Faculty Bibliography

PURPOSE: Using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in a rat glioma model, and nested model selection (NMS), to compare estimates of the pharmacokinetic parameters vp , Ktrans , and ve for two different contrast agents (CAs)-gadofosveset, which reversibly binds to human serum albumin, and gadopentetate dimeglumine, which does not. MATERIALS AND METHODS: DCE-MRI studies were performed on nine Fisher 344 rats inoculated intracerebrally with 9L gliosarcoma cells using both gadofosveset and gadopentetate. The parameters vp , Ktrans , and ve were estimated using NMS. RESULTS: Ktrans estimates using gadofosveset, compared to gadopentetate, differed in their means (gadofosveset 0.025 +/- 0.008 min-1 vs. gadopentetate 0.046 +/- 0.011 min-1 ; P = 0.0039). This difference notwithstanding, the intraclass correlation coefficient (ICC) for the two estimates of Ktrans showed nearly perfect linear dependence (ICC = 0.8479 by Pearson's r). Other estimates, ve (gadofosveset 22.7 +/- 4.7% vs. gadopentetate 23.6 +/- 5.6%; P = 0.4258) and vp (gadofosveset 1.5 +/- 0.5% vs. gadopentetate 1.6 +/- 0.4%; P = 0.25), were not different in their means between the two CAs, and there was almost perfect agreement for ve (ICC = 0.8798) and substantial agreement for vp (ICC = 0.7981) between the two CAs. CONCLUSION: Estimates of Ktrans were statistically different using gadofosveset and gadopentetate, whereas ve and vp were similar with two CAs. NMS produced robust estimates of pharmacokinetic parameters using DCE-MRI that show promise as important measures of tumor physiology and microenvironment. J. Magn. Reson. Imaging 2014;. (c) 2014 Wiley Periodicals, Inc.

Evidence suggests that normal pressure hydrocephalus (NPH) is underdiagnosed in day to day radiologic practice, and differentiating NPH from cerebral atrophy due to other neurodegenerative diseases and normal aging remains a challenge. To better characterize NPH, we test the hypothesis that a prediction model based on automated MRI brain tissue segmentation can help differentiate shunt-responsive NPH patients from cerebral atrophy due to Alzheimer disease (AD) and normal aging. Brain segmentation into gray and white matter (GM, WM), and intracranial cerebrospinal fluid was derived from pre-shunt T1-weighted MRI of 15 shunt-responsive NPH patients (9 men, 72.6 +/- 8.0 years-old), 17 AD patients (10 men, 72.1 +/- 11.0 years-old) chosen as a representative of cerebral atrophy in this age group; and 18 matched healthy elderly controls (HC, 7 men, 69.7 +/- 7.0 years old). A multinomial prediction model was generated based on brain tissue volume distributions. GM decrease of 33 % relative to HC characterized AD (P < 0.005). High preoperative ventricular and near normal GM volumes characterized NPH. A multinomial regression model based on gender, GM and ventricular volume had 96.3 % accuracy differentiating NPH from AD and HC. In conclusion, automated MRI brain tissue segmentation differentiates shunt-responsive NPH with high accuracy from atrophy due to AD and normal aging. This method may improve diagnosis of NPH and improve our ability to distinguish normal from pathologic aging.

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Purpose 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. Materials and Methods 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. Results 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). Conclusion 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. (c) RSNA, 2014 Online supplemental material is available for this article.

BACKGROUND: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. METHODS: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). RESULTS: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. CONCLUSION/CONCLUSIONS: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.

OBJECTIVE: Glioblastoma (GBM) is a heterogeneous neoplasm with a small percentage of long-term survivors. Despite aggressive surgical resection and advances in radiotherapy and chemotherapy, the median survival for patients with GBM is 12-14 months. Factors associated with a favorable prognosis include young age, high performance status, gross resection >98%, non-eloquent tumor location and O6-methylguanine methyltransferase (MGMT) promoter methylation. We retrospectively analyzed the relationship of clinical, epidemiologic, genetic and molecular characteristics with survival in patients with GBM. METHODS: This retrospective analysis of overall survival looked at the outcomes of 480 patients diagnosed with GBM over 14 years at a single institution. Multivariate analysis was performed examining multiple patient characteristics. RESULTS: Median survival time improved from 11.8 months in patients diagnosed from 1995 to 1999 to 15.9 months in those diagnosed from 2005 to 2008. Factors associated with survivor groups were age, KPS, tumor resection, treatment received and early progression. 18 cancer-related genes were upregulated in short-term survivors and five genes were downregulated in short-term survivors. CONCLUSIONS: Epidemiologic, clinical, and molecular characteristics all contribute to GBM prognosis. Identifying factors associated with survival is important for treatment strategies as well as research for novel therapeutics and technologies. This study demonstrated improved survival for patients over time as well as significant differences among survivor groups.

OBJECTIVE: The purposes of this article are to review the American Board of Radiology requirements for practice quality improvement and to describe our approach to improving imaging utilization while offering a guide to implementing similar projects at other institutions, emphasizing the plan-do-study-act approach. CONCLUSION: There is increased emphasis on improving quality in health care. Our institution has undertaken a multiphase practice quality improvement project addressing the appropriate utilization of screening cervical spinal CT in an emergency department.