To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) ...imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.
Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.
Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).
This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.
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.
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.
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.
As it identifies tumor infiltration and regions at high risk for recurrence, sMRI could complement conventional MRI to improve local control in GBM patients.
To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers.
...An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests.
Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years).
Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.
OBJECTIVE Diagnostic algorithms for nontraumatic angiographically negative subarachnoid hemorrhage (AN-SAH) vary, and the optimal method remains subject to debate. This study assessed the added value ...of cervical spine MRI in identifying a cause for nontraumatic AN-SAH. METHODS Consecutive patients 18 years of age or older who presented with nontraumatic SAH between February 1, 2009, and October 31, 2014, with negative cerebrovascular catheter angiography and subsequent cervical MRI were studied. Patients with intraparenchymal, subdural, or epidural hemorrhage; recent trauma; or known vascular malformations were excluded. All cervical MR images were reviewed by two blinded neuroradiologists. The diagnostic yield of cervical MRI was calculated. A literature review was conducted to identify studies reporting the diagnostic yield of cervical MRI in patients with AN-SAH. The weighted pooled estimate of diagnostic yield of cervical MRI was calculated. RESULTS For all 240 patients (mean age 53 years, 48% male), catheter angiography was performed within 4 days after admission (median 12 hours, interquartile range IQR 10 hours). Cervical MRI was performed within 19 days of admission (median 24 hours, IQR 10 hours). In a single patient, cervical MRI identified a source for SAH (cervical vascular malformation). Meta-analysis of 7 studies comprising 538 patients with AN-SAH produced a pooled estimate of 1.3% (95% confidence interval 0.5%-2.5%) for diagnostic yield of cervical MRI. No statistically significant between-study heterogeneity or publication bias was identified. CONCLUSIONS Cervical MRI following AN-SAH, in the absence of findings to suggest spinal etiology, has a very low diagnostic yield and is not routinely necessary.
Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain ...largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways.
One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication.
Volumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10(-4)).
GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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).
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.
Introduction
MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted ...by MRI-derived tumor imaging features of glioblastoma (GBM).
Methods
Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status.
Results
Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (
p
= 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (
p
= 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature.
Conclusion
MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.
Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and ...metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype.
We retrospectively identified 104 treatment-naïve glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute).
Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A.
We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.
Summary Purpose The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with ...glioblastoma (GBM) than either individual data type. Methods The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive ( http://cancerimagingarchive.net ) of 102 GBM patients using the VASARI scoring system. The patients’ clinical and genetic data were obtained from the TCGA website ( http://www.cancergenome.nih.gov/ ). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. Results The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike's information criterion 566.7, P < 0.001). Conclusion A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.
Computed tomography (CT) of the cervical spine (C-spine) is routinely ordered for low-risk mechanisms of injury, including ground-level fall. Two commonly used clinical decision rules (CDRs) to guide ...C-spine imaging in trauma are the National Emergency X-Radiography Utilization Study (NEXUS) and the Canadian Cervical Spine Rule for Radiography (CCR).
Retrospective cross-sectional study of 3,753 consecutive adult patients presenting to an urban Level I emergency department who received C-spine CT scans were obtained over a 6-month period. The primary outcome of interest was prevalence of C-spine fracture. Secondary outcomes included fracture stability, appropriateness of imaging by NEXUS and CCR criteria, and estimated radiation dose exposure and costs associated with C-spine imaging studies.
Of the 760 patients meeting inclusion criteria, 7 C-spine fractures were identified (0.92% ± 0.68%). All fractures were identified by NEXUS and CCR criteria with 100% sensitivity. Of all these imaging studies performed, only 69% met NEXUS indications for imaging (50% met CCR indications). C-spine CT scans in patients not meeting CDR indications were associated with costs of $15,500 to $22,000 by NEXUS ($14,600-$25,600 by CCR) in this single center during the 6-month study period.
For ground-level fall, C-spine CT is overused. The consistent application of CDR criteria would reduce annual nationwide imaging costs in the United States by $6.8 to $9.6 million based on NEXUS ($6.4-$15.6 million based on CCR) and would reduce population radiation dose exposure by 0.8 to 1.1 million mGy based on NEXUS (0.7-1.9 million mGy based on CCR) if applied across all Level I trauma centers. Greater use of evidence-based CDRs plays an important role in facilitating emergency department patient management and reducing systemwide radiation dose exposure and imaging expenditures.
Diagnostic study, level III.
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