Supratentorial Low-Grade Glioma Resectability: Statistical Predictive Analysis Based on Anatomic MR Features and Tumor Characteristics

Talos, Ion Florin; Zou, Kelly H.; Ohno-Machado, Lucila; Bhagwat, Jui G.; Kikinis, Ron; Black, Peter McL.; Jólesz, Ferenc A.

doi:10.1148/radiol.2392050661

Cited by 91 publications

(58 citation statements)

References 38 publications

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“…Finally, no special care was taken to exclude rCBV pixels from large vessels within the segmented glioma volume. Although large vessels can be appreciated as dark areas on T2-w images and thus should be excluded during the knowledge-based operations (1,3,29), it is well known that the T2*-effect from large vessels in gradient-echo echo-planar perfusion imaging result in an overestimation of the intravascular susceptibility effect in pixels adjacent to large vessels. The consequence of this is overestimated rCBV values in pixels not recognized as vessel tissue by the segmentation procedure.…”

Section: Discussionmentioning

confidence: 99%

“…From Equation [1], a perfect circle will return a circularity value of 1 whereas a straight line will result in a circularity value close to zero. Across all slices, First Tumor was identified as the binary object with the highest circularity value (i.e., the most compact object).…”

Section: Morphological Operationsmentioning

confidence: 99%

“…The high level of soft tissue contrast makes MR images an important aid in the identification and treatment of gliomas (1)(2)(3). Furthermore, in combination with conventional MR imaging, several studies have shown that dynamic susceptibility contrast (DSC) MR imaging of gliomas is a good presurgical indicator of glioma grade (4 -6) and patient outcome regardless of glioma grade (7,8).…”

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confidence: 99%

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Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

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Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

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Section: Discussionmentioning

confidence: 99%

Section: Morphological Operationsmentioning

confidence: 99%

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confidence: 99%

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Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

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Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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“…Such a quantitative measure may help improve the accuracy of currently available predictive models for brain tumor resectability [29].…”

Section: Discussionmentioning

confidence: 99%

Volumetric Assessment of Tumor Infiltration of Adjacent White Matter Based on Anatomic MRI and Diffusion Tensor Tractography

et al. 2007

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Rationale and Objectives-To perform a retrospective, quantitative assessment of the anatomic relationship between intra-axial, supratentorial, primary brain tumors, and adjacent white matter fiber tracts, based on anatomic and diffusion tensor MRI. We hypothesized that white matter infiltration may be common among different types of tumor.Material and Methods-Preoperative, anatomic (T1-and T2-weighted) and LINESCAN diffusion tensor MRI were obtained in 12 patients harboring supratentorial gliomas (WHO grade II and III). The two imaging modalities were rigidly registered. The tumors were manually segmented from the T1-and T2-weighted MRI, and their volume calculated. A three-dimensional tractography was performed in each case. A second segmentation and volume measurement was performed on the tumor regions intersecting adjacent white matter fiber tracts. Statistical methods included summary statistics to examine the fraction of tumor volume infiltrating adjacent white matter.Results-There were five patients with low-grade oligodendroglioma (WHO grade II), one with low-grade mixed oligoastrocytoma (WHO grade II), one with ganglioglioma, two with low-grade astrocytoma (WHO II), and three with anaplastic astrocytoma (WHO grade III). We identified white matter tracts infiltrated by tumor in all 12 cases. The median tumor volume (± standard deviation) in our patient population was 42.5 ± 28.9 ml. The median tumor volume (± standard deviation) infiltrating white matter fiber tracts was 5.2 ± 9.9 ml. The median percentage of tumor volume infiltrating white matter fiber tracts was 21.4 % ± 9.7 %.Conclusions-The information provided by diffusion tensor imaging combined with anatomic MRI might be useful for neurosurgical planning and intraoperative guidance. Our results confirm previous reports that extensive white matter infiltration by primary brain tumors is a common occurrence. However, prospective, large population studies are required to definitively clarify this issue, and how infiltration relates to histologic tumor type, tumor size and location. KeywordsBrain tumor; diffusion tensor imaging; anatomic MRI; 3D-tractography; tumor infiltration of white matter aCorrespondence to: Ion-Florin Talos, M.D., Instructor in Radiology, Brigham and Women

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“…Aggressive resection can increase the risk for postoperative neurological deficits (7). For example, brain tumors located near critical brain areas subserving primary motor, sensory, or language functions are difficult to resect maximally while avoiding postoperative neurological deficits (8). Therefore, the principal challenge and objective of neurosurgical intervention is to maximize the resection of tumor, and minimize the potential for neurological deficit by preserving critical tissue.…”

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confidence: 99%

Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors

Eberlin

Norton²,

Orringer³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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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.

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Supratentorial Low-Grade Glioma Resectability: Statistical Predictive Analysis Based on Anatomic MR Features and Tumor Characteristics

Abstract: The main variables associated with incomplete tumor resection in 101 patients were identified by using statistical predictive analyses.

Cited by 91 publications

References 38 publications

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Volumetric Assessment of Tumor Infiltration of Adjacent White Matter Based on Anatomic MRI and Diffusion Tensor Tractography

Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors

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