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Neuronal ceroid lipofuscinosis (NCL) is a group of neurodegenerative lysosomal storage disorders characterized by vision loss, mental and motor deficits, and spontaneous seizures. Neuropathological analyses of autopsy material from NCL patients and animal models revealed brain atrophy closely associated with glial activity. Earlier reports also noticed loss of retinal cells and reactive gliosis in some forms of NCL. To study this phenomenon in detail, we analyzed the ocular phenotype of CLN6nclf mice, an established mouse model for variant-late infantile NCL. Retinal morphometry, immunohistochemistry, optokinetic tracking, electroretinography, and mRNA expression were used to characterize retinal morphology and function as well as the responses of Müller cells and microglia. Our histological data showed a severe and progressive degeneration in the CLN6nclf retina co-inciding with reactive Müller glia. Furthermore, a prominent phenotypic transformation of ramified microglia to phagocytic, bloated, and mislocalized microglial cells was identified in CLN6nclf retinas. These events overlapped with a rapid loss of visual perception and retinal function. Based on the strong microglia reactivity we hypothesized that dietary supplementation with immuno-regulatory compounds, curcumin and docosahexaenoic acid (DHA), could ameliorate microgliosis and reduce retinal degeneration. Our analyses showed that treatment of three-week-old CLN6nclf mice with either 5% DHA or 0.6% curcumin for 30 weeks resulted in a reduced number of amoeboid reactive microglia and partially improved retinal function. DHA-treatment also improved the morphology of CLN6nclf retinas with a preserved thickness of the photoreceptor layer in most regions of the retina. Our results suggest that microglial reactivity closely accompanies disease progression in the CLN6nclf retina and both processes can be attenuated with dietary supplemented immuno-modulating compounds.
Background: Computed tomography (CT) low-dose (LD) imaging is used to lower radiation exposure, especially in vascular imaging; in current literature, this is mostly on latest generation high-end CT systems.
Purpose: To evaluate the effects of reduced tube current on objective and subjective image quality of a 15-year-old 16-slice CT system for pulmonary angiography (CTPA).
Material and Methods: CTPA scans from 60 prospectively randomized patients (28 men, 32 women) were examined in this study on a 15-year-old 16-slice CT scanner system. Standard CT (SD) settings were 100 kV and 150 mAs, LD settings were 100 kV and 50 mAs. Attenuation of the pulmonary trunk, various anatomic landmarks, and image noise were quantitatively measured; contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR) were calculated. Three independent blinded radiologists subjectively rated each image series using a 5-point grading scale.
Results: CT dose index (CTDI) in the LD series was 66.46% lower compared to the SD settings (2.49 ± 0.55 mGy versus 7.42 ± 1.17 mGy). Attenuation of the pulmonary trunk showed similar results for both series (SD 409.55 ± 91.04 HU; LD 380.43 HU ± 93.11 HU; P = 0.768). Subjective image analysis showed no significant differences between SD and LD settings regarding the suitability for detection of central and peripheral PE (central SD/LD, 4.88; intra-class correlation coefficients [ICC], 0.894/4.83; ICC, 0.745; peripheral SD/LD, 4.70; ICC, 0.943/4.57; ICC, 0.919; all P > 0.4).
Conclusion: The LD protocol, on a 15-year-old CT scanner system without current high-end hardware or post-processing tools, led to a dose reduction of approximately 67% with similar subjective image quality and delineation of central and peripheral pulmonary arteries.
Simple Summary: Early and accurate diagnosis of breast cancer that has spread to other organs and tissues is crucial, as therapeutic decisions and outcome expectations might change. Computed tomography (CT) is often used to detect breast cancer’s spread, but this method has its weaknesses. The computer-assisted technique “radiomics” extracts grey-level patterns, so-called radiomic features, from medical images, which may reflect underlying biological processes. Our retrospective study therefore evaluated whether breast cancer spread can be predicted by radiomic features derived from iodine maps, an application on a new generation of CT scanners visualizing tissue blood flow. Based on 77 patients with newly diagnosed breast cancer, we found that this approach might indeed predict cancer spread to other organs/tissues. In the future, radiomics may serve as an additional tool for cancer detection and risk assessment.
