Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials

Porto Alegre Line predicts lenticulostriate arteries encasement and extent of resection in insular gliomas. A preliminary study

Object

In insular glioma surgery, lenticulostriate arteries (LSTa) tumoral encasement increases neurological deficits risk despite intensive efforts to preserve the internal capsule's integrity. In this study, we focus on the LSTa relationships with the medial aspect of the insular tumors. We propose a new non-invasive method for LSTa involvement prediction in preoperative MRI (Porto Alegre Line). We compare it with direct intraoperative encased LSTa visualization.

Methods

A retrospective review of our database of 52 patients of insular glioma was performed. In cases with no tumor located medial to Porto Alegre line, our medial resection limit, mainly for the tumor part located next to the limen insula, was the inferior fronto-occipital fasciculus (IFOF), identified through altered speech patterns during electric subcortical stimulation. In cases with no assumed LSTa involvement, the parameter used to stop resection was the confirmation of the corticospinal tract with 10-mA stimulus. The resection limit of tumors placed medially to the Porto Alegre line was intraoperative direct LSTa visualization.

Results

The LSTa involvement was the most critical medial limiting factor in more aggressive tumor resection and an excellent overall survival (P = 0.022). In cases in which there were direct intraoperative LSTa encasement visualization, Porto Alegre Line was employed as an MRI preoperative landmark for prediction of LSTa involvement in those patients with Sensitivity, Specificity, Positive Predictive Values of 1, 0.975 and 0.923, respectively.

Conclusion

We have found that LSTa encasement is a limiting factor to reach a satisfactory extent of resection and that Porto Alegre Line can predict it.

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.

Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

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.

Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial

Despite aggressive treatment, glioblastoma has a poor prognosis due to its infiltrative nature. Spectroscopic MRI-measured brain metabolites, particularly the choline to N-acetylaspartate ratio (Cho/NAA), better characterizes the extent of tumor infiltration. In a previous pilot trial (NCT03137888), brain regions with Cho/NAA ≥ 2x normal were treated with high-dose radiation for newly diagnosed glioblastoma patients. This report is a secondary analysis of that trial where spectroscopic MRI-based biomarkers are evaluated for how they correlate with progression-free and overall survival (PFS/OS). Subgroups were created within the cohort based on pre-radiation treatment (pre-RT) median cutoff volumes of residual enhancement (2.1 cc) and metabolically abnormal volumes used for treatment (19.2 cc). We generated Kaplan-Meier PFS/OS curves and compared these curves via the log-rank test between subgroups. For the subgroups stratified by metabolic abnormality, statistically significant differences were observed for PFS (p = 0.019) and OS (p = 0.020). Stratification by residual enhancement did not lead to observable differences in the OS (p = 0.373) or PFS (p = 0.286) curves. This retrospective analysis shows that patients with lower post-surgical Cho/NAA volumes had significantly superior survival outcomes, while residual enhancement, which guides high-dose radiation in standard treatment, had little significance in PFS/OS. This suggests that the infiltrating, non-enhancing component of glioblastoma is an important factor in patient outcomes and should be treated accordingly.

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning.

Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography

Accurate and reproducible tissue identification is essential for understanding structural and functional changes that may occur naturally with aging, or because of a chronic disease, or in response to intervention therapies. Peripheral quantitative computed tomography (pQCT) is regularly employed for body composition studies, especially for the structural and material properties of the bone. Furthermore, pQCT acquisition requires low radiation dose and the scanner is compact and portable. However, pQCT scans have limited spatial resolution and moderate SNR. pQCT image quality is frequently degraded by involuntary subject movement during image acquisition. These limitations may often compromise the accuracy of tissue quantification, and emphasize the need for automated and robust quantification methods. We propose a tissue identification and quantification methodology that addresses image quality limitations and artifacts, with increased interest in subject movement. We introduce a multi-atlas image segmentation (MAIS) framework for semantic segmentation of hard and soft tissues in pQCT scans at multiple levels of the lower leg. We describe the stages of statistical atlas generation, deformable registration and multi-tissue classifier fusion. We evaluated the performance of our methodology using multiple deformable registration approaches against reference tissue masks. We also evaluated the performance of conventional model-based segmentation against the same reference data to facilitate comparisons. We studied the effect of subject movement on tissue segmentation quality. We also applied the top performing method to a larger out-of-sample dataset and report the quantification results. The results show that multi-atlas image segmentation with diffeomorphic deformation and probabilistic label fusion produces very good quality over all tissues, even for scans with significant quality degradation. The application of our technique to the larger dataset reveals trends of age-related body composition changes that are consistent with the literature. Because of its robustness to subject motion artifacts, our MAIS methodology enables analysis of larger number of scans than conventional state-of-the-art methods. Automated analysis of both soft and hard tissues in pQCT is another contribution of this work.

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.

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.

The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow

Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.

Review of clinical trials in intraoperative molecular imaging during cancer surgery

Most solid cancers are treated by surgical resections to reduce the burden of disease. Surgeons often face the challenge of detecting small areas of residual neoplasm after resection or finding small primary tumors for the initial resection. Intraoperative molecular imaging (IMI) is an emerging technology with the potential to dramatically improve cancer surgery operations by allowing surgeons to better visualize areas of neoplasm using fluorescence imaging. Over the last two years, two molecular optical contrast agents received U.S. Food and Drug Administration approval, and several more drugs are now on the horizon. Thus a conference was organized at the University of Pennsylvania to bring together oncologic surgeons from different specialties to discuss the current clinical status of IMI trials with a specific focus on phase 2 and phase 3 studies. In addition, phase 1 and experimental trials were also discussed briefly, to highlight other novel techniques. Our review summarizes the discussions from the conference and delves into the types of cancers discussed, different contrast agents in human trials, and the clinical value being studied.

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.

5-ALA and FDA approval for glioma surgery

The US Food and Drug Administration (FDA) approved 5-aminolevulinic acid (5-ALA; Gleolan^®; photonamic GmbH and Co. KG) for use as an intraoperative optical imaging agent in patients with suspected high-grade gliomas (HGGs) in 2017. This was the first ever optical imaging agent approved as an adjunct for the visualization of malignant tissue during surgery for brain tumors. The approval occurred a decade after European approval and a multicenter, phase III randomized trial which confirmed that surgeons using 5-ALA fluorescence-guided surgery as a surgical adjunct could achieve more complete resections of tumors in HGG patients and better patient outcomes than with conventional microsurgery. Much of the delay in the US FDA approval of 5-ALA stemmed from its conceptualization as a therapeutic and not as an intraoperative imaging tool. We chronicle the challenges encountered during the US FDA approval process to highlight a new standard for approval of intraoperative optical imaging agents in brain tumors.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Rank-Two NMF Clustering for Glioblastoma Characterization

This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix M from every MRI slice in every segmentation process' step. This matrix will be used as an input for the first segmentation process to extract the edema region from T2 and FLAIR modalities. After that, in the rest of segmentation processes, we extract the edema region from T1c modality, generate the matrix M, and segment the necrosis, the enhanced tumor, and the nonenhanced tumor regions. In the segmentation process, we apply a rank-two NMF clustering. We have executed our tumor characterization method on BraTS 2015 challenge dataset. Quantitative and qualitative evaluations over the publicly training and testing dataset from the MICCAI 2015 multimodal brain segmentation challenge (BraTS 2015) attested that the proposed algorithm could yield a competitive performance for brain glioblastomas characterization (necrosis, tumor core, and edema) among several competing methods.

Lesion segmentation for MR spectroscopic imaging using the convolution difference method

PURPOSE

Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion.

METHODS

A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out.

RESULTS

Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold.

CONCLUSION

This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.

Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery

PURPOSE

Glioblastoma (GBM) neurosurgical resection relies on contrast-enhanced MRI-based neuronavigation. However, it is well-known that infiltrating tumor extends beyond contrast enhancement. Fluorescence-guided surgery (FGS) using 5-aminolevulinic acid (5-ALA) was evaluated to improve extent of resection (EOR) of GBMs. Preoperative morphological tumor metrics were also assessed.

