| 1. |
- Abdulla, Maysaa, et al.
(författare)
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Prognostic impact of abdominal lymph node involvement in diffuse large B-cell lymphoma
- 2020
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Ingår i: European Journal of Haematology. - : Wiley. - 0902-4441 .- 1600-0609. ; 104:3, s. 207-213
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: The prognostic value of site of nodal involvement in diffuse large B-cell lymphomas (DLBCL) is mainly unknown. We aimed to determine the prognostic significance of nodal abdominal involvement in relation to tumour cell markers and clinical characteristics of 249 DLBCL patients in a retrospective single-centre study.METHODS: Contrast-enhanced computed tomography (CT) of the abdomen and thorax revealed pathologically enlarged abdominal lymph nodes in 156 patients, while in 93 patients there were no pathologically enlarged lymph nodes in the abdomen. In 81 cases, the diagnosis of DLBCL was verified by histopathological biopsy obtained from abdominal lymph node.RESULTS: Patients with abdominal nodal disease had inferior lymphoma-specific survival (P = .04) and presented with higher age-adjusted IPI (P < .001), lactate dehydrogenase (P < .001) and more often advanced stage (P < .001), bulky disease (P < .001), B symptoms (P < .001), and double expression of MYC and BCL2 (P = .02) compared to patients without nodal abdominal involvement, but less often extranodal involvement (P < .02). The worst outcome was observed in those where the abdominal nodal involvement was verified by histopathological biopsy.CONCLUSION: Diffuse large B-cell lymphomas patients with abdominal nodal disease had inferior outcome and more aggressive behaviour, reflected both in clinical and biological characteristics.
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| 2. |
- Breznik, Eva, et al.
(författare)
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Multiple comparison correction methods for whole-body magnetic resonance imaging
- 2020
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Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses. Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions. Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate. Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.
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| 3. |
- Diamanti, Klev, 1987-, et al.
(författare)
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Integration of whole-body [18F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (Ki), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. Bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach.
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| 4. |
- Ekström, Simon, 1991-
(författare)
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Efficient GPU-based Image Registration : for Detailed Large-Scale Whole-body Analysis
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Imaging has become an important aspect of medicine, enabling visualization of internals in a non-invasive manner. The rapid advancement and adoption of imaging techniques have led to a demand for tools able to take advantage of the information that is produced. Medical image analysis aims to extract relevant information from acquired images to aid diagnostics in healthcare and increase the understanding within medical research. The main subject of this thesis, image registration, is a widely used tool in image analysis that can be employed to find a spatial transformation aligning a set of images. One application, that is described in detail in this thesis, is the use of image registration for large-scale analysis of whole-body images through the utilization of the correspondences defined by the resulting transformations. To produce detailed results, the correspondences, i.e. transformations, need to be of high resolution and the quality of the result has a direct impact on the quality of the analysis. Also, this type of application aims to analyze large cohorts and the value of a registration method is not only weighted by its ability to produce an accurate result but also by its efficiency. This thesis presents two contributions on the subject; a new method for efficient image registration with the ability to produce dense deformable transformations, and the application of the presented method in large-scale analysis of a whole-body dataset acquired using an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system. In this thesis, it is shown that efficient and detailed image registration can be performed by employing graph cuts and a heuristic where the optimization is performed on subregions of the image. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging.
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| 5. |
- Ekström, Simon, 1991-, et al.
(författare)
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Fast graph-cut based optimization for practical dense deformable registration of volume images
- 2020
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Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier. - 0895-6111 .- 1879-0771. ; 84
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Tidskriftsartikel (refereegranskat)abstract
- Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas-based image segmentation. Registration is often phrased as an optimization problem, i.e., finding a deformation field that is optimal according to a given objective function. Discrete, combinatorial, optimization techniques have successfully been employed to solve the resulting optimization problem. Specifically, optimization based on α-expansion with minimal graph cuts has been proposed as a powerful tool for image registration. The high computational cost of the graph-cut based optimization approach, however, limits the utility of this approach for registration of large volume images. Here, we propose to accelerate graph-cut based deformable registration by dividing the image into overlapping sub-regions and restricting the α-expansion moves to a single sub-region at a time. We demonstrate empirically that this approach can achieve a large reduction in computation time - from days to minutes - with only a small penalty in terms of solution quality. The reduction in computation time provided by the proposed method makes graph-cut based deformable registration viable for large volume images. Graph-cut based image registration has previously been shown to produce excellent results, but the high computational cost has hindered the adoption of the method for registration of large medical volume images. Our proposed method lifts this restriction, requiring only a small fraction of the computational cost to produce results of comparable quality.
