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Purpose:Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma.Experimental Design:Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies.Results:Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E−15).Conclusions:We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human–mouse matched coclinical trials.

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Congenital anomalies of the kidney and the urinary tract such as renal agenesis and ectopic ureter have complex development. These anomalies have variable presentations and associations. In this report, we highlight the case of a young man with congenital renal agenesis presenting for a urinary tract infection. Abdominal and pelvic computed tomography imaging revealed the rare association of renal agenesis with contralateral ectopic ureter and subsequent hydroureteronephrosis. A urinary tract infection can be the presenting complication of such association, and a long follow-up is needed to anticipate the management.

Split spinal cord syndrome (SCM), an entity of spinal dysraphisms, occurs rarely in adults and is associated with tethered cord syndrome, which commonly presents with back pain. Besides clinical findings, neuroimaging by Magnetic resonance imaging or computed tomography is needed for diagnosis. We report a case of a previously healthy 51-year-old man who presented for right upper abdominal quadrant pain. A computed tomography scan of the abdomen and pelvis incidentally discovered the diagnosis of type 1 SCM. This case highlights that SCM can remain asymptomatic throughout life to be diagnosed at one point by neuroimaging. Whenever no clinical complications exist, no surgical intervention might be indicated.

Background Spontaneous urinary collecting system rupture is caused by increased ureteral intraluminal pressure secondary to an obstruction. Rarely, a small stone exerts high intraureteral pressure especially if it is located distally. Many management modalities with good outcomes have been implicated. Purpose Herein, we present a case of nontraumatic rupture of the renal calyx due to a 4 mm obstructing stone at the vesicoureteral junction. Basic procedures CT scan of abdomen and pelvis without contrast, CT scan of the abdomen and pelvis with intravenous contrast, Cystoscopy, Double–J ureter stent, Urinary Foley catheter. Main findings The diagnosis was confirmed by CT imaging. Non-contrast enhanced CT scan of abdomen and pelvis showed obstructive calculi measuring 4 mm in the right vesicoureteral junction. Contrast-enhanced CT scan revealed leakage of contrast in the perinephric space at the right major calyx with intact bilateral ureters, suggestive of calyceal rupture. The treatment involved antibiotics and double-J stenting. Principal conclusions This case demonstrates that spontaneous calyceal rupture should be suspected in urolothiasis patients presenting for a severe pain even if the calculus is small (less than 5 mm) and the laboratory markers are normal. An immediate management is required to relief symptoms and prevent further complications.

Multi-hop cellular network MCN is a wireless communication architecture that combines the benefits of conventional single-hop cellular networks and multi-hop ad hoc relaying networks. The route selection in MCN depends on the availability of intermediate nodes and their neighborhood connectivity. Cognitive radio CR is an emerging communication paradigm that exploits the available radio frequencies opportunistically for the effective utilization of the radio frequency spectrum. The incorporation of CR and mobile ad hoc network routing protocols in MCN could potentially improve the spectrum utilization and the routing performance of MCN. This paper firstly presents the proposed model for the multi-interface CR mobile node with transceiver synchronization and then investigates its opportunistic spectrum utilization and routing performance in MCN. Copyright © 2015 John Wiley & Sons, Ltd.

Purpose: Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Experimental Design: Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies. Results: Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E−15). Conclusions: We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human–mouse matched coclinical trials.

A noninvasive and reliable surrogate method of determining MGMT status could serve to complement brain tumor biopsy or as an alternative in those patients who have a contraindication to undergo an invasive procedure.The significance of magnetic resonance 3D volumetrics and qualitative imaging features for predicting MGMT methylation status in glioblastoma was evaluated (73.6% accuracy achieved).Our analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were associated with the status of MGMT promoter methylation in glioblastoma.The results of our study provide further evidence of an association between standard preoperative MRI features and MGMT methylation status in glioblastoma. Background and ObjectiveThe O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively. MethodsA retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database. ResultsThe status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM. ConclusionsThe obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

Glioblastomas are highly infiltrated by diverse immune cells, including microglia, macrophages, and myeloid-derived suppressor cells (MDSCs). Understanding the mechanisms by which glioblastoma-associated myeloid cells (GAMs) undergo metamorphosis into tumor-supportive cells, characterizing the heterogeneity of immune cell phenotypes within glioblastoma subtypes, and discovering new targets can help the design of new efficient immunotherapies. In this study, we performed a comprehensive battery of immune phenotyping, whole-genome microarray analysis, and microRNA expression profiling of GAMs with matched blood monocytes, healthy donor monocytes, normal brain microglia, nonpolarized M0 macrophages, and polarized M1, M2a, M2c macrophages. Glioblastoma patients had an elevated number of monocytes relative to healthy donors. Among CD11b+ cells, microglia and MDSCs constituted a higher percentage of GAMs than did macrophages. GAM profiling using flow cytometry studies revealed a continuum between the M1- and M2-like phenotype. Contrary to current dogma, GAMs exhibited distinct immunological functions, with the former aligned close to nonpolarized M0 macrophages.

BACKGROUND 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. METHODS 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. RESULTS 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). CONCLUSIONS 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.

Imaging is the modality of choice for noninvasive characterization of biological tissue and organ systems; imaging serves as early diagnostic tool for most disease processes and is rapidly evolving, thus transforming the way we diagnose and follow patients over time. A vast number of cancer imaging characteristics have been correlated to underlying genomics; however, none have established causality. Therefore, our objectives were to test if there is a causal relationship between imaging and genomic information; and to develop a clinically relevant radiomic pipeline for glioblastoma molecular characterization. Functional validation was performed using a prototypic in vivo RNA-interference-based orthotopic xenograft mouse model. The automated pipeline collects 4800 MRI-derived texture features per tumor. Using univariate feature selection and boosted tree predictive modeling, a patient-specific genomic probability map was derived and patient survival predicted (The Cancer Genome Atlas/MD Anderson data sets). Data demonstrated a significant xenograft to human association (area under the curve [AUC] 84%, P

PURPOSE:To create an imaging genomic map, linking MR imaging traits with gene and miRNA expression profiles,in GBM patients to determine genomic correlates of diffusion radiophenotype to find new genomic targets. Decreases in diffusion in GBM,is associated with increased cellular density and higher nuclear to cytoplasm ratio.Here,we present the first study examining in a quantitative way MRI-diffusion genomics in GBM to determine targetable genomic biomarkers in GBM. MATERIALS AND METHODS: We identified 80 treatment-naive GBM patients from The Cancer Genome Atlas(TCGA)who had both gene and miRNA expression profiles and pretreatment MR-neuroimaging specifically ADC maps. Image segmentation analysis was done in Slicer 3.6 using segmentation module,Fluid-Attenuated Inversion Recovery(FLAIR)was used for segmentation of the edema and post-contrast T1 weighted-imaging(T1WI)for segmentation of enhancement and necrosis.Diffusion was analyzed in Olea Sphere 2.3 and Conventional FLAIR/post contrast T1WI was registered to DWI/ADC maps.ADC,FLAIR,T1 Gadolinium enhancement values were measured using the ROI based method,in the peri-tumoral edema/non-enhancing tumor and the enhancing tumor zones,dividing the peri-tumoral edema/non-enhancing tumor into 3 zones each of 1 cm width,3 ROI measurements were taken from each zone.Multiple quantitative imaging features were identified and combined to create the imaging biomarker signature predictive for the invasion and poor prognosis of the tumor. All the genomic data was also analyzed to determine the most upregulated mRNAs/miRNAs using ingenuity pathway analysis(IPA) in those with restricted diffusion. RESULTS: Quantitative ADC values were in high correlation with biologically concordant functional genomic targets predicting the invasiveness and progression. CONCLUSIONS: The diffusion and conventional MR radiophenotype identified genes and miRNAs and corresponding molecular networks that were highly associated with tumor invasion.By these means we were able to identify possible key genes and miRNAs involved in the latter regulation.

This paper deals with problems encountered in continuous-time estimation. The process is modelled with the delta operator (δ=(q−l)/T e where T e is the sampling period and q the shift operator). The structure of the discrete-time model with the operator q is supposed to be the same as the corresponding continuous-time model. The parameters of the delta model tend in this case towards those of the continuous-time model when the sampling period tends towards zero. The problems encountered when treating the data of the observation equation are the same as in the continuous-time approach (particularly, the problems of the derivatives). They are discussed here. A pole-placement control law using the parameters of the delta model is then applied. Two possibilities for the choice of the closed-loop poles will be presented. Physical experiments on an electrical laboratory furnace are discussed. Here only the case of processes without time delay will be treated. But a natural robustness of the adaptive control law with respect to eventually non modelled time delays will appear.