PROCEDURES

Thirty patients from a phase II trial evaluating 5-ALA FGS in newly diagnosed GBM were assessed. Tumors were segmented preoperatively to assess morphological features as well as postoperatively to evaluate EOR and residual tumor volume (RTV).

RESULTS

Median EOR and RTV were 94.3 % and 0.821 cm(3), respectively. Preoperative surface area to volume ratio and RTV were significantly associated with overall survival, even when controlling for the known survival confounders.

CONCLUSIONS

This study supports claims that 5-ALA FGS is helpful at decreasing tumor burden and prolonging survival in GBM. Moreover, morphological indices are shown to impact both resection and patient survival.

Rater Reliability of the Hardy Classification for Pituitary Adenomas in the Magnetic Resonance Imaging Era

Objectives  The Hardy classification is used to classify pituitary tumors for clinical and research purposes. The scale was developed using lateral skull radiographs and encephalograms, and its reliability has not been evaluated in the magnetic resonance imaging (MRI) era. Design  Fifty preoperative MRI scans of biopsy-proven pituitary adenomas using the sellar invasion and suprasellar extension components of the Hardy scale were reviewed. Setting  This study was a cohort study set at a single institution. Participants  There were six independent raters. Main Outcome Measures  The main outcome measures of this study were interrater reliability, intrarater reliability, and percent agreement. Results  Overall interrater reliability of both Hardy subscales on MRI was strong. However, reliability of the intermediate scores was weak, and percent agreement among raters was poor (12-16%) using the full scales. Dichotomizing the scale into clinically useful groups maintained strong interrater reliability for the sellar invasion scale and increased the percent agreement for both scales. Conclusion  This study raises important questions about the reliability of the original Hardy classification. Editing the measure to a clinically relevant dichotomous scale simplifies the rating process and may be useful for preoperative tumor characterization in the MRI era. Future research studies should use the dichotomized Hardy scale (sellar invasion Grades 0-III versus Grade IV, suprasellar extension Types 0-C versus Type D).

Interrater and intrarater reliability of the Knosp scale for pituitary adenoma grading

OBJECTIVE

The goal of this study was to determine the interrater and intrarater reliability of the Knosp grading scale for predicting pituitary adenoma cavernous sinus (CS) involvement.

METHODS

Six independent raters (3 neurosurgery residents, 2 pituitary surgeons, and 1 neuroradiologist) participated in the study. Each rater scored 50 unique pituitary MRI scans (with contrast) of biopsy-proven pituitary adenoma. Reliabilities for the full scale were determined 3 ways: 1) using all 50 scans, 2) using scans with midrange scores versus end scores, and 3) using a dichotomized scale that reflects common clinical practice. The performance of resident raters was compared with that of faculty raters to assess the influence of training level on reliability.

RESULTS

Overall, the interrater reliability of the Knosp scale was “strong” (0.73, 95% CI 0.56–0.84). However, the percent agreement for all 6 reviewers was only 10% (26% for faculty members, 30% for residents). The reliability of the middle scores (i.e., average rated Knosp Grades 1 and 2) was “very weak” (0.18, 95% CI −0.27 to 0.56) and the percent agreement for all reviewers was only 5%. When the scale was dichotomized into tumors unlikely to have intraoperative CS involvement (Grades 0, 1, and 2) and those likely to have CS involvement (Grades 3 and 4), the reliability was “strong” (0.60, 95% CI 0.39–0.75) and the percent agreement for all raters improved to 60%. There was no significant difference in reliability between residents and faculty (residents 0.72, 95% CI 0.55–0.83 vs faculty 0.73, 95% CI 0.56–0.84). Intrarater reliability was moderate to strong and increased with the level of experience.

CONCLUSIONS

Although these findings suggest that the Knosp grading scale has acceptable interrater reliability overall, it raises important questions about the “very weak” reliability of the scale's middle grades. By dichotomizing the scale into clinically useful groups, the authors were able to address the poor reliability and percent agreement of the intermediate grades and to isolate the most important grades for use in surgical decision making (Grades 3 and 4). Authors of future pituitary surgery studies should consider reporting Knosp grades as dichotomized results rather than as the full scale to optimize the reliability of the scale.

Automatic estimation of extent of resection and residual tumor volume of patients with glioblastoma

OBJECTIVE In the treatment of glioblastoma, residual tumor burden is the only prognostic factor that can be actively influenced by therapy. Therefore, an accurate, reproducible, and objective measurement of residual tumor burden is necessary. This study aimed to evaluate the use of a fully automatic segmentation method-brain tumor image analysis (BraTumIA)-for estimating the extent of resection (EOR) and residual tumor volume (RTV) of contrast-enhancing tumor after surgery. METHODS The imaging data of 19 patients who underwent primary resection of histologically confirmed supratentorial glioblastoma were retrospectively reviewed. Contrast-enhancing tumors apparent on structural preoperative and immediate postoperative MR imaging in this patient cohort were segmented by 4 different raters and the automatic segmentation BraTumIA software. The manual and automatic results were quantitatively compared. RESULTS First, the interrater variabilities in the estimates of EOR and RTV were assessed for all human raters. Interrater agreement in terms of the coefficient of concordance (W) was higher for RTV (W = 0.812; p < 0.001) than for EOR (W = 0.775; p < 0.001). Second, the volumetric estimates of BraTumIA for all 19 patients were compared with the estimates of the human raters, which showed that for both EOR (W = 0.713; p < 0.001) and RTV (W = 0.693; p < 0.001) the estimates of BraTumIA were generally located close to or between the estimates of the human raters. No statistically significant differences were detected between the manual and automatic estimates. BraTumIA showed a tendency to overestimate contrast-enhancing tumors, leading to moderate agreement with expert raters with respect to the literature-based, survival-relevant threshold values for EOR. CONCLUSIONS BraTumIA can generate volumetric estimates of EOR and RTV, in a fully automatic fashion, which are comparable to the estimates of human experts. However, automated analysis showed a tendency to overestimate the volume of a contrast-enhancing tumor, whereas manual analysis is prone to subjectivity, thereby causing considerable interrater variability.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T₂-hyperintense tumor compartments (NCE-T₂). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T₂-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T₂-hyperintense tumor compartments.

Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients

BACKGROUND

The standard of care for glioblastoma (GBM) is maximal safe resection followed by radiation therapy with chemotherapy. Currently, contrast-enhanced MRI is used to define primary treatment volumes for surgery and radiation therapy. However, enhancement does not identify the tumor entirely, resulting in limited local control. Proton spectroscopic MRI (sMRI), a method reporting endogenous metabolism, may better define the tumor margin. Here, we develop a whole-brain sMRI pipeline and validate sMRI metrics with quantitative measures of tumor infiltration.

METHODS

Whole-brain sMRI metabolite maps were coregistered with surgical planning MRI and imported into a neuronavigation system to guide tissue sampling in GBM patients receiving 5-aminolevulinic acid fluorescence-guided surgery. Samples were collected from regions with metabolic abnormalities in a biopsy-like fashion before bulk resection. Tissue fluorescence was measured ex vivo using a hand-held spectrometer. Tissue samples were immunostained for Sox2 and analyzed to quantify the density of staining cells using a novel digital pathology image analysis tool. Correlations among sMRI markers, Sox2 density, and ex vivo fluorescence were evaluated.

RESULTS

Spectroscopic MRI biomarkers exhibit significant correlations with Sox2-positive cell density and ex vivo fluorescence. The choline to N-acetylaspartate ratio showed significant associations with each quantitative marker (Pearson's ρ = 0.82, P < .001 and ρ = 0.36, P < .0001, respectively). Clinically, sMRI metabolic abnormalities predated contrast enhancement at sites of tumor recurrence and exhibited an inverse relationship with progression-free survival.

CONCLUSIONS

As it identifies tumor infiltration and regions at high risk for recurrence, sMRI could complement conventional MRI to improve local control in GBM patients.

Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

BACKGROUND

Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM.

METHODS

We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image.

RESULTS

Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively).

CONCLUSIONS

Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.