Hippocampal calcification on computed tomography in relation to cognitive decline in memory clinic patients: A case-control study

Background It was recently shown that calcification of the hippocampus can be detected on computed tomography (CT) images and these calcifications occur in up to 20% of people over 50 years of age. However, little is known about hippocampal calcification and its relation to cognition and cognitive decline. Therefore, the aim of this study was to (1) determine the prevalence of hippocampal calcification on CT in memory clinic patients controls, and (2) to assess its relation with cognitive decline. Methods 67 patients from a memory clinic (cases) were matched by age and gender to a control group. In both groups, hippocampal calcification was assessed by two raters on thin slice, non-contrast enhanced brain CT images. Calcifications were scored bilaterally on presence and severity (absent, mild, moderate, severe). Mini Mental State Exam (MMSE) score was determined in cases. Results Hippocampal calcification presence was significantly higher in cases (N = 26, 38.8%) compared to controls (N = 9, 13.4%) (P < .01) with an odds ratio of 4.40 (95%CI: 1.63-14.87). In cases, MMSE score was significantly lower in those with hippocampal calcification compared to those without (21.6 vs 24.5, p = .02). Conclusion In this case-control study we found significantly more hippocampal calcification in patients with cognitive decline as compared to controls. Furthermore, within the cases, MMSE score was significantly lower in those with hippocampal calcification

De radioloog als behandelaar bij kanker: oncologische interventieradiologie.

Interventional oncology is a new specialism which focuses on image-guided minimal-invasive treatment of cancer patients. Interventional oncology has joined the traditional treatments of surgery, chemotherapy and radiotherapy as the fourth pillar of cancer care. Oncological interventions can be divided into three categories: intra-arterial techniques, tumour ablation techniques, and palliative procedures. Two examples of such interventions in Dutch hospitals are the intra-arterial Yttrium-90 microsphere radioembolisation of colorectal liver metastases and the CT-guided radiofrequency ablation of tumours such as renal cell carcinoma. In interventional oncology all procedures are performed under image guidance. Imaging is used to guide the instruments and for real-time monitoring of the procedure

Impact of revised Task Force Criteria: Distinguishing the athlete's heart from ARVC/D using cardiac magnetic resonance imaging

Background: Cardiac magnetic resonance (CMR) evaluation of athletes for arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is complicated by overlapping features such as right ventricular (RV) volume increase. The revised ARVC/D diagnostic Task Force Criteria (TFC) incorporate cut-off values for RV ejection fraction (EF) and RV end-diastolic volume (EDV) on CMR.Design: To distinguish ARVC/D patients from athletes we compared CMR ventricular volumes, function, TFC cut-off values, and LV/RV ratios since athletes show proportionate, and ARVC/D patients disproportionate, changes in LV and RV.Methods: Quantitative CMR parameters of 33 ARVC/D patients (64% male, mean age 45.4 years, diagnosed by revised TFC), 66 healthy athletes and 66 healthy non-athletes (sex and age matched) were compared using revised TFC and new cut-off values representing LV/RV balance.Results and conclusions: Absolute values for ARVC/D patients/athletes/non-athletes were: in males, RV EDV 149/133/106 ml/m2, ratio EDV LV/RV 0.70/0.91/0.93, RV EF 34/52/54%, LV EF 48/57/58%, ratio EF LV/RV 1.49/1.10/1.09; and in females, RV EDV 115/115/91 ml/m2, ratio EDV LV/RV 0.86/0.94/0.97, RV EF 43/54/58%, LV EF 52/57/61%, ratio EF LV/RV 1.23/1.08/1.04 (p-values < 0.05). Areas under the ROC-curve are 0.68 (RV EDV index), 0.84 (LV/RV EDV ratio) and 0.93 (RV EF), demonstrating significantly (p < 0.001) better performance of RV EF and LV/RV EDV ratio. If a wall motion abnormality is present (observed in 30 ARVC/D patients and not in healthy subjects), RV EF can help distinguish ARVC/D from physiological cardiac adaptation in athletes on CMR whereas RV EDV index cannot. A good alternative in athletes is the LV/RV EDV ratio, representing normal proportionate adaptation of both ventricles

Etidronate halts systemic arterial calcification in pseudoxanthoma elasticum

Background and aims: In pseudoxanthoma elasticum (PXE), low levels of inorganic pyrophosphate result in extensive arterial calcification. Recently, the treatment of ectopic mineralization in the PXE (TEMP) trial showed that one year of treatment with etidronate halts progression of femoral artery calcification in PXE patients. The aim of this study was to test the efficacy of etidronate on calcification in different vascular beds. Methods: In this prespecified post-hoc analysis of the TEMP trial, arterial calcification mass was quantified in the carotid siphon, common carotid artery, thoracic and abdominal aorta, coronary arteries, iliac arteries, and the femoropopliteal and crural arteries using CT at baseline and after one year of etidronate treatment or placebo. In addition, a total arterial calcification score was calculated. The difference in calcification progression was compared between the etidronate and placebo group. Results: 74 PXE patients were enrolled and randomized. Etidronate significantly halted progression of calcification in all vascular beds except for the coronary arteries. For the total arterial calcification score, the median absolute increase in mass score was −63.6 (−438.4–42.2) vs. 113.7 (9.4–377.1) (p < 0.01) and the median relative increase was −2.4% (−10.3–3.8) vs. 6.3% (0.2–15.8) (p < 0.01) in the etidronate and placebo arm, respectively. Conclusions: Etidronate treatment halts systemic arterial calcification in PXE. Further research must assess the long term safety and efficacy of etidronate on clinical outcomes in PXE

Etidronate halts systemic arterial calcification in pseudoxanthoma elasticum

Background and aims: In pseudoxanthoma elasticum (PXE), low levels of inorganic pyrophosphate result in extensive arterial calcification. Recently, the treatment of ectopic mineralization in the PXE (TEMP) trial showed that one year of treatment with etidronate halts progression of femoral artery calcification in PXE patients. The aim of this study was to test the efficacy of etidronate on calcification in different vascular beds. Methods: In this prespecified post-hoc analysis of the TEMP trial, arterial calcification mass was quantified in the carotid siphon, common carotid artery, thoracic and abdominal aorta, coronary arteries, iliac arteries, and the femoropopliteal and crural arteries using CT at baseline and after one year of etidronate treatment or placebo. In addition, a total arterial calcification score was calculated. The difference in calcification progression was compared between the etidronate and placebo group. Results: 74 PXE patients were enrolled and randomized. Etidronate significantly halted progression of calcification in all vascular beds except for the coronary arteries. For the total arterial calcification score, the median absolute increase in mass score was −63.6 (−438.4–42.2) vs. 113.7 (9.4–377.1) (p < 0.01) and the median relative increase was −2.4% (−10.3–3.8) vs. 6.3% (0.2–15.8) (p < 0.01) in the etidronate and placebo arm, respectively. Conclusions: Etidronate treatment halts systemic arterial calcification in PXE. Further research must assess the long term safety and efficacy of etidronate on clinical outcomes in PXE

Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT

The amount of calcifications in the coronary arteries is a powerful and independent predictor of cardiovascular events and is used to identify subjects at high risk who might benefit from preventive treatment. Routine quantification of coronary calcium scores can complement screening programs using low-dose chest CT, such as lung cancer screening. We present a system for automatic coronary calcium scoring based on deep convolutional neural networks (CNNs). The system uses three independently trained CNNs to estimate a bounding box around the heart. In this region of interest, connected components above 130 HU are considered candidates for coronary artery calcifications. To separate them from other high intensity lesions, classification of all extracted voxels is performed by feeding two-dimensional 50 mm × 50 mm patches from three orthogonal planes into three concurrent CNNs. The networks consist of three convolutional layers and one fully-connected layer with 256 neurons. In the experiments, 1028 non-contrast-enhanced and non-ECG-triggered low-dose chest CT scans were used. The network was trained on 797 scans. In the remaining 231 test scans, the method detected on average 194.3 mm3 of 199.8 mm3 coronary calcifications per scan (sensitivity 97.2 %) with an average false-positive volume of 10.3 mm3. Subjects were assigned to one of five standard cardiovascular risk categories based on the Agatston score. Accuracy of risk category assignment was 84.4 % with a linearly weighted κ of 0.89. The proposed system can perform automatic coronary artery calcium scoring to identify subjects undergoing low-dose chest CT screening who are at risk of cardiovascular events with high accuracy.</p

Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT

The amount of calcifications in the coronary arteries is a powerful and independent predictor of cardiovascular events and is used to identify subjects at high risk who might benefit from preventive treatment. Routine quantification of coronary calcium scores can complement screening programs using low-dose chest CT, such as lung cancer screening. We present a system for automatic coronary calcium scoring based on deep convolutional neural networks (CNNs). The system uses three independently trained CNNs to estimate a bounding box around the heart. In this region of interest, connected components above 130 HU are considered candidates for coronary artery calcifications. To separate them from other high intensity lesions, classification of all extracted voxels is performed by feeding two-dimensional 50 mm × 50 mm patches from three orthogonal planes into three concurrent CNNs. The networks consist of three convolutional layers and one fully-connected layer with 256 neurons. In the experiments, 1028 non-contrast-enhanced and non-ECG-triggered low-dose chest CT scans were used. The network was trained on 797 scans. In the remaining 231 test scans, the method detected on average 194.3 mm3 of 199.8 mm3 coronary calcifications per scan (sensitivity 97.2 %) with an average false-positive volume of 10.3 mm3. Subjects were assigned to one of five standard cardiovascular risk categories based on the Agatston score. Accuracy of risk category assignment was 84.4 % with a linearly weighted κ of 0.89. The proposed system can perform automatic coronary artery calcium scoring to identify subjects undergoing low-dose chest CT screening who are at risk of cardiovascular events with high accuracy.</p

Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT

The amount of calcifications in the coronary arteries is a powerful and independent predictor of cardiovascular events and is used to identify subjects at high risk who might benefit from preventive treatment. Routine quantification of coronary calcium scores can complement screening programs using low-dose chest CT, such as lung cancer screening. We present a system for automatic coronary calcium scoring based on deep convolutional neural networks (CNNs). The system uses three independently trained CNNs to estimate a bounding box around the heart. In this region of interest, connected components above 130 HU are considered candidates for coronary artery calcifications. To separate them from other high intensity lesions, classification of all extracted voxels is performed by feeding two-dimensional 50 mm × 50 mm patches from three orthogonal planes into three concurrent CNNs. The networks consist of three convolutional layers and one fully-connected layer with 256 neurons. In the experiments, 1028 non-contrast-enhanced and non-ECG-triggered low-dose chest CT scans were used. The network was trained on 797 scans. In the remaining 231 test scans, the method detected on average 194.3 mm3 of 199.8 mm3 coronary calcifications per scan (sensitivity 97.2 %) with an average false-positive volume of 10.3 mm3. Subjects were assigned to one of five standard cardiovascular risk categories based on the Agatston score. Accuracy of risk category assignment was 84.4 % with a linearly weighted κ of 0.89. The proposed system can perform automatic coronary artery calcium scoring to identify subjects undergoing low-dose chest CT screening who are at risk of cardiovascular events with high accuracy.</p