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Purpose Low back pain (LBP) is one of the most prevalent health condition worldwide and responsible for the most years lived with disability, yet the etiology is often unknown. Magnetic resonance imaging (MRI) is frequently used for treatment decision even though it is often inconclusive. There are many different image features that could relate to low back pain. Conversely, multiple etiologies do relate to spinal degeneration but do not actually cause the perceived pain. This narrative review provides an overview of all possible relevant features visible on MRI images and determines their relation to LBP. Methods We conducted a separate literature search per image feature. All included studies were scored using the GRADE guidelines. Based on the reported results per feature an evidence agreement (EA) score was provided, enabling us to compare the collected evidence of separate image features. The various relations between MRI features and their associated pain mechanisms were evaluated to provide a list of features that are related to LBP. Results All searches combined generated a total of 4472 hits of which 31 articles were included. Features were divided into five different categories:’discogenic’, ‘neuropathic’,’osseous’, ‘facetogenic’, and’paraspinal’, and discussed separately. Conclusion Our research suggests that type I Modic changes, disc degeneration, endplate defects, disc herniation, spinal canal stenosis, nerve compression, and muscle fat infiltration have the highest probability to be related to LBP. These can be used to improve clinical decision-making for patients with LBP based on MRI.

Contains fulltext : 251973.pdf (Publisher’s version ) (Closed access) Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components. However, the choice of the components of these algorithms was often pragmatic rather than systematic and systems were not compared to each other across papers in a fair manner. We systematically investigated the effectiveness of using 3-D CNNs instead of 2-D CNNs for seven commonly used architectures, including DenseNet, Inception, and ResNet variants. For the architecture that performed best, we furthermore investigated the effect of initializing the network with pretrained weights, providing automatically computed lesion maps as additional network input, and predicting a continuous instead of a categorical output. A 3-D DenseNet-201 with these components achieved an area under the receiver operating characteristic curve of 0.930 on our test set of 105 CT scans and an AUC of 0.919 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2-D CNN. This article provides insights into the performance benefits of various components for COVID-19 classification and grading systems. We have created a challenge on grand-challenge.org to allow for a fair comparison between the results of this and future research.

Contains fulltext : 235298.pdf (Publisher’s version ) (Open Access) Importance: Cardiovascular disease (CVD) is common in patients treated for breast cancer, especially in patients treated with systemic treatment and radiotherