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.
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.
The 2-point DIXON method is widely used to assess fat fractions (FFs) in magnetic resonance images (MRIs) of the tongue, pharyngeal wall, and surrounding tissues in patients with obstructive sleep ...apnea (OSA). However, the method is semiquantitative and is susceptible to B0 field inhomogeneities and R2* confounding factors. Using the method, although several studies have shown that patients with OSA have increased fat deposition around the pharyngeal cavity, conflicting findings was also reported in 1 study. This discrepancy necessitates that we examine the FF estimation method used in the earlier studies and seek a more accurate method to measure FFs.
We examined the advantages of using the GOOSE (globally optimal surface estimation) method to replace the 2-point DIXON method for quantifying fat in the tongue and surrounding tissues on MRIs. We first used phantoms with known FFs (true FFs) to validate the GOOSE method and examine the errors in the DIXON method. Then, we compared the 2 methods in the tongue, soft palate, pharyngeal wall, and parapharyngeal fat pad of 63 healthy participants to further assess the errors caused by the DIXON method. Six participants were excluded from the comparison of the tongue FFs because of technical failures. Paired Student t tests were performed on FFs to detect significant differences between the 2 methods. All measures were obtained using 3 T Siemens MRI scanners.
In the phantoms, the FFs measured by GOOSE agreed with the true FF, with only a 1.2% mean absolute error. However, the same measure by DIXON had a 10.5% mean absolute error. The FFs obtained by DIXON were significantly lower than those obtained by GOOSE (P < 0.0001) in the human participants. We found strong correlations between GOOSE and DIXON in the tongue (R2 = 0.90), soft palate (R2 = 0.66), and parapharyngeal fat pad (R2 = 0.88), but the correlation was weaker in the posterior pharyngeal walls (R2 = 0.32) in participants.
The widely used 2-point DIXON underestimated FFs, relative to GOOSE, in phantom measurements and tissues studied in vivo. Thus, an advanced method, such as GOOSE, that uses multiecho complex data is preferred for estimating FF.
This paper presents a novel implementation of a three-dimensional Virtual Librarian Chatbot using IBM Watson artificial intelligence technology and virtual reality. In this method, participants ...interact with virtual librarian chatbots by asking specific questions about the library system. This research investigated the factors used in the Technology Acceptance Model, mainly Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Intention to Use, and Curiosity, to examine the effectiveness of the suggested Virtual Librarian Chatbot. These results highlight the potential for integrating Virtual Librarian Chatbots in academic libraries to enhance user experience, support remote learning and research, and streamline library services. The successful implementation of such chatbots could transform user interactions with academic libraries in the digital age, offering instant access to information and reducing the workload of human librarians.
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).
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.
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.
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.
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