Dataset Information

Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging.

ABSTRACT: Histopathologic evaluation of glioblastoma multiforme (GBM) at initial diagnosis is typically performed on tissue obtained from regions of contrast enhancement (CE) as depicted on gadolinium-enhanced, T1-weighted images. The non-enhancing (NE) portion of the lesion, which contains both reactive edema and infiltrative tumor, is only partially removed due to concerns about damaging functioning brain. The purpose of this study was to evaluate histopathologic and physiologic MRI features of image-guided tissue specimens from CE and NE regions to investigate correlations between imaging and histopathologic parameters. One hundred nineteen tissue specimens (93 CE and 26 NE regions) were acquired from 51 patients with newly diagnosed GBM by utilizing stereotactic image-guided sampling. Variables of anatomic, diffusion-weighted imaging (DWI), and dynamic susceptibility-weighted, contrast-enhanced perfusion imaging (DSC) from each tissue sample location were obtained and compared with histopathologic features such as tumor score, cell density, proliferation, architectural disruption, hypoxia, and microvascular hyperplasia. Tissue samples from CE regions had increased tumor score, cellular density, proliferation, and architectural disruption compared with NE regions. DSC variables such as relative cerebral blood volume, peak height, and recovery factor were significantly higher, and the percentage of signal intensity recovery was significantly lower in the CE compared with the NE regions. DWI variables were correlated with histopathologic features of GBM within NE regions. Image-guided tissue acquisition and assessment of residual tumor from treatment-naive GBM should be guided by DSC in CE regions and by DWI in NE regions.

SUBMITTER: Barajas RF

PROVIDER: S-EPMC3379808 | biostudies-other | 2012 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging.

Barajas Ramon F RF Phillips Joanna J JJ Parvataneni Rupa R Molinaro Annette A Essock-Burns Emma E Bourne Gabriela G Parsa Andrew T AT Aghi Manish K MK McDermott Michael W MW Berger Mitchel S MS Cha Soonmee S Chang Susan M SM Nelson Sarah J SJ

Neuro-oncology 20120618 7

Histopathologic evaluation of glioblastoma multiforme (GBM) at initial diagnosis is typically performed on tissue obtained from regions of contrast enhancement (CE) as depicted on gadolinium-enhanced, T1-weighted images. The non-enhancing (NE) portion of the lesion, which contains both reactive edema and infiltrative tumor, is only partially removed due to concerns about damaging functioning brain. The purpose of this study was to evaluate histopathologic and physiologic MRI features of image-gu ...[more]

PMID: 22711606

Similar Datasets

Project description:Purpose To assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer. Materials and Methods After ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded. Results Repeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater. Conclusion MR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice. Online supplemental material is available for this article.

Project description:ObjectivesTo identify qualitative VASARI (Visually AcceSIble Rembrandt Images) Magnetic Resonance (MR) Imaging features for differentiation of glioblastoma (GBM) and brain metastasis (BM) of different primary tumors.Materials and methodsT1-weighted pre- and post-contrast, T2-weighted, and T2-weighted, fluid attenuated inversion recovery (FLAIR) MR images of a total of 239 lesions from 109 patients with either GBM or BM (breast cancer, non-small cell (NSCLC) adenocarcinoma, NSCLC squamous cell carcinoma, small-cell lung cancer (SCLC)) were included. A set of adapted, qualitative VASARI MR features describing tumor appearance and location was scored (binary; 1 = presence of feature, 0 = absence of feature). Exploratory data analysis was performed on binary scores using a combination of descriptive statistics (proportions with 95% binomial confidence intervals), unsupervised methods and supervised methods including multivariate feature ranking using either repeated fitting or recursive feature elimination with Support Vector Machines (SVMs).ResultsGBMs were found to involve all lobes of the cerebrum with a fronto-occipital gradient, often affected the corpus callosum (32.4%, 95% CI 19.1-49.2), and showed a strong preference for the right hemisphere (79.4%, 95% CI 63.2-89.7). BMs occurred most frequently in the frontal lobe (35.1%, 95% CI 28.9-41.9) and cerebellum (28.3%, 95% CI 22.6-34.8). The appearance of GBMs was characterized by preference for well-defined non-enhancing tumor margin (100%, 89.8-100), ependymal extension (52.9%, 36.7-68.5) and substantially less enhancing foci than BMs (44.1%, 28.9-60.6 vs. 75.1%, 68.8-80.5). Unsupervised and supervised analyses showed that GBMs are distinctively different from BMs and that this difference is driven by definition of non-enhancing tumor margin, ependymal extension and features describing laterality. Differentiation of histological subtypes of BMs was driven by the presence of well-defined enhancing and non-enhancing tumor margins and localization in the vision center. SVM models with optimal hyperparameters led to weighted F1-score of 0.865 for differentiation of GBMs from BMs and weighted F1-score of 0.326 for differentiation of BM subtypes.ConclusionVASARI MR imaging features related to definition of non-enhancing margin, ependymal extension, and tumor localization may serve as potential imaging biomarkers to differentiate GBMs from BMs.

Dataset Information

Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging.

Publications

Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets