C. Carl Jaffe

Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers

PURPOSE To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). MATERIALS AND METHODS This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBV(max)] and mean rCBV [rCBV(mean)]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV(NEL)) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. RESULTS No statistically significant differences were noted for rCBV(max), rCBV(mean) of contrast-enhanced lesion or rCBV(NEL) between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBV(max) (P = .0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV(mean) with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBV(mean) alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. CONCLUSION The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor

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.

Image data sharing for biomedical research--meeting HIPAA requirements for De-identification.

Data sharing is increasingly recognized as critical to cross-disciplinary research and to assuring scientific validity. Despite National Institutes of Health and National Science Foundation policies encouraging data sharing by grantees, little data sharing of clinical data has in fact occurred. A principal reason often given is the potential of inadvertent violation of the Health Insurance Portability and Accountability Act privacy regulations. While regulations specify the components of private health information that should be protected, there are no commonly accepted methods to de-identify clinical data objects such as images. This leads institutions to take conservative risk-averse positions on data sharing. In imaging trials, where images are coded according to the Digital Imaging and Communications in Medicine (DICOM) standard, the complexity of the data objects and the flexibility of the DICOM standard have made it especially difficult to meet privacy protection objectives. The recent release of DICOM Supplement 142 on image de-identification has removed much of this impediment. This article describes the development of an open-source software suite that implements DICOM Supplement 142 as part of the National Biomedical Imaging Archive (NBIA). It also describes the lessons learned by the authors as NBIA has acquired more than 20 image collections encompassing over 30 million images.

Imaging and Genomics: Is There a Synergy?

One promising route for imaging is exemplified by the article by Gevaert et al in this issue: That study, which analyzed features extracted from non–small cell lung cancer CT and PET cases, offers an original approach to exploring the clinical prognostic value of imaging-genomics.

The Clinical Impact of Ultrasonic Beam Focusing Patterns

Previous clinical ultrasound investigations have been adversely affected by inadequate knowledge of the focusing characteristics of the transducer. Precise knowledge of the beam width profile has substantial impact on routine diagnostic procedures and is essential for proper display of the anechoic nature of small cysts, tissue textures, and particularly the distal shadow which characterizes gallstones. In vitro and in vivo tests using transducers with various beam focusing profiles clearly demonstrate that optimal imaging performance occurs within a very narrow range at a critical distance from the transducer face.

Response Assessment in Clinical Trials: Implications for Sarcoma Clinical Trial Design

Response assessment and design of clinical trials require careful consideration of many factors, especially as validated response criteria can ultimately lead to the approval of an anticancer agent. Current anatomic imaging criteria are difficult to apply for evaluation of certain types of tumors, including soft tissue sarcomas. The emergence of new molecular imaging techniques, such as 64-slice computed tomography scanners and dynamic contrast magnetic resonance imaging, provide complementary information to conventional anatomical imaging. Currently the U.S. National Cancer Institute and the U.S. Food and Drug Administration are aiming to revise existing response criteria based on the development of volumetric anatomic imaging for oncology. Reviewing existing and new approaches in the design of clinical trials will help to optimize the clinical development and evaluation of new therapies for sarcomas.

Lack of an acoustic shadow on scans of gallstones: a possible artifact.

The high attenuation of gallstones results in the formation of an acoustic shadow on the ultrasound scan. Such shadows are best seen when the stone lies within the focal zone of the transducer and is large in comparison to the beam width or wavelength employed. Inadequecies in the dynamic range of available TV display units necessitate the use of compression-amplification signal processing which may preclude perception of such a shadow and seriously interfere with diagnostic accuracy.

TCIA: An information resource to enable open science

Reusable, publicly available data is a pillar of open science. The Cancer Imaging Archive (TCIA) is an open image archive service supporting cancer research. TCIA collects, de-identifies, curates and manages rich collections of oncology image data. Image data sets have been contributed by 28 institutions and additional image collections are underway. Since June of 2011, more than 2,000 users have registered to search and access data from this freely available resource. TCIA encourages and supports cancer-related open science communities by hosting and managing the image archive, providing project wiki space and searchable metadata repositories. The success of TCIA is measured by the number of active research projects it enables (>40) and the number of scientific publications and presentations that are produced using data from TCIA collections (39).

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (<18, N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.

A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma.

OBJECTIVE Individual MRI characteristics (e.g., volume) are routinely used to identify survival-associated phenotypes for glioblastoma (GBM). This study investigated whether combinations of MRI features can also stratify survival. Furthermore, the molecular differences between phenotype-induced groups were investigated. METHODS Ninety-two patients with imaging, molecular, and survival data from the TCGA (The Cancer Genome Atlas)-GBM collection were included in this study. For combinatorial phenotype analysis, hierarchical clustering was used. Groups were defined based on a cutpoint obtained via tree-based partitioning. Furthermore, differential expression analysis of microRNA (miRNA) and mRNA expression data was performed using GenePattern Suite. Functional analysis of the resulting genes and miRNAs was performed using Ingenuity Pathway Analysis. Pathway analysis was performed using Gene Set Enrichment Analysis. RESULTS Clustering analysis reveals that image-based grouping of the patients is driven by 3 features: volume-class, hemorrhage, and T1/FLAIR-envelope ratio. A combination of these features stratifies survival in a statistically significant manner. A cutpoint analysis yields a significant survival difference in the training set (median survival difference: 12 months, p = 0.004) as well as a validation set (p = 0.0001). Specifically, a low value for any of these 3 features indicates favorable survival characteristics. Differential expression analysis between cutpoint-induced groups suggests that several immune-associated (natural killer cell activity, T-cell lymphocyte differentiation) and metabolism-associated (mitochondrial activity, oxidative phosphorylation) pathways underlie the transition of this phenotype. Integrating data for mRNA and miRNA suggests the roles of several genes regulating proliferation and invasion. CONCLUSIONS A 3-way combination of MRI phenotypes may be capable of stratifying survival in GBM. Examination of molecular processes associated with groups created by this combinatorial phenotype suggests the role of biological processes associated with growth and invasion characteristics.