Brain Tumor Resection

The patients who benefit the most from AMIGO are in whom the distinction between tumor and brain and between different critical brain regions is most difficult. This occurs particularly in patients who have low grade gliomas, because although visible on certain imaging sequences, these tumors are nearly indistinguishable from surrounding brain during surgery even through the operating microscope. Moreover, visually all of the cortex and white matter look the same, and the surgeon can not discern the presence of white matter tracts or important areas during resection. In the AMIGO suite we can tie together preoperative mapping, accomplish intraoperative electrophysiology mapping, and obtain new US, MR, CT and PET images as needed.

Brain Tumor Resection Workflow in AMIGO

Craniotomy. Image guidance is used to perform a minimal craniotomy with optimized exposure of the lesion.
Ultrasound. When the dura is exposed, US is performed prior to making any incisions. US provides a fast initial orientation, including the location of major blood vessels. On left, the surgeon is using the BrainLab navigation system integrated with the BK US, and on the right is US with color doppler mode.
ECS. In a very small subset of cases, after the dura is opened and the cortex is exposed, intracranial electrical stimulation testing (ECS) is performed. ECS uses voltage applied directly to the cortex to map important functional areas. This is valuable in confirming and applying preoperative fMRI findings.
Navigation. Throughout the procedure, the pre-operative multimodal image data is used to navigate.
Navigation. Information is available to the surgeon about location and trajectory of her tools.
Tractography. Visualization of the tumor relative to the arcuate fasciculus white matter tract.
Gross Mass Removal. Gross tumor removal is performed using conventional tools aided by iterative neuro navigation. A cauterizer is shown in the picture.
Gross Mass Removal. Ablation and aspiration of tissue is shown.
Gross Mass Removal. Image guidance makes effective tumor resection possible.
Intraoperative MR. Prior to intraoperative MRI, a temporary closure is performed.
Intraoperative MR. A ceiling mounted high field (3T) MR scanner is then brought into the OR.
Tumor assessment. The tumor is contoured in green on the pre-op MRI image.
Assessment of residual tumor. The residual tumor is contoured in red.
Closure and post operative confirmatory imaging. Once the surgeon is satisfied with the extent of tumor resection, the dura is stitched, skull plate replaced, and skin stitched. Post-operative MR scans are obtained to confirm that there are no intraoperative complications and to set a new baseline. Once conscious, the patient is immediately asked to demonstrate motor control, such as foot movement. This confirms that resection has not affected at-risk areas of the motor cortex.

Book

  • Golby, Alexandra J, ed. Image-Guided Neurosurgery. 1st ed. Vol. Image-Guided Neurosurgery. Academic Press, 2015. p. 536. Print.

Book Chapters

  • Ferenc A. Jolesz, Alexandra J. Golby, Daniel A. Orringer. Magnetic Resonance Image-Guided Neurosurgery. Ch.32. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 451-64.
  • Isaiah H. Norton, Daniel A. Orringer, Alexandra J. Golby. Image-Guided Neurosurgical Planning. Ch.37. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 507-18.
  • Nobuhiko Hata, Paul R. Morrison, Zsolt Cselik, Ron Kikinis, Peter McL. Black, and Ferenc A. Jolesz. MRI-Guided and Controlled Laser-Induced Interstitial Thermal Therapy of Brain Tumors Using Integrated Navigation and Thermal Mapping Ch.42. Part V. Image-Guided Clinical Applications. In Ferenc A. Jolesz (Ed.), Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014. pp. 567-74.

Select Publications

Thomas Noh, Martina Mustroph, and Alexandra J Golby. 2021. “Intraoperative Imaging for High-Grade Glioma Surgery.” Neurosurg Clin N Am, 32, 1, Pp. 47-54.Abstract
This article discusses intraoperative imaging techniques used during high-grade glioma surgery. Gliomas can be difficult to differentiate from surrounding tissue during surgery. Intraoperative imaging helps to alleviate problems encountered during glioma surgery, such as brain shift and residual tumor. There are a variety of modalities available all of which aim to give the surgeon more information, address brain shift, identify residual tumor, and increase the extent of surgical resection. The article starts with a brief introduction followed by a review of with the latest advances in intraoperative ultrasound, intraoperative MRI, and intraoperative computed tomography.
Luca Canalini, Jan Klein, Dorothea Miller, and Ron Kikinis. 12/2020. “Enhanced Registration of Ultrasound Volumes by Segmentation of Resection Cavity in Neurosurgical Procedures.” Int J Comput Assist Radiol Surg, 15, 12, Pp. 1963-74.Abstract
PURPOSE: Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. METHODS: The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. RESULTS: Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. CONCLUSIONS: We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.
Saramati Narasimhan, Jared A Weis, Ma Luo, Amber L Simpson, Reid C Thompson, and Michael I Miga. 5/2020. “Accounting for Intraoperative Brain Shift Ascribable to Cavity Collapse During Intracranial Tumor Resection.” J Med Imaging (Bellingham), 7, 3, Pp. 031506.Abstract
For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.
Sarah Frisken, Ma Luo, Parikshit Juvekar, Adomas Bunevicius, Ines Machado, Prashin Unadkat, Melina M Bertotti, Matt Toews, William M Wells, Michael I Miga, and Alexandra J Golby. 1/2020. “A Comparison of Thin-Plate Spline Deformation and Finite Element Modeling to Compensate for Brain Shift during Tumor Resection.” Int J Comput Assist Radiol Surg, 15, 1, Pp. 75-85.Abstract
PURPOSE: Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data. METHODS: Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation. RESULTS: The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods. CONCLUSIONS: In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.
Ma Luo, Sarah F Frisken, Jared A Weis, Logan W Clements, Prashin Unadkat, Reid C Thompson, Alexandra J Golby, and Michael I Miga. 2017. “Retrospective Study Comparing Model-Based Deformation Correction to Intraoperative Magnetic Resonance Imaging for Image-Guided Neurosurgery.” J Med Imaging (Bellingham), 4, 3, Pp. 035003.Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
Rahul Sastry, Wenya Linda Bi, Steve Pieper, Sarah Frisken, Tina Kapur, William Wells, and Alexandra J Golby. 2017. “Applications of Ultrasound in the Resection of Brain Tumors.” J Neuroimaging, 27, 1, Pp. 5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

David Calligaris, Daniel R Feldman, Isaiah Norton, Priscilla K Brastianos, Ian F Dunn, Sandro Santagata, and Nathalie YR Agar. 2015. “Molecular Typing of Meningiomas by Desorption Electrospray Ionization Mass Spectrometry Imaging for Surgical Decision-Making.” Int J Mass Spectrom, 377, Pp. 690-8.Abstract

Meningiomas are the most frequent intracranial tumors. The majority is benign slow-growing tumors but they can be difficult to treat depending on their location and size. While meningiomas are well delineated on magnetic resonance imaging by their uptake of contrast, surgical limitations still present themselves from not knowing the extent of invasion of the dura matter by meningioma cells. The development of tools to characterize tumor tissue in real or near real time could prevent recurrence after tumor resection by allowing for more precise surgery, i.e. removal of tumor with preservation of healthy tissue. The development of ambient ionization mass spectrometry for molecular characterization of tissue and its implementation in the surgical decision-making workflow carry the potential to fulfill this need. Here, we present the characterization of meningioma and dura mater by desorption electrospray ionization mass spectrometry to validate the technique for the molecular assessment of surgical margins and diagnosis of meningioma from surgical tissue in real-time. Nine stereotactically resected surgical samples and three autopsy samples were analyzed by standard histopathology and mass spectrometry imaging. All samples indicated a strong correlation between results from both techniques. We then highlight the value of desorption electrospray ionization mass spectrometry for the molecular subtyping/subgrouping of meningiomas from a series of forty genetically characterized specimens. The minimal sample preparation required for desorption electrospray ionization mass spectrometry offers a distinct advantage for applications relying on real-time information such as surgical decision-making. The technology here was tested to distinguish meningioma from dura mater as an approach to precisely define surgical margins. In addition we classify meningiomas into fibroblastic and meningothelial subtypes and more notably recognize meningiomas with NF2 genetic aberrations.

David Calligaris, Diana Caragacianu, Xiaohui Liu, Isaiah Norton, Christopher J Thompson, Andrea L Richardson, Mehra Golshan, Michael L Easterling, Sandro Santagata, Deborah A Dillon, Ferenc A Jolesz, and Nathalie YR Agar. 2014. “Application of Desorption Electrospray Ionization Mass Spectrometry Imaging in Breast Cancer Margin Analysis.” Proc Natl Acad Sci U S A, 111, 42, Pp. 15184-9.Abstract

Distinguishing tumor from normal glandular breast tissue is an important step in breast-conserving surgery. Because this distinction can be challenging in the operative setting, up to 40% of patients require an additional operation when traditional approaches are used. Here, we present a proof-of-concept study to determine the feasibility of using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for identifying and differentiating tumor from normal breast tissue. We show that tumor margins can be identified using the spatial distributions and varying intensities of different lipids. Several fatty acids, including oleic acid, were more abundant in the cancerous tissue than in normal tissues. The cancer margins delineated by the molecular images from DESI-MSI were consistent with those margins obtained from histological staining. Our findings prove the feasibility of classifying cancerous and normal breast tissues using ambient ionization MSI. The results suggest that an MS-based method could be developed for the rapid intraoperative detection of residual cancer tissue during breast-conserving surgery.

Sandro Santagata, Livia S Eberlin, Isaiah Norton, David Calligaris, Daniel R Feldman, Jennifer L Ide, Xiaohui Liu, Joshua S Wiley, Matthew L Vestal, Shakti H Ramkissoon, Daniel A Orringer, Kristen K Gill, Ian F Dunn, Dora Dias-Santagata, Keith L Ligon, Ferenc A Jolesz, Alexandra J Golby, Graham R Cooks, and Nathalie YR Agar. 2014. “Intraoperative Mass Spectrometry Mapping of an Onco-metabolite to Guide Brain Tumor Surgery.” Proc Natl Acad Sci U S A, 111, 30, Pp. 11121-6.Abstract

For many intraoperative decisions surgeons depend on frozen section pathology, a technique developed over 150 y ago. Technical innovations that permit rapid molecular characterization of tissue samples at the time of surgery are needed. Here, using desorption electrospray ionization (DESI) MS, we rapidly detect the tumor metabolite 2-hydroxyglutarate (2-HG) from tissue sections of surgically resected gliomas, under ambient conditions and without complex or time-consuming preparation. With DESI MS, we identify isocitrate dehydrogenase 1-mutant tumors with both high sensitivity and specificity within minutes, immediately providing critical diagnostic, prognostic, and predictive information. Imaging tissue sections with DESI MS shows that the 2-HG signal overlaps with areas of tumor and that 2-HG levels correlate with tumor content, thereby indicating tumor margins. Mapping the 2-HG signal onto 3D MRI reconstructions of tumors allows the integration of molecular and radiologic information for enhanced clinical decision making. We also validate the methodology and its deployment in the operating room: We have installed a mass spectrometer in our Advanced Multimodality Image Guided Operating (AMIGO) suite and demonstrate the molecular analysis of surgical tissue during brain surgery. This work indicates that metabolite-imaging MS could transform many aspects of surgical care.

Livia S Eberlin, Isaiah Norton, Daniel Orringer, Ian F Dunn, Xiaohui Liu, Jennifer L Ide, Alan K Jarmusch, Keith L Ligon, Ferenc A Jolesz, Alexandra J Golby, Sandro Santagata, Nathalie YR Agar, and Graham R Cooks. 2013. “Ambient Mass Spectrometry for the Intraoperative Molecular Diagnosis of Human Brain Tumors.” Proc Natl Acad Sci U S A, 110, 5, Pp. 1611-6.Abstract

The main goal of brain tumor surgery is to maximize tumor resection while preserving brain function. However, existing imaging and surgical techniques do not offer the molecular information needed to delineate tumor boundaries. We have developed a system to rapidly analyze and classify brain tumors based on lipid information acquired by desorption electrospray ionization mass spectrometry (DESI-MS). In this study, a classifier was built to discriminate gliomas and meningiomas based on 36 glioma and 19 meningioma samples. The classifier was tested and results were validated for intraoperative use by analyzing and diagnosing tissue sections from 32 surgical specimens obtained from five research subjects who underwent brain tumor resection. The samples analyzed included oligodendroglioma, astrocytoma, and meningioma tumors of different histological grades and tumor cell concentrations. The molecular diagnosis derived from mass-spectrometry imaging corresponded to histopathology diagnosis with very few exceptions. Our work demonstrates that DESI-MS technology has the potential to identify the histology type of brain tumors. It provides information on glioma grade and, most importantly, may help define tumor margins by measuring the tumor cell concentration in a specimen. Results for stereotactically registered samples were correlated to preoperative MRI through neuronavigation, and visualized over segmented 3D MRI tumor volume reconstruction. Our findings demonstrate the potential of ambient mass spectrometry to guide brain tumor surgery by providing rapid diagnosis, and tumor margin assessment in near-real time.