Purpose
Hyperparathyroidism (HPT) is a common endocrine disorder caused by hyperfunctioning parathyroid glands (HP). The correct detection and localization of HP is challenging but crucial, as it may ...guide surgical treatment, particularly in patients with primary HPT. There is a growing body of data regarding the role of radiolabelled choline positron emission tomography (PET) in this setting. Therefore, we performed a systematic review and meta-analysis of the diagnostic performance of this method in detecting HP in patients with HPT.
Methods
This systematic review and meta-analysis was carried out according to PRISMA guidelines. A comprehensive computer literature search of PubMed/MEDLINE, EMBASE and Cochrane Library databases for studies published through May 2018 was performed using the following search algorithm: (a) “choline” or “fluorocholine” or “F-choline” or “C-choline” or “FCH” or “CH” or “FECH” or “FMCH” and (b) “PET” or “positron emission tomography” and (c) “parathyroid” or “hyperparathyroidism”. The diagnostic performance of radiolabelled choline PET was expressed as sensitivity and positive predictive value (PPV) on a per-patient and per-lesion basis and as detection rate (DR) on a per-patient basis, with pooled proportion and 95% confidence interval (95% CI) obtained using a random-effects model.
Results
Eighteen studies were included in the systematic review. Fourteen articles (517 patients) were selected for the meta-analysis. The meta-analysis provided the following results on a per-patient analysis analysis: sensitivity 95% (95% CI: 92–97%), PPV 97% (95% CI: 95–98%) and DR 91% (95% CI: 87–94%). On a per-lesion analysis, pooled sensitivity and PPV were 92% (95% CI: 88–96) and 92% (95% CI: 89–95%), respectively. No significant heterogeneity was found among the selected studies.
Conclusions
Radiolabelled choline PET demonstrated excellent diagnostic performance in detecting HP in patients with HPT. Large multicentre studies and cost-effectiveness analyses are needed to better define the role of this imaging method in this setting.
The aim of this study was to derive reference values of 18F-fluoro-ethyl-L-tyrosine positron emission tomography (18F-FET-PET) uptake in normal brain and head structures to allow for differentiation ...from tumor tissue.
We examined the datasets of 70 patients (median age 53 years, range 15-79), whose dynamic 18F-FET-PET was acquired between January 2016 and October 2017. Maximum standardized uptake value (SUVmax), target-to-background standardized uptake value ratio (TBR), and time activity curve (TAC) of the 18F-FET-PET were assessed in tumor tissue and in eight normal anatomic structures and compared using the t-test and Mann-Whitney U-test. Correlation analyses were performed using Pearson or Spearman coefficients, and comparisons between several variables with Pearson's chi-squared tests and Kruskal-Wallis tests as well as the Benjamini-Hochberg correction.
All analyzed structures showed an 18F-FET uptake higher than background (threshold: TBR > 1.5). The venous sinuses and cranial muscles exhibited a TBR of 2.03±0.46 (confidence interval (CI) 1.92-2.14), higher than the uptake of caudate nucleus, pineal gland, putamen, and thalamus (TBR 1.42±0.17, CI 1.38-1.47). SUVmax, TBR, and TAC showed no difference in the analyzed structures between subjects with high-grade gliomas and subjects with low-grade gliomas, except the SUVmax of the pineal gland (t-tests of the pineal gland: SUVmax: p = 0.022; TBR: p = 0.411). No significant differences were found for gender and age.
Normal brain tissue demonstrates increased 18F-FET uptake compared to background tissue. Two distinct clusters have been identified, comprising venous structures and gray matter with a reference uptake of up to SUVmax of 2.99 and 2.33, respectively.
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. ...We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD).
1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress
Tc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance.
718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73;
< 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (
< 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (
< 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (
= 0.3).
DL improves automatic interpretation of MPI as compared with current quantitative methods.
Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high ...levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference.
This retrospective study includes 43 patients who underwent clinically indicated CCTA and ICA. Datasets were reconstructed with ASiR-V 70% (using standard SD and high-definition HD kernels) and with DLIR at different levels (i.e., medium M and high H). Image noise, image quality, and coronary luminal narrowing were evaluated by three blinded readers. Diagnostic accuracy was compared against ICA.
Noise did not significantly differ between ASiR-V SD and DLIR-M (37 vs. 37 HU, p = 1.000), but was significantly lower in DLIR-H (30 HU, p < 0.001) and higher in ASiR-V HD (53 HU, p < 0.001). Image quality was higher for DLIR-M and DLIR-H (3.4–3.8 and 4.2–4.6) compared to ASiR-V SD and HD (2.1–2.7 and 1.8–2.2; p < 0.001), with DLIR-H yielding the highest image quality. Consistently across readers, no significant differences in sensitivity (88% vs. 92%; p = 0.453), specificity (73% vs. 73%; p = 0.583) and diagnostic accuracy (80% vs. 82%; p = 0.366) were found between ASiR-V HD and DLIR-H.
DLIR significantly reduces noise in CCTA compared to ASiR-V, while yielding superior image quality at equal diagnostic accuracy.
The present study evaluated the impact of deep-learning image reconstruction (DLIR) on noise, image quality, and diagnostic accuracy. In 43 patients who underwent clinically indicated coronary CT angiography and invasive coronary angiography, image quality was improved by up to 62% at similar noise levels. In addition, DLIR-H yielded the highest noise reduction (up to 43%) and best image quality (improvement of up to 138%). More importantly, sensitivity (92% vs. 88%), specificity (73% vs. 73%) and diagnostic accuracy (82% vs. 80%) of DLIR were at least non-inferior to ASiR-V.
Abstract Anomalous coronary arteries (ACA) represent a congenital disorder with an anomalous location of the coronary ostium and/or vascular course. Although most individuals with ACA are ...asymptomatic and remain undiagnosed, some ACA variants are clinically significant leading to symptoms and even adverse cardiac events. Currently, disease prevalence, pathophysiological mechanisms, risks of sudden cardiac death, and the optimal assessment and treatment strategies among subtypes of ACA remain largely unknown. Consequently, there is a lack of guidelines regarding imaging, sport restriction, and treatment options in individuals with ACA at all ages. Cardiac imaging techniques may play a pivotal role in the assessment of individuals with ACA and may offer guidance toward an optimal treatment strategy. This state-of-the-art review highlights current challenges and future perspectives with a special focus on the role of noninvasive multimodality imaging in patients with ACA.
Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater ...number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics.
The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001).
Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.
Abstract
Aim
The long-term prognostic benefit of coronary computed tomographic angiography (CCTA) findings of coronary artery disease (CAD) in asymptomatic populations is unknown.
Methods and results
...From the prospective multicentre international CONFIRM long-term study, we evaluated asymptomatic subjects without known CAD who underwent both coronary artery calcium scoring (CACS) and CCTA (n = 1226). Coronary computed tomographic angiography findings included the severity of coronary artery stenosis, plaque composition, and coronary segment location. Using the C-statistic and likelihood ratio tests, we evaluated the incremental prognostic utility of CCTA findings over a base model that included a panel of traditional risk factors (RFs) as well as CACS to predict long-term all-cause mortality. During a mean follow-up of 5.9 ± 1.2 years, 78 deaths occurred. Compared with the traditional RF alone (C-statistic 0.64), CCTA findings including coronary stenosis severity, plaque composition, and coronary segment location demonstrated improved incremental prognostic utility beyond traditional RF alone (C-statistics range 0.71–0.73, all P < 0.05; incremental χ2 range 20.7–25.5, all P < 0.001). However, no added prognostic benefit was offered by CCTA findings when added to a base model containing both traditional RF and CACS (C-statistics P > 0.05, for all).
Conclusions
Coronary computed tomographic angiography improved prognostication of 6-year all-cause mortality beyond a set of conventional RF alone, although, no further incremental value was offered by CCTA when CCTA findings were added to a model incorporating RF and CACS.
Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, ...clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI.
This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models.
Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation.
Magnetic resonance (MR) imaging is widely used for diagnostic imaging in medicine as it is considered a safe alternative to ionizing radiation-based techniques. Recent reports on potential genotoxic ...effects of strong and fast switching electromagnetic gradients such as used in cardiac MR (CMR) have raised safety concerns. The aim of this study was to analyse DNA double-strand breaks (DSBs) in human blood lymphocytes before and after CMR examination.
In 20 prospectively enrolled patients, peripheral venous blood was drawn before and after 1.5 T CMR scanning. After density gradient cell separation of blood samples, DNA DSBs in lymphocytes were quantified using immunofluorescence microscopy and flow cytometric analysis. Wilcoxon signed-rank testing was used for statistical analysis. Immunofluorescence microscopic and flow cytometric analysis revealed a significant increase in median numbers of DNA DSBs in lymphocytes induced by routine 1.5 T CMR examination.
The present findings indicate that CMR should be used with caution and that similar restrictions may apply as for X-ray-based and nuclear imaging techniques in order to avoid unnecessary damage of DNA integrity with potential carcinogenic effect.
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