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Brain Tumor Image Analysis (BraTumIA) is a fully automated segmentation tool dedicated to detecting brain tumors imaged by magnetic resonance imaging (MRI). BraTumIA has recently been applied to several clinical investigations; however, the validity of this novel method has not yet been fully examined. The present study was conducted to validate the quality of tumor segmentation with BraTumIA in comparison with results from 11C-methionine positron emission tomography (MET-PET). A total of 45 consecutive newly diagnosed high-grade gliomas imaged by MRI and MET-PET were analyzed. Automatic tumor segmentation was conducted by BraTumIA and the resulting segmentation images were registered to MET-PET. Three-dimensional conformal association between these two modalities was calculated, considering MET-PET as the gold standard. High underestimation and overestimation errors were observed in tumor segmentation calculated by BraTumIA compared with MET-PET. Furthermore, when the tumor/normal ratio threshold was set at 1.3 from MET-PET, the BraTumIA false-positive fraction was ~0.4 and the false-negative fraction was 0.9. By tightening this threshold to 2.0, the BraTumIA false-positive fraction was 0.6 and the false-negative fraction was 0.6. Following comparison of segmentation performance with BraTumIA with regard to glioblastoma (GBM) and World Health Organization (WHO) grade III glioma, GBM exhibited better segmentation compared with WHO grade III glioma. Although BraTumIA may be able to detect enhanced tumors, non-enhancing tumors and necrosis, the spatial concordance rate with MET-PET was relatively low. Careful interpretation is therefore required when using this technique. [ABSTRACT FROM AUTHOR]

Background: Many studies have reported that transverse computed tomography (CT) imaging findings correlate with prognosis of patients with small peripheral lung neoplasm with lepidic growth. However, no studies have examined this correlation with the aid of three-dimensional (3D) CT data. Purpose: To determine the most efficacious imaging factor for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. Material and Methods: We evaluated CT findings, nodule patterns, SUVmax on FDG-PET/CT, as well as nodule volume and ratios of solid parts to nodule volume that were semi-automatically measured on CT images of 64 pulmonary nodules of ≤ 2 cm in 60 consecutive patients (24 men and 36 women; mean age, 65 years). For logistic modeling, we used all of the significant factors observed between the neoplasms with good and with poor prognosis as independent variables to estimate the statistically significant factors for discriminating invasive adenocarcinomas with lepidic growth (lesions with poor prognosis, n=42) from the other neoplasms, including preinvasive lesions (lesions with good prognosis, n=22), resulting in a recommendation for the optimal criterion for predicting lesions with poor prognosis. Results: The logistic regression model identified the ratio of the solid part to the whole volume of a pulmonary nodule as the only significant factor (P=0.04) for differentiating lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. A ratio of 0.238 or more showed the highest discriminatory accuracy of 84% with 91% sensitivity and 76% specificity. Conclusion: Computer-aided analyses of pulmonary nodules proved most useful for establishing the optimal criterion for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. [ABSTRACT FROM AUTHOR]

One of the challenges in glioblastoma (GBM) imaging is to visualize non-enhancing tumor (NET) lesions. The ratio of T1- and T2-weighted images (rT1/T2) is reported as a helpful imaging surrogate of microstructures of the brain. This research study investigated the possibility of using rT1/T2 as a surrogate for the T1- and T2-relaxation time of GBM to visualize NET effectively. The data of thirty-four histologically confirmed GBM patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were collected for analysis. Two of them also underwent MR relaxometry with rT1/T2 reconstructed for all cases. Met-PET was used as ground truth with T2-FLAIR hyperintense lesion, with >1.5 in tumor-to-normal tissue ratio being NET. rT1/T2 values were compared with MR relaxometry and Met-PET. rT1/T2 values significantly correlated with both T1- and T2-relaxation times in a logarithmic manner (p < 0.05 for both cases). The distributions of rT1/T2 from Met-PET high and low T2-FLAIR hyperintense lesions were different and a novel metric named Likeliness of Methionine PET high (LMPH) deriving from rT1/T2 was statistically significant for detecting Met-PET high T2-FLAIR hyperintense lesions (mean AUC = 0.556 ± 0.117; p = 0.01). In conclusion, this research study supported the hypothesis that rT1/T2 could be a promising imaging marker for NET identification. [ABSTRACT FROM AUTHOR]

Background Context: Giant cell tumor of the tendon sheath (GCTTS) is a common, benign lesion of the synovial membrane that occurs more often in large joints than in digits. Giant cell tumor of the tendon sheath rarely arises in close proximity to the axial skeleton.Purpose: The purpose of the study was to report a rare case of GCTTS arising from the membrane surrounding the posterior arch of the atlas (C1).Study Design/setting: This is a case report.Methods: The methods involve clinical findings and review of current literature.Results: In this report, we describe a rare case of GCTTS arising from the membrane surrounding the posterior arch of C1, with no apparent continuity with the facet joint. Here we show the radiographic features, with particular emphasis on positron emission tomography-computerized tomography scans, which have not been previously reported.Conclusions: We experienced an extremely rare case of GCTTS arising from the membrane surrounding the posterior arch of the C1 vertebra. In spite of the rarity of this disease, GCTTS should be considered in the differential diagnosis of the axial skeletal lesion. Awareness of GCTTS is important because its radiographic features may simulate other neoplastic lesions in the spine. [ABSTRACT FROM AUTHOR]