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| 6. |
- Guglielmo, Priscilla, et al.
(författare)
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Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [18F]FDG-PET/MR images. Twelve subjects underwent whole-body [18F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.
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| 7. |
- Kuzniar, Marek, et al.
(författare)
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Feasibility of Assessing Inflammation in Asymptomatic Abdominal Aortic Aneurysms With Integrated 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging
- 2020
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Ingår i: European Journal of Vascular and Endovascular Surgery. - : Elsevier. - 1078-5884 .- 1532-2165. ; 59:3, s. 464-471
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: This study aimed to evaluate the feasibility of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) combined with contrast enhanced magnetic resonance imaging (MRI) to identify inflammation in asymptomatic abdominal aortic aneurysms (AAA).METHODS: FDG PET/MRI was performed on 15 patients with asymptomatic infrarenal AAAs >45 mm diameter. Prevalence of FDG uptake and MRI findings of inflammatory changes (oedema, wall thickening, and late gadolinium enhancement [LGE]) in the aortic wall were investigated at three levels: suprarenal aorta; non-aneurysmal aortic neck; and AAA.RESULTS: The median diameter of the AAAs was 54 mm (range 47-65 mm) and the median expansion rate in the last 12 months was 3 mm (range 1-13 mm). The standard uptake value (SUV) of FDG in the aneurysmal wall (SUVmax 2.5) was higher than the blood pool (SUVmax 1.0; p < .001). The maximum target to background ratio was higher in the suprarenal aorta (mean ± SD; 3.1 ± 0.6) and aortic neck (2.7 ± 0.5) than in the aneurysmal aorta (2.5 ± 0.5; p < .001). Thirty-six FDG hotspots were observed in the aneurysmal wall of 13 patients. Wall thickening and LGE were identified in eight patients. The number of FDG hotspots correlated with recent AAA growth (r = 0.62, p = .01). The recent aneurysm expansion rate was higher in aneurysms with LGE than in those without (7 mm vs. 2 mm; p = .03). MRI inflammatory changes were observed in nine of 36 hot spots (25%) and in three of 13 patients with focal FDG uptake.CONCLUSION: Fully integrated FDG PET/MRI can be used to study inflammation in asymptomatic AAAs. Heterogenous uptake of FDG in the aneurysmal wall indicates increased glucose metabolism, suggesting an ongoing inflammation. However, these FDG hotspots rarely correspond to MRI findings of inflammation, raising the question of which type of cellular activity is present in these areas. The presence of LGE and FDG hotspots both correlated to recent aneurysm growth, and their usefulness as clinical markers of aneurysm growth warrant additional investigation.
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| 8. |
- Langner, Taro, et al.
(författare)
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Identifying morphological indicators of aging with neural networks on large-scale whole-body MRI
- 2020
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Ingår i: IEEE Transactions on Medical Imaging. - 0278-0062 .- 1558-254X. ; 39:5, s. 1430-1437
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Tidskriftsartikel (refereegranskat)abstract
- A wealth of information is contained in images obtained by whole-body magnetic resonance imaging (MRI). Studying the link between the imaged anatomy and properties known from outside sources has the potential to give new insights into the underlying factors that manifest themselves in individual human morphology. In this work we investigate the expression of age-related changes in the whole-body image. A large dataset of about 32,000 subjects scanned from neck to knee and aged 44–82 years from the UK Biobank study was used for a machine-based analysis. We trained a convolutional neural network based on the VGG16 architecture to predict the age of a given subject based on image data from these scans. In 10-fold cross-validation on 23,000 of these images the network reached a mean absolute error (MAE) of 2.49 years (R 2 = 0.83) and showed consistent performance on a separate test set of another 8,000 images. On a second test set of 100 images the network outperformed the averaged estimates given by three experienced radiologists, which reached an MAE of 5.58 years (R 2 = 0.08), by more than three years on average. In an attempt to explain these findings, we employ saliency analysis that opens up the image-based criteria used by the automated method to human interpretation. We aggregate the saliency into a single anatomical visualization which clearly highlights structures in the aortic arch and knee as primary indicators of age.
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| 9. |
- Langner, Taro, et al.
(författare)
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Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.
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| 10. |
- Langner, Taro, et al.
(författare)
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Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
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