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Background: Glioma grading is a critical procedure for selecting the most effective treatment policy. Biopsy result is the gold standard method for glioma grading, but inherent sampling errors in the biopsy procedure could lead to tumor misclassification. Aim: This study evaluated grading performances of a more comprehensive collection of the physiological indices quantified using an optimized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) paradigm for glioma grading. Methods: Thirty-five patients with glioma underwent DCE-MR imaging to evaluate the grading performances of DCE-MRI-derived physiological indices. The statistical differences in the physiological indices between the different grades of gliomas were studied, and the grading performances of these parameters were evaluated using the leave-one-out cross-validation method. Results: There were significant statistical differences in DCE-MRI-derived physiological indices between the different grades of gliomas. The mean rCBVs for grade II (low-grade glioma, LGG), grade III, grade IV, and high-grade (HGG) gliomas were 2.03 ± 0.78, 3.61 ± 1.64, 7.14 ± 3.19, and 5.28 ± 3.02, respectively. The mean rCBFs of 1.94 ± 0.97, 2.67 ± 0.96, 4.57 ± 1.77, and 3.57 ± 1.68 were, respectively, quantified for grade II (LGG), grade III, grade IV, and high-grade gliomas. The leave-one-out cross-validation method indicates that the grades of glioma tumors could be determined based on a specific threshold for each physiological index; for example, the optimal cutoff values for rCBF, rCBV, Ktrans, Kep, and Vp indices to distinguish between HGGs and LGGs were 2.11, 2.80, 0.025 mL/g min, 0.29 min−1, and 0.065 mL/g, respectively. Conclusions: From the results, it could be concluded that glioma grades could be determined using DCE-MRI-derived physiological indices with an acceptable agreement with histopathological results. [ABSTRACT FROM AUTHOR]

Introduction/Objectives: The fecal immunochemical test (FIT) helps triage primary care patients at risk of colorectal cancer (CRC). Improving FIT returns has received recent attention, however uncertainty exists regarding the accurate completion of samples provided for laboratory analysis. This study aims to identify the rejection rate of returned FIT samples and determine rejection causes. Methods: FIT samples from symptomatic patients within South Yorkshire, Bassetlaw, and North Derbyshire are processed at a central laboratory. Tests requests are made from 225 GP practices, which serve an estimated 2 million population. This study describes a retrospective review of FIT samples received in the central laboratory between 01/09/19 and 31/12/22. Locally held data was interrogated in March 2023 to determine the number of FIT samples received and rejected during the study period. Documented reasons for rejection were explored to identify common themes. Results: Total FIT specimens received during the study period was 126 422. Of these, 5190 (4.1%) were rejected. Monthly rejection rates fell from 17.4% in September 2019 to 1.3% in December 2022 (P <.001). Sampling errors were the most frequent cause for FIT rejection (2151/5190), with other causes including: expired specimen; no sample collection date/ time, no request form, incomplete patient information and illegible handwriting. Conclusions: This is the first study exploring FIT rejection rates in symptomatic primary care patients, which shows improvements in rejection rates over time. Targeted interventions could improve rejection rates further, thereby reducing NHS resource use and costs and diagnostic delays. [ABSTRACT FROM AUTHOR]

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Cohen's kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss' generalized kappa. Fleiss' generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package "rel." The purpose of this article is to show that the large-sample variance of Fleiss' generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure. [ABSTRACT FROM AUTHOR]

Background: Extracellular volume fraction (ECV) is altered in pathological cardiac remodeling and predicts death and arrhythmia. ECV can be quantified using cardiovascular magnetic resonance (CMR) T1 mapping but calculation requires a measured hematocrit (Hct). The longitudinal relaxation of blood has been used in adults to generate a synthetic Hct (estimate of true Hct) but has not been validated in pediatric populations. Methods: One hundred fourteen children and young adults underwent a total of 163 CMRs with T1 mapping. The majority of subjects had a measured Hct the same day (N = 146). Native and post-contrast T1 were determined in blood pool, septum, and free wall of mid-LV, avoiding areas of late gadolinium enhancement. Synthetic Hct and ECV were calculated and intraclass correlation coefficient (ICC) and linear regression were used to compare measured and synthetic values. Results: The mean age was 16.4 ± 6.4 years and mean left ventricular ejection fraction was 59% ± 9%. The mean measured Hct was 41.8 ± 3.0% compared to the mean synthetic Hct of 43.2% ± 2.9% (p < 0.001, ICC 0.46 [0.27, 0.52]) with the previously published model and 41.8% ± 1.4% (p < 0.001, ICC 0.28 [0.13, 0.42]) with the locally-derived model. Mean measured mid-free wall ECV was 30.5% ± 4.8% and mean synthetic mid-free wall ECV of local model was 29. 7% ± 4.6% (p < 0.001, ICC 0.93 [0.91, 0.95]). Correlations were not affected by heart rate and did not significantly differ in subpopulation analysis. While the ICC was strong, differences between measured and synthetic ECV ranged from -8. 4% to 4.3% in the septum and -12.6% to 15.8% in the free wall. Using our laboratory's normal cut-off of 28.5%, 59 patients (37%) were miscategorized (53 false negatives, 6 false positives) with published model ECV. The local model had 37 miscategorizations (20 false negatives, 17 false positives), significantly fewer but still a substantial number (23%). Conclusions: Our data suggest that use of synthetic Hct for the calculation of ECV results in miscategorization of individual patients. This difference may be less significant once synthetic ECV is calculated and averaged over a large research cohort, making it potentially useful as a research tool. However, we recommend formal measurement of Hct in children and young adults for clinical CMRs. [ABSTRACT FROM AUTHOR]