Faculty Bibliography

Functional diffusion mapping (fDM) is a cancer imaging technique that quantifies voxelwise changes in apparent diffusion coefficient (ADC). Previous studies have shown value of fDMs in bevacizumab therapy for recurrent glioblastoma multiforme (GBM). The aim of the present study was to implement explicit criteria for diffusion MRI quality control and independently evaluate fDM performance in a multicenter clinical trial (RTOG 0625/ACRIN 6677). A total of 123 patients were enrolled in the current multicenter trial and signed institutional review board-approved informed consent at their respective institutions. MRI was acquired prior to and 8 weeks following therapy. A 5-point QC scoring system was used to evaluate DWI quality. fDM performance was evaluated according to the correlation of these metrics with PFS and OS at the first follow-up time-point. Results showed ADC variability of 7.3% in NAWM and 10.5% in CSF. A total of 68% of patients had usable DWI data and 47% of patients had high quality DWI data when also excluding patients that progressed before the first follow-up. fDM performance was improved by using only the highest quality DWI. High pre-treatment contrast enhancing tumor volume was associated with shorter PFS and OS. A high volume fraction of increasing ADC after therapy was associated with shorter PFS, while a high volume fraction of decreasing ADC was associated with shorter OS. In summary, DWI in multicenter trials are currently of limited value due to image quality. Improvements in consistency of image quality in multicenter trials are necessary for further advancement of DWI biomarkers.

Perfusion imaging is a method for assessing the flow of blood occurring at the tissue level and can be accomplished by both CT and MR perfusion techniques. The use of perfusion imaging has increased substantially in the past decade, particularly in neuro-oncologic imaging, where it is has been used for brain tumor grading and directing biopsies or targeted therapy, as well as for the evaluation of treatment response and disease progression. This article discusses the basic principles and techniques of perfusion imaging, as well as its applications in neuro-oncology.

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.