.

Methods

We retrospectively identified all patients with histologically confirmed glioblastoma in Western Norway between 1.1.2007 and 31.12.2014. Volumetric analyses were performed using a semi-automated method. EOR was categorized according to the recent classification system. Kaplan-Meier method and Cox proportional hazard ratios were applied for survival analyses.

Results

Among 235 included patients, biopsy (EOR class 4) was performed in 50 patients (21.3%), submaximal contrast enhancement (CE) resection (EOR class 3) in 66 patients (28.1%), and maximal CE resection (EOR class 2) in 119 patients (50.6%). Median survival was 6.2 months, 9.2 months, and 14.9 months, respectively. Within EOR class 2, 80 patients underwent complete CE resection (EOR class 2A) and had a median survival of 20.0 months, while 39 patients had a near-total CE resection, with ≤1 cm3 CE residual volume (EOR class 2B), and a median survival of 11.1 months, P < 0.001. The 2-year survival rate in EOR class 2A was 40.0%, compared to 7.7% in EOR class 2B.

Conclusions

RANO resect group classification for the extent of resection reflected outcome from glioblastoma in a real-world setting. There was significantly superior survival after complete CE resection compared to near-total resection.

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.

Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions

BACKGROUND

 Recently introduced MRI techniques facilitate accelerated examinations or increased resolution with the same duration. Further techniques offer homogeneous image quality in regions with anatomical transitions. The question arises whether and how these techniques can be adopted for routine diagnostic imaging.

METHODS

 Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered.

RESULTS AND CONCLUSIONS

 Compressed sensing and simultaneous multi-slice imaging are novel acceleration techniques with different yet complimentary applications. They do not suffer from classical signal-to-noise-ratio penalties. Combining 3 D and acceleration techniques facilitates new broader examination protocols, particularly for clinical brain imaging. In further regions of the nervous systems mainly specific applications appear to benefit from recent technological improvements.

KEY POINTS

  · New acceleration techniques allow for faster or higher resolution examinations.. · New brain imaging approaches have evolved, including more universal examination protocols.. · Other regions of the nervous system are dominated by targeted applications of recently introduced MRI techniques..

CITATION FORMAT

· Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 2 - Acceleration Methods and Implications for Individual Regions. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8789.

Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation

In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.

Intraoperative integration of multimodal imaging to improve neuronavigation: a technical note

BACKGROUND

Brain shift may cause significant error in neuronavigation leading the surgeon to possible mistakes. Intraoperative MRI is the most reliable technique in brain tumor surgery. Unfortunately, it is highly expensive and time consuming and, at the moment, it is available only in few neurosurgical centers.

METHODS

In this case series the surgical workflow for brain tumor surgery is described where neuronavigation of pre-operative MRI, intraoperative CT scan and US as well as rigid and elastic image fusion between preoperative MRI and intraoperative US and CT, respectively, was applied to four brain tumor patients in order to compensate for surgical induced brain shift by using a commercially available software (Elements Image Fusion 4.0 with Virtual iMRI Cranial; Brainlab AG).

RESULTS

Three exemplificative cases demonstrated successful integration of different components of the described intraoperative surgical workflow. The data indicates that intraoperative navigation update is feasible by applying intraoperative 3D US and CT scanning as well as rigid and elastic image fusion applied depending on the degree of observed brain shift.

CONCLUSIONS

Integration of multiple intraoperative imaging techniques combined with rigid and elastic image fusion of preoperative MRI may reduce the risk of incorrect neuronavigation during brain tumor resection. Further studies are needed to confirm the present findings in a larger population.

The Extent of Resection in Gliomas-Evidence-Based Recommendations on Methodological Aspects of Research Design

OBJECTIVE

Modern neurosurgery has established maximal safe resection as a cornerstone in the management of diffuse gliomas. Evaluation of the extent of resection (EOR), and its association with certain outcomes or interventions, heavily depends on an adequate methodology to draw strong conclusions. We aim to identify weaknesses and limitations that may threaten the internal validity and generalizability of studies involving the EOR in patients with glioma and to suggest methodological recommendations that may help mitigate these threats.

METHODS

A systematic search was performed by querying PubMed, Web of Science, and Scopus since inception to April 30, 2021 using PICOS/PRISMA guidelines. Articles were then screened to identify high-impact studies evaluating the EOR in patients diagnosed with diffuse gliomas in accordance with predefined criteria. We identify common weakness and limitations during the evaluation of the EOR in the selected studies and then delineate potential methodological recommendations for future endeavors dealing with the EOR.

RESULTS

We identified 31 high-impact studies and found several research design issues including inconsistencies regarding EOR terminology, measurement, data collection, analysis, and reporting. Although some of these issues were related to now outdated reporting standards, many were still present in recent publications and deserve attention in contemporary and future research.

CONCLUSIONS

There is a current need to focus more attention to the methodological aspects of glioma research. Methodological inconsistencies may introduce weaknesses into the internal validity of the studies and hamper comparative analysis of cohorts from different institutions. We hope our recommendations will eventually help develop stronger methodological designs in future research endeavors.

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

Background

Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.

Purpose

To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.

Methods

We conducted a systematic review of 572 brain tumour segmentation studies during 2015-2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).

Statistical tests

We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.

Results

We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.

Conclusion

U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

18F-FET-PET-guided gross total resection improves overall survival in patients with WHO grade III/IV glioma: moving towards a multimodal imaging-guided resection

Purpose

PET using radiolabeled amino acid [¹⁸F]-fluoro-ethyl-_L-tyrosine (FET-PET) is a well-established imaging modality for glioma diagnostics. The biological tumor volume (BTV) as depicted by FET-PET often differs in volume and location from tumor volume of contrast enhancement (CE) in MRI. Our aim was to investigate whether a gross total resection of BTVs defined as < 1 cm³ of residual BTV (PET GTR) correlates with better oncological outcome.

Methods

We retrospectively analyzed imaging and survival data from patients with primary and recurrent WHO grade III or IV gliomas who underwent FET-PET before surgical resection. Tumor overlap between FET-PET and CE was evaluated. Completeness of FET-PET resection (PET GTR) was calculated after superimposition and semi-automated segmentation of pre-operative FET-PET and postoperative MRI imaging. Survival analysis was performed using the Kaplan–Meier method and the log-rank test.

Results

From 30 included patients, PET GTR was achieved in 20 patients. Patients with PET GTR showed improved median OS with 19.3 compared to 13.7 months for patients with residual FET uptake (p = 0.007; HR 0.3; 95% CI 0.12–0.76). This finding remained as independent prognostic factor after performing multivariate analysis (HR 0.19, 95% CI 0.06–0.62, p = 0.006). Other survival influencing factors such as age, IDH-mutation, MGMT promotor status, and adjuvant treatment modalities were equally distributed between both groups.

Conclusion

Our results suggest that PET GTR improves the OS in patients with WHO grade III or IV gliomas. A multimodal imaging approach including FET-PET for surgical planning in newly diagnosed and recurrent tumors may improve the oncological outcome in glioma patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11060-021-03844-1.

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

Sex-based survival differences in IDH-wildtype glioblastoma: Results from a retrospective cohort study

A female survival benefit has been described for glioblastoma patients. Recent studies report that the effect of 0⁶-methylguanine-DNA-methyltransferase gene promoter (MGMTp) methylation is only present in female patients. We retrospectively studied sex-based survival, including MGMTp-methylation, in a cohort of 159 uniformly treated isocitrate dehydrogenase wildtype (IDHwt) patients. All patients were treated with temozolomide-based chemoradiotherapy after surgery. Kaplan-Meier survival curves and Cox regression models were used to evaluate overall survival. The study included 59 female (37.1%) and 100 male patients (62.9%). There were no statistically significant differences between sexes concerning demographic, surgical or radiological characteristics. Female patients harbored MGMTp-methylated tumors in 45.8% of cases and males in 33% (P = 0.129). Median overall survival was 13.4 months for men and women alike. After adjustment of survival for age, Karnofsky Performance Score, extent of resection and MGMTp-methylation, sex did not have a significant survival impact. However, MGMTp-methylation proved to be an independent beneficial prognosticator for both sexes, contradicting earlier reports. Several sex-based molecular subtypes of glioblastoma with different response to current treatment may exist explaining conflicting survival results in different patient cohorts. Further research on sex-based differences in IDHwt glioblastoma patients is needed.

Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

How Reliable Are Volumetric Techniques for High-Grade Gliomas? A Comparison Study of Different Available Tools

BACKGROUND

Gliomas are the most common malignant primary brain tumors. Assessment of the tumor volume represents a crucial point in preoperative and postoperative evaluation.

OBJECTIVE

To compare pre- and postoperative tumor volumes obtained with an automated, semi-automatic, and manual segmentation tool. Mean processing time of each segmentation techniques was measured.

METHODS

Manual segmentation was performed on preoperative and postoperative magnetic resonance images with the open-source software Horos (Horos Project). "SmartBrush," a tool of the IPlan Cranial software (Brainlab, Feldkirchen, Germany), was used to carry out the semi-automatic segmentation. The open-source BraTumIA software (NeuroImaging Tools and Resources Collaboratory) was employed for the automated segmentation. Pearson correlation coefficient was used to assess volumetric comparison. Subsequently deviation/range and average discrepancy were determined. The Wilcoxon signed-rank test was used to assess statistical significance.

RESULTS

A total of 58 patients with a newly diagnosed high-grade glioma were enrolled. The comparison of the volumes calculated with Horos and IPlan showed a strong agreement both on preoperative and postoperative images (respectively: "enhancing" ρ = 0.99-0.78, "fluid-attenuated inversion recovery" ρ = 0.97-0.92, and "total tumor volume" ρ = 0.98-0.95). Agreement between BraTumIA and the other 2 techniques appeared to be strong for preoperative images, but showed a higher disagreement on postoperative images. Mean time expenditure for tumor segmentation was 27 min with manual segmentation, 17 min with semi-automated, and 8 min with automated software.

CONCLUSION

The considered segmentation tools showed high agreement in preoperative volumetric assessment. Both manual and semi-automated software appear adequate for the postoperative quantification of residual volume. The evaluated automated software is not yet reliable. Automated software considerably reduces the time expenditure.

Image Analysis Reveals Microstructural and Volumetric Differences in Glioblastoma Patients with and without Preoperative Seizures

Purpose: Seizures related to tumor growth are common in glioma patients, especially in low-grade glioma patients this is often the first tumor manifestation. We hypothesize that there are associations between preoperative seizures and morphologic features (e.g., tumor size, location) and histogram features in patients with glioblastoma (GB). Methods: Retrospectively, 160 consecutive patients with initial diagnosis and surgery of GB (WHO IV) and preoperative MRI were analyzed. Preoperative MRI sequences were co-registered (T2-FLAIR, T1-contrast, DTI) and tumors were segmented by a neuroradiologist using the software ITK-snap blinded to the clinical data. Tumor volume (FLAIR, T1-contrast) and histogram analyses of ADC- and FA-maps were recorded in the contrast enhancing tumor part (CET) and the non-enhancing peritumoral edema (FLAIR). Location was determined after co-registration of the data with an atlas. Permutation-based multiple-testing adjusted t statistics were calculated to compare imaging variables between patients with and without seizures. Results: Patients with seizures showed significantly smaller tumors (CET, adj. p = 0.029) than patients without preoperative seizures. Less seizures were observed in patients with tumor location in the right cingulate gyrus (adj. p = 0.048) and in the right caudate nucleus (adj. p = 0.009). Significant differences of histogram analyses of FA in the contrast enhancing tumor part were observed between patients with and without seizures considering also tumor location and size. Conclusion: Preoperative seizures in GB patients are associated with lower preoperative tumor volume. The different histogram analyses suggest that there might be microstructural differences in the contrast enhancing tumor part of patients with seizures measured by fractional anisotropy. Higher variance of GB presenting without seizures might indicate a more aggressive growth of these tumors.

Brain Tumor-Enhancement Visualization and Morphometric Assessment: A Comparison of MPRAGE, SPACE, and VIBE MRI Techniques

BACKGROUND AND PURPOSE

Postgadolinium MR imaging is crucial for brain tumor diagnosis and morphometric assessment. We compared brain tumor enhancement visualization and the "target" object morphometry obtained with the most commonly used 3D MR imaging technique, MPRAGE, with 2 other routinely available techniques: sampling perfection with application-optimized contrasts by using different flip angle evolutions (SPACE) and volumetric interpolated brain examination (VIBE).

MATERIALS AND METHODS

Fifty-four contrast-enhancing tumors (38 gliomas and 16 metastases) were assessed using MPRAGE, VIBE, and SPACE techniques randomly acquired after gadolinium-based contrast agent administration on a 3T scanner. Enhancement conspicuity was assessed quantitatively by calculating the contrast rate and contrast-to-noise ratio, and qualitatively, by consensus visual comparative ratings. The total enhancing tumor volume and between-sequence discrepancy in the margin delineation were assessed on the corresponding 3D target objects contoured with a computer-assisted software for neuronavigation. The Wilcoxon signed rank and Pearson χ² nonparametric tests were used to investigate between-sequence discrepancies in the contrast rate, contrast-to-noise ratio, visual conspicuity ratings, tumor volume, and margin delineation estimates. Differences were also tested for 1D (Response Evaluation Criteria in Solid Tumors) and 2D (Response Assessment in Neuro-Oncology) measurements.

RESULTS

Compared with MPRAGE, both SPACE and VIBE obtained higher contrast rate, contrast-to-noise ratio, and visual conspicuity ratings in both gliomas and metastases (P range, <.001-.001). The between-sequence 3D target object margin discrepancy ranged between 3% and 19.9% of lesion tumor volume. Larger tumor volumes, 1D and 2D measurements were obtained with SPACE (P range, <.01-.007).

CONCLUSIONS

Superior conspicuity for brain tumor enhancement can be achieved using SPACE and VIBE techniques, compared with MPRAGE. Discrepancies were also detected when assessing target object size and morphology, with SPACE providing more accurate estimates.

Effect of MRI-based semiautomatic size-assessment in cerebral metastases on the RANO-BM classification

AIM

Evaluation of a semiautomatic software algorithm for magnetic resonance imaging (MRI)-based assessment of cerebral metastases in cancer patients.

MATERIAL AND METHODS

Brain metastases (n = 131) in 38 patients, assessed by contrast-enhanced MRI, were retrospectively evaluated at two timepoints (baseline, follow-up) by two experienced neuroradiologists in a blinded manner. The response assessment in neuro-oncology (RANO) criteria for brain metastases (RANO-BM) were applied by means of a software (autoRANO-BM) as well as manually (manRANO-BM) at an interval of 3 weeks.

RESULTS

The average diameter of metastases was 12.03 mm (SD ± 6.66 mm) for manRANO-BM and 13.97 mm (SD ± 7.76 mm) for autoRANO-BM. Diameter figures were higher when using semiautomatic measurements (median = 11.8 mm) as compared to the manual ones (median = 10.2 mm; p = 0.000). Correlation coefficients for intra-observer variability were 0.993 (autoRANO-BM) and 0.979 (manRANO-BM). The interobserver variability (R1/R2) was 0.936/0.965 for manRANO-BM and 0.989/0.998 for autoRANO-BM. A total of 19 lesions (15%) were classified differently when using semiautomatic measurements. In 14 cases with suspected disease progression by manRANO-BM a stable course was found according to autoRANO-BM.

CONCLUSION

Computerized measuring techniques can aid in the assessment of cerebral metastases by reducing examiner-dependent effects and may consequently result in a different classification according to RANO-BM criteria.

Risk factors for neurocognitive impairment in patients with benign intracranial lesions

This study was designed to assess risk factors for neurocognitive impairment in patients with benign intracranial lesions including tumors and vascular lesions. 74 patients (29 m, 51 f, mean age 54.4 years) with surgery for benign intracranial lesions were included in this prospective single-center study. Extensive neuropsychological testing was performed preoperatively, including tests for attention, memory and executive functions. Furthermore, headache and depression were assessed using the german version of the HDI (IBK) and the BDI-II. Multiple linear regression analyses of the percentile ranks (adjusted for age, sex and education) including the parameters age, Karnofsky Performance Status Scale (KPS), mood, pain and lesion size were performed to identify risk factors for cognitive impairment. Using the Mann-Whitney U test, the influence of hemisphere and type of lesion (tumor/vascular) was assessed. Posthoc Bonferroni correction was performed. Poorer neurocognitive functions were observed only in the category attention in patients with higher age (divided attention, WMS) and reduced KPS (WMS). Lesion volume, mood, pain, hemisphere or the type of the lesion (tumor, vascular) were not identified as risk factors for poorer neurocognitive functions in patients with benign intracranial lesions. Age and KPS are the main risk factors for poorer neurocognitive functions in the category attention in patients with benign intracranial lesions. Knowledge of these risk factors might be important to find appropriate therapy regimes to improve cognitive functions and quality of life.

Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine

Background

Magnetic resonance imaging (MRI) is the modality of choice for diagnosing and monitoring muscular tissue pathologies and bone marrow alterations in the context of lower back pain, neuromuscular diseases and osteoporosis. Chemical shift encoding-based water-fat MRI allows for reliable determination of proton density fat fraction (PDFF) of the muscle and bone marrow. Prior to quantitative data extraction, segmentation of the examined structures is needed. Performed manually, the segmentation process is time consuming and therefore limiting the clinical applicability. Thus, the development of automated segmentation algorithms is an ongoing research focus.

Construction and content

This database provides ground truth data which may help to develop and test automatic lumbar muscle and vertebra segmentation algorithms. Lumbar muscle groups and vertebral bodies (L1 to L5) were manually segmented in chemical shift encoding-based water-fat MRI and made publically available in the database MyoSegmenTUM. The database consists of water, fat and PDFF images with corresponding segmentation masks for lumbar muscle groups (right/left erector spinae and psoas muscles, respectively) and lumbar vertebral bodies 1–5 of 54 healthy Caucasian subjects. The database is freely accessible online at https://osf.io/3j54b/?view_only=f5089274d4a449cda2fef1d2df0ecc56.

Conclusion

A development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine.

Inter-rater agreement in glioma segmentations on longitudinal MRI

Background

Tumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate.

Methods

In 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement.

Results

For glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year.

Conclusion

Manual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts.

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Inter-rater agreement for longitudinal glioma segmentation was determined.

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Agreement between 4 experts was higher than between 4 novices.

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Three time-points of glioblastoma (WHO IV) and diffuse glioma (WHO II-III) are studied.

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Impact on extent of resection and growth rate measurements was determined.

Determining a cut-off residual tumor volume threshold for patients with newly diagnosed glioblastoma treated with temozolomide chemoradiotherapy: A multicenter cohort study

Standard-of-care treatment of glioblastomas involves maximal safe resection and adjuvant temozolomide chemo-radiotherapy. Although extent of resection (EOR) is a well-known surgical predictor for overall survival most lesions cannot be completely resected. We hypothesize that in the event of incomplete resection, residual tumor volume (RTV) may be a more significant predictor than EOR. This was a multicenter retrospective review of 147 adult glioblastoma patients (mean age 53 years) that underwent standard treatment. Semiautomatic magnetic resonance imaging segmentation was performed for pre- and postoperative scans for volumetric analysis. Cox proportional hazards regression and Kaplan-Meier survival analyses were performed for prognostic factors including: age, Karnofsky performance score (KPS), O(6)-methylguanine methyltransferase (MGMT) promoter methylation status, EOR and RTV. EOR and RTV cut-off values for improved OS were determined and internally validated by receiver operator characteristic (ROC) analysis for 12-month overall survival. Half of the tumors had MGMT promoter methylation (77, 52%). The median tumor volume, EOR and RTV were 43.20 cc, 93.5%, and 3.80 cc respectively. Gross total resection was achieved in 52 patients (35%). Cox proportional hazards regression, ROC and maximum Youden index analyses for RTV and EOR showed that a cut-off value of <3.50 cc (HR 0.69; 95% CI 0.48-0.98) and ≥84% (HR 0.64; 95% CI 0.43-0.96) respectively conferred an overall survival advantage. Independent overall survival predictors were MGMT promoter methylation (adjusted HR 0.35; 95% CI 0.23-0.55) and a RTV of <3.50 cc (adjusted HR 0.53; 95% CI 0.29-0.95), but not EOR for incompletely resected glioblastomas.

Monitoring Radiation Treatment Effects in Glioblastoma: FLAIR Volume as Significant Predictor of Survival

Glioblastoma is the most common adult central nervous system malignancy and carries a poor prognosis. Disease progression and recurrence after chemoradiotherapy are assessed via serial magnetic resonance imaging sequences. T2-weighted fluid-attenuated inversion recovery (FLAIR) signal is presumed to represent edema containing microscopic cancer infiltration. Here we assessed the prognostic impact of computerized volumetry of FLAIR signal in the peri-treatment setting for glioblastoma. We analyzed pre- and posttreatment FLAIR sequences of 40 patients treated at the Columbia University Medical Center between 2011 and 2014, excluding those without high-quality FLAIR imaging within 2 weeks before treatment and 60 to 180 days afterward. We manually contoured regions of FLAIR hyperintensity as per Radiation Therapy Oncology Group guidelines and calculated the volumes of nonenhancing tumor burden. At the time of this study, all but 1 patient had died. Pre- and posttreatment FLAIR volumes were assessed for correlation to overall and progression-free survival. Larger post-treatment FLAIR volumes from sequences taken between 60 and 180 days after conclusion of chemoradiotherapy were negatively correlated with overall survival (P = .048 on Pearson's correlation and P = .017 and P = .043 on univariable and multivariable Cox regression analyses, respectively) and progression-free survival (P = .002 on Pearson's correlation and P = < .001 and P = < .001 on univariable and multivariable Cox regression analyses). This study suggests that higher FLAIR volumes in the 2- to 6-month posttreatment window are associated with worsened survival.

Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry

Purpose

Unambiguous evaluation of glioblastoma (GB) progression is crucial, both for clinical trials as well as day by day routine management of GB patients. 3D-volumetry in the follow-up of GB provides quantitative data on tumor extent and growth, and therefore has the potential to facilitate objective disease assessment. The present study investigated the utility of absolute changes in volume (delta) or regional, segmentation-based subtractions for detecting disease progression in longitudinal MRI follow-ups.

Methods

165 high resolution 3-Tesla MRIs of 30 GB patients (23m, mean age 60.2y) were retrospectively included in this single center study. Contrast enhancement (CV) and tumor-related signal alterations in FLAIR images (FV) were semi-automatically segmented. Delta volume (dCV, dFV) and regional subtractions (sCV, sFV) were calculated. Disease progression was classified for every follow-up according to histopathologic results, decisions of the local multidisciplinary CNS tumor board and a consensus rating of the neuro-radiologic report.

Results

A generalized logistic mixed model for disease progression (yes / no) with dCV, dFV, sCV and sFV as input variables revealed that only dCV was significantly associated with prediction of disease progression (P = .005). Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75).

Conclusion

Absolute volume changes of the contrast enhancing tumor part were the most accurate volumetric determinant to detect progressive disease in assessment of GB and outweighed FLAIR changes as well as regional, segmentation-based image subtractions. This parameter might be useful in upcoming objective response criteria for glioblastoma.

Different Volumetric Measurement Methods for Pituitary Adenomas and Their Crucial Clinical Significance

Confirming the status of residual tumors is crucial. In stationary or spontaneous regression cases, early treatments are inappropriate. The long-used geometric calculation formula is 1/2 (length × width × height). However, it yields only rough estimates and is particularly unreliable for irregularly shaped masses. In our study, we attempted to propose a more accurate method. Between 2004 and 2014, 94 patients with pituitary tumors were enrolled in this retrospective study. All patients underwent transsphenoidal surgery and received magnetic resonance imaging (MRI). The pre- and postoperative volumes calculated using the traditional formula were termed A1 and A2, and those calculated using the proposed method were termed O1 and O2, respectively. Wilcoxon signed rank test revealed no significant difference between the A1 and O1 groups (P = 0.1810) but a significant difference between the A2 and O2 groups (P < 0.0001). Significant differences were present in the extent of resection (P < 0.0001), high-grade cavernous sinus invasion (P = 0.0312), and irregular shape (P = 0.0116). Volume is crucial in evaluating tumor status and determining treatment. Therefore, a more scientific method is especially useful when lesions are irregularly shaped or when treatment is determined exclusively based on the tumor volume.

Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded

Objective

Comparison of a fully-automated segmentation method that uses compartmental volume information to a semi-automatic user-guided and FDA-approved segmentation technique.

Methods

Nineteen patients with a recently diagnosed and histologically confirmed glioblastoma (GBM) were included and MR images were acquired with a 1.5 T MR scanner. Manual segmentation for volumetric analyses was performed using the open source software 3D Slicer version 4.2.2.3 (www.slicer.org). Semi-automatic segmentation was done by four independent neurosurgeons and neuroradiologists using the computer-assisted segmentation tool SmartBrush® (referred to as SB), a semi-automatic user-guided and FDA-approved tumor-outlining program that uses contour expansion. Fully automatic segmentations were performed with the Brain Tumor Image Analysis (BraTumIA, referred to as BT) software. We compared manual (ground truth, referred to as GT), computer-assisted (SB) and fully-automated (BT) segmentations with regard to: (1) products of two maximum diameters for 2D measurements, (2) the Dice coefficient, (3) the positive predictive value, (4) the sensitivity and (5) the volume error.

Results

Segmentations by the four expert raters resulted in a mean Dice coefficient between 0.72 and 0.77 using SB. BT achieved a mean Dice coefficient of 0.68. Significant differences were found for intermodal (BT vs. SB) and for intramodal (four SB expert raters) performances. The BT and SB segmentations of the contrast-enhancing volumes achieved a high correlation with the GT. Pearson correlation was 0.8 for BT; however, there were a few discrepancies between raters (BT and SB 1 only). Additional non-enhancing tumor tissue extending the SB volumes was found with BT in 16/19 cases. The clinically motivated sum of products of diameters measure (SPD) revealed neither significant intermodal nor intramodal variations. The analysis time for the four expert raters was faster (1 minute and 47 seconds to 3 minutes and 39 seconds) than with BT (5 minutes).

Conclusion

BT and SB provide comparable segmentation results in a clinical setting. SB provided similar SPD measures to BT and GT, but differed in the volume analysis in one of the four clinical raters. A major strength of BT may its independence from human interactions, it can thus be employed to handle large datasets and to associate tumor volumes with clinical and/or molecular datasets ("-omics") as well as for clinical analyses of brain tumor compartment volumes as baseline outcome parameters. Due to its multi-compartment segmentation it may provide information about GBM subcompartment compositions that may be subjected to clinical studies to investigate the delineation of the target volumes for adjuvant therapies in the future.

Value of Early Postoperative FLAIR Volume Dynamic in Glioma with No or Minimal Enhancement

OBJECTIVE

The evaluation of postoperative magnetic resonance imaging (MRI) in glioma with no or minimal enhancement is controversial because the evaluation of residual tumor volume can be biased. The purpose of this study was to clarify the value of early postoperative and 3-month MRI regarding its validity in predicting recurrent disease.

METHODS

For this retrospective, single-center study, overall fluid attenuated inversion recovery (FLAIR) volumes (early postoperative [<48 hours] and 3-month MRI including FLAIR and T1-weighted sequences with and without contrast agent) of 99 patients were assessed using manual segmentation. FLAIR volume dynamic over the first 3 months after surgery and its effect on disease recurrence were evaluated while considering histopathologic features.

RESULTS

Overall FLAIR-hyperintense volume significantly decreased between early postoperative and 3-month follow-up MRIs (P < 0.001). Early FLAIR volume increase had a high positive predictive value for overall disease recurrence after resection (85.71% [95%-CI: 62.64-96.24]). Early FLAIR volume dynamic (P < 0.001), isocitrate dehydrogenase 1/2 status (P = 0.002), and preoperative Karnofsky Performance Status (P = 0.012) were observed as independent factors for progression-free survival in multivariate analysis.

CONCLUSION

Early postoperative FLAIR volume assessment in gliomas with no or minimal enhancement is susceptible to a systematic overestimation of residual tumors. Nevertheless, early FLAIR volume dynamic is an independent factor for tumor recurrence that should be evaluated in order timely adapt surveillance and therapy regimens accordingly.