Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images

Volumetric analysis of the kidney parenchyma provides additional information for the detection and monitoring of various renal diseases. Therefore the purposes of the study were to develop and evaluate a semi-automated segmentation tool and a modified ellipsoid formula for volumetric analysis of the kidney in non-contrast T2-weighted magnetic resonance (MR)-images. Three readers performed semi-automated segmentation of the total kidney volume (TKV) in axial, non-contrast-enhanced T2-weighted MR-images of 24 healthy volunteers (48 kidneys) twice. A semi-automated threshold-based segmentation tool was developed to segment the kidney parenchyma. Furthermore, the three readers measured renal dimensions (length, width, depth) and applied different formulas to calculate the TKV. Manual segmentation served as a reference volume. Volumes of the different methods were compared and time required was recorded. There was no significant difference between the semi-automatically and manually segmented TKV (p = 0.31). The difference in mean volumes was 0.3 ml (95% confidence interval (CI), ?10.1 to 10.7 ml). Semi-automated segmentation was significantly faster than manual segmentation, with a mean difference = 188 s (220 vs. 408 s); p < 0.05. Volumes did not differ significantly comparing the results of different readers. Calculation of TKV with a modified ellipsoid formula (ellipsoid volume × 0.85) did not differ significantly from the reference volume; however, the mean error was three times higher (difference of mean volumes ?0.1 ml; CI ?31.1 to 30.9 ml; p = 0.95). Applying the modified ellipsoid formula was the fastest way to get an estimation of the renal volume (41 s). Semi-automated segmentation and volumetric analysis of the kidney in native T2-weighted MR data delivers accurate and reproducible results and was significantly faster than manual segmentation. Applying a modified ellipsoid formula quickly provides an accurate kidney volume.     

Estimation of the relationship between body mass index and EQ-5D health utilities in individuals with type 2 diabetes: Evidence from the population-based KORA studies

ObjectivesObesity is known to be an important risk factor for type 2 diabetes and its related comorbid conditions; however, its specific impact on generic health-related quality of life (HRQL) is less clear. The objective of this study was to estimate the association between body mass index (BMI) and HRQL in individuals with type 2 diabetes.MethodsThe EQ-5D quality of life questionnaire was administered in a follow-up of 10,385 participants aged 33–94 of the population-based German MONICA/KORA surveys. 1033 participants with type 2 diabetes were identified by self-report combined with validated physician diagnoses. Semiparametric additive regression models were used to estimate the effect of BMI on EQ-5D health utilities adjusted for age, sex, education and comorbidities.ResultsBMI was significantly associated with EQ-5D health utilities even after adjustment for macro- and microvascular complications. The functional relationship between BMI and utilities was nonlinear, reflecting optimal health around 26 kg/m² and significantly decreasing health utilities with increasing levels of overweight and obesity (? 0.09 points between BMI values 26 and 40). Among the diabetic complications, the history of a stroke (? 0.13) and neuropathy (? 0.10) were the strongest predictors of reduced health utility scores.ConclusionsBMI is strongly associated with health utilities in persons with type 2 diabetes. This suggests that lifestyle measures to reduce obesity can markedly improve patients' health-related quality of life and that the negative effect of potential weight gain should be taken into account when determining patient preferences for different type 2 diabetes treatment options.     

The PCBP1 gene encoding poly(rc) binding protein i is recurrently mutated in Burkitt lymphoma

The genetic hallmark of Burkitt lymphoma is the translocation t(8;14)(q24;q32), or one of its light chain variants, resulting in IG‐MYC juxtaposition. However, these translocations alone are insufficient to drive lymphomagenesis, which requires additional genetic changes for malignant transformation. Recent studies of Burkitt lymphoma using next generation sequencing approaches have identified various recurrently mutated genes including ID3, TCF3, CCND3, and TP53. Here, by using similar approaches, we show that PCBP1 is a recurrently mutated gene in Burkitt lymphoma. By whole‐genome sequencing, we identified somatic mutations in PCBP1 in 3/17 (18%) Burkitt lymphomas. We confirmed the recurrence of PCBP1 mutations by Sanger sequencing in an independent validation cohort, finding mutations in 3/28 (11%) Burkitt lymphomas and in 6/16 (38%) Burkitt lymphoma cell lines. PCBP1 is an intron‐less gene encoding the 356 amino acid poly(rC) binding protein 1, which contains three K‐Homology (KH) domains and two nuclear localization signals. The mutations predominantly (10/12, 83%) affect the KH III domain, either by complete domain loss or amino acid changes. Thus, these changes are predicted to alter the various functions of PCBP1, including nuclear trafficking and pre‐mRNA splicing. Remarkably, all six primary Burkitt lymphomas with a PCBP1 mutation expressed MUM1/IRF4, which is otherwise detected in around 20–40% of Burkitt lymphomas. We conclude that PCBP1 mutations are recurrent in Burkitt lymphomas and might contribute, in cooperation with other mutations, to its pathogenesis. © 2015 Wiley Periodicals, Inc.