Abstract: Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differ- ences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.
Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.
Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.
Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system.
Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.
BACKGROUND: Evaluation of latest generation automated attenuation-based tube potential selection (ATPS) impact on image quality and radiation dose in contrast-enhanced chest-abdomen-pelvis computed tomography examinations for gynaecologic cancer staging.
METHODS: This IRB approved single-centre, observer-blinded retrospective study with a waiver for informed consent included a total of 100 patients with contrast-enhanced chest-abdomen-pelvis CT for gynaecologic cancer staging. All patients were examined with activated ATPS for adaption of tube voltage to body habitus. 50 patients were scanned on a third-generation dual-source CT (DSCT), and another 50 patients on a second-generation DSCT. Predefined image quality setting remained stable between both groups at 120 kV and a current of 210 Reference mAs. Subjective image quality assessment was performed by two blinded readers independently. Attenuation and image noise were measured in several anatomic structures. Signal-to-noise ratio (SNR) was calculated. For the evaluation of radiation exposure, CT dose index (CTDIvol) values were compared.
RESULTS: Diagnostic image quality was obtained in all patients. The median CTDIvol (6.1 mGy, range 3.9-22 mGy) was 40 % lower when using the algorithm compared with the previous ATCM protocol (median 10.2 mGy · cm, range 5.8-22.8 mGy). A reduction in potential to 90 kV occurred in 19 cases, a reduction to 100 kV in 23 patients and a reduction to 110 kV in 3 patients of our experimental cohort. These patients received significantly lower radiation exposure compared to the former used protocol.
CONCLUSION: Latest generation automated ATPS on third-generation DSCT provides good diagnostic image quality in chest-abdomen-pelvis CT while average radiation dose is reduced by 40 % compared to former ATPS protocol on second-generation DSCT.
Dual-energy CT (DECT) has emerged into clinical routine as an imaging technique with unique postprocessing utilities that improve the evaluation of different body areas. The virtual non-calcium (VNCa) reconstruction algorithm has shown beneficial effects on the depiction of bone marrow pathologies such as bone marrow edema. Its main advantage is the ability to substantially increase the image contrast of structures that are usually covered with calcium mineral, such as calcified vessels or bone marrow, and to depict a large number of traumatic, inflammatory, infiltrative, and degenerative disorders affecting either the spine or the appendicular skeleton. Therefore, VNCa imaging represents another step forward for DECT to image conditions and disorders that usually require the use of more expensive and time-consuming techniques such as magnetic resonance imaging, positron emission tomography/CT, or bone scintigraphy. The aim of this review article is to explain the technical background of VNCa imaging, showcase its applicability in the different body regions, and provide an updated outlook on the clinical impact of this technique, which goes beyond the sole improvement in image quality.
Background: Dual-source dual-energy computed tomography (DECT) offers the potential for opportunistic osteoporosis screening by enabling phantomless bone mineral density (BMD) quantification. This study sought to assess the accuracy and precision of volumetric BMD measurement using dual-source DECT in comparison to quantitative CT (QCT). Methods: A validated spine phantom consisting of three lumbar vertebra equivalents with 50 (L1), 100 (L2), and 200 mg/cm3 (L3) calcium hydroxyapatite (HA) concentrations was scanned employing third-generation dual-source DECT and QCT. While BMD assessment based on QCT required an additional standardised bone density calibration phantom, the DECT technique operated by using a dedicated postprocessing software based on material decomposition without requiring calibration phantoms. Accuracy and precision of both modalities were compared by calculating measurement errors. In addition, correlation and agreement analyses were performed using Pearson correlation, linear regression, and Bland-Altman plots. Results: DECT-derived BMD values differed significantly from those obtained by QCT (p < 0.001) and were found to be closer to true HA concentrations. Relative measurement errors were significantly smaller for DECT in comparison to QCT (L1, 0.94% versus 9.68%; L2, 0.28% versus 5.74%; L3, 0.24% versus 3.67%, respectively). DECT demonstrated better BMD measurement repeatability compared to QCT (coefficient of variance < 4.29% for DECT, < 6.74% for QCT). Both methods correlated well to each other (r = 0.9993; 95% confidence interval 0.9984–0.9997; p < 0.001) and revealed substantial agreement in Bland-Altman plots. Conclusions: Phantomless dual-source DECT-based BMD assessment of lumbar vertebra equivalents using material decomposition showed higher diagnostic accuracy compared to QCT.
The new class of microbial rhodopsins, called xenorhodopsins (XeRs),[1] extends the versatility of this family by inward H+ pumps.[2–4] These pumps are an alternative optogenetic tool to the light-gated ion channels (e.g. ChR1,2), because the activation of electrically excitable cells by XeRs is independent from the surrounding physiological conditions. In this work we functionally and spectroscopically characterized XeR from Nanosalina (NsXeR).[1] The photodynamic behavior of NsXeR was investigated on the ps to s time scale elucidating the formation of the J and K and a previously unknown long-lived intermediate. The pH dependent kinetics reveal that alkalization of the surrounding medium accelerates the photocycle and the pump turnover. In patch-clamp experiments the blue-light illumination of NsXeR in the M state shows a potential-dependent vectoriality of the photocurrent transients, suggesting a variable accessibility of reprotonation of the retinal Schiff base. Insights on the kinetically independent switching mechanism could furthermore be obtained by mutational studies on the putative intracellular H+ acceptor D220.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: This prospective randomized trial is designed to compare the performance of conventional transarterial chemoembolization (cTACE) using Lipiodol-only with additional use of degradable starch microspheres (DSM) for hepatocellular carcinoma (HCC) in BCLC-stage-B based on metric tumor response. Methods: Sixty-one patients (44 men; 17 women; range 44–85) with HCC were evaluated in this IRB-approved HIPPA compliant study. The treatment protocol included three TACE-sessions in 4-week intervals, in all cases with Mitomycin C as a chemotherapeutic agent. Multiparametric magnetic resonance imaging (MRI) was performed prior to the first and 4 weeks after the last TACE. Two treatment groups were determined using a randomization sheet: In 30 patients, TACE was performed using Lipiodol only (group 1). In 31 cases Lipiodol was combined with DSMs (group 2). Response according to tumor volume, diameter, mRECIST criteria, and the development of necrotic areas were analyzed and compared using the Mann–Whitney-U, Kruskal–Wallis-H-test, and Spearman-Rho. Survival data were analyzed using the Kaplan–Meier estimator. Results: A mean overall tumor volume reduction of 21.45% (± 62.34%) was observed with an average tumor volume reduction of 19.95% in group 1 vs. 22.95% in group 2 (p = 0.653). Mean diameter reduction was measured with 6.26% (± 34.75%), for group 1 with 11.86% vs. 4.06% in group 2 (p = 0.678). Regarding mRECIST criteria, group 1 versus group 2 showed complete response in 0 versus 3 cases, partial response in 2 versus 7 cases, stable disease in 21 versus 17 cases, and progressive disease in 3 versus 1 cases (p = 0.010). Estimated overall survival was in mean 33.4 months (95% CI 25.5–41.4) for cTACE with Lipiosol plus DSM, and 32.5 months (95% CI 26.6–38.4), for cTACE with Lipiodol-only (p = 0.844), respectively. Conclusions: The additional application of DSM during cTACE showed a significant benefit in tumor response according to mRECIST compared to cTACE with Lipiodol-only. No benefit in survival time was observed.