BackgroundThe heart-to-mediastinum ratio (HMR) of 123I-metaiodobenzylguanidine (MIBG) showed variations among institutions and needs to be standardized among various scinticamera-collimator combinations.MethodsA total of 225 phantom experiments were performed in 84 institutions to calculate cross-calibration coefficients of HMR. Based on phantom studies, a conversion coefficient for each camera-collimator system was created, including low-energy (LE, n = 125) and a medium-energy (ME, n = 100) collimators. An average conversion coefficient from the most common ME group was used to calculate the standard HMR. In clinical MIBG studies (n = 52) from three institutions, HMRs were standardized from both LE- and ME-type collimators and classified into risk groups of <1.60, 1.60-2.19, and ≥2.20.ResultsThe average conversion coefficients from the individual camera-collimator condition to the mathematically calculated reference HMR ranged from 0.55 to 0.75 for LE groups and from 0.83 to 0.95 for ME groups. The conversion coefficient of 0.88 was used to unify HMRs from all acquisition conditions. Using the standardized HMR, clinical studies (n = 52) showed good agreement between LE and ME types regarding three risk groups (κ = 0.83, P < .0001, complete agreement in 90%, 42% of the patients reclassified into the same risk group).ConclusionBy using the reference HMR and conversion coefficients for the system, HMRs with various conditions can be converted to the standard HMRs in a range of normal to low HMRs.
BackgroundArtificial neural network (ANN)-based bone scan index (BSI), a marker of the amount of bone metastasis, has been shown to enhance diagnostic accuracy and reproducibility but is potentially affected by training databases. The aims of this study were to revise the software using a large number of Japanese databases and to validate its diagnostic accuracy compared with the original Swedish training database.MethodsThe BSI was calculated with EXINIbone (EB; EXINI Diagnostics) using the Swedish training database (n = 789). The software using Japanese training databases from a single institution (BONENAVI version 1, BN1, n = 904) and the revised version from nine institutions (version 2, BN2, n = 1,532) were compared. The diagnostic accuracy was validated with another 503 multi-center bone scans including patients with prostate (n = 207), breast (n = 166), and other cancer types. The ANN value (probability of abnormality) and BSI were calculated. Receiver operating characteristic (ROC) and net reclassification improvement (NRI) analyses were performed.ResultsThe ROC analysis based on the ANN value showed significant improvement from EB to BN1 and BN2. In men (n = 296), the area under the curve (AUC) was 0.877 for EB, 0.912 for BN1 (p = not significant (ns) vs. EB) and 0.934 for BN2 (p = 0.007 vs. EB). In women (n = 207), the AUC was 0.831 for EB, 0.910 for BN1 (p = 0.016 vs. EB), and 0.932 for BN2 (p < 0.0001 vs. EB). The optimum sensitivity and specificity based on BN2 was 90% and 84% for men and 93% and 85% for women. In patients with prostate cancer, the AUC was equally high with EB, BN1, and BN2 (0.939, 0.949, and 0.957, p = ns). In patients with breast cancer, the AUC was improved from EB (0.847) to BN1 (0.910, p = ns) and BN2 (0.924, p = 0.039). The NRI using ANN between EB and BN1 was 17.7% (p = 0.0042), and that between EB and BN2 was 29.6% (p < 0.0001). With respect to BSI, the NRI analysis showed downward reclassification with total NRI of 31.9% ( p < 0.0001).ConclusionIn the software for calculating BSI, the multi-institutional database significantly improved identification of bone metastasis compared with the original database, indicating the importance of a sufficient number of training databases including various types of cancers.
PurposeIn patients with a small heart, defined as an end-systolic volume (ESV) of ≤20 mL calculated using the Quantitative Gated SPECT (QGS) program, underestimation of ESV and overestimation of ejection fraction (EF) using gated myocardial perfusion imaging are considered errors caused by inappropriate delineation of the left ventricle (LV). The aim of this study was to develop a new method for delineation of the LV and to evaluate it in studies using a digital phantom, normal subjects and patients.MethodsThe active shape-based method for LV delineation, EXINI heart (ExH), was adjusted to more accurately process small hearts. In small hearts, due to the partial volume effect and the short distance to the opposite ventricular wall, the endocardial and the epicardial surfaces are shifted in the epicardial direction depending on the midventricular volume. The adjusted method was evaluated using digital XCAT phantoms with Monte Carlo simulation (8 virtual patients), a Japanese multicentre normal database (69 patients) and consecutive Japanese patients (116 patients). The LV volumes, EF and diastolic parameters derived from ExH and QGS were compared.ResultsThe digital phantom studies showed a mean ESV of 87 % ± 9 % of the true volume calculated using ExH and 22 % ± 18 % calculated using QGS. In the normal database, QGS gave higher EFs in women than in men (71.4 ± 6.0 % vs. 67.2 ± 6.0 %, p = 0.0058), but ExH gave comparable EFs (70.7 ± 4.9 % and 71.4 ± 5 % in men and women, respectively, p = ns). QGS gave higher EFs in subjects with a small heart than in those with a normal-sized heart (74.5 ± 5.1 % vs. 66.1 ± 4.9 %), but ExH gave comparable values (70.0 ± 5.9 % vs. 71.6 ± 4.2 %, respectively, p = ns). In consecutive patients, the average EFs with QGS in patients with ESV >20 mL, 11–20 mL and ≤10 mL were 57.9 %, 71.9 % and 83.2 %, but with ExH the differences among these groups were smaller (65.2 %, 67.8 % and 71.5 %, respectively).ConclusionThe volume-dependent edge correction algorithm was able to effectively reduce the effects on ESV and EF of a small heart. The uniform normal values might be applicable to both men and women and to both small and normal-sized hearts.
Brown adipose tissue (BAT) is an attractive therapeutic target to combat diabetes and obesity due to its ability to increase glucose expenditure. In a genetic rat model (ZDF fa/fa) of type-2 diabetes and obesity, we aimed to investigate glucose utilization of BAT by 18F-FDG PET imaging. Male Zucker diabetic fatty (ZDF) and Male Zucker lean (ZL) control rats were studied at 13 weeks. Three weeks prior to imaging, ZDF rats were randomized into a no-restriction (ZDF-ND) and a mild calorie restriction (ZDF-CR) group. Dynamic 18F-FDG PET using a dedicated small animal PET system was performed under hyperinsulinemic-euglycemic clamp. 18F-FDG PET identified intense inter-scapular BAT glucose uptake in all ZL control rats, while no focally increased 18F-FDG uptake was detected in all ZDF-ND rats. Mild but significant improved BAT tracer uptake was identified after calorie restriction in diabetic rats (ZDF-CR). The weight of BAT tissue and fat deposits were significantly increased in ZDF-CR and ZDF-ND rats as compared to ZL controls, while UCP-1 and mitochondrial concentrations were significantly decreased. Whitening and severely impaired insulin-stimulated glucose uptake in BAT was confirmed in a rat model of type-2 diabetes. Additionally, calorie restriction partially restored the impaired BAT glucose uptake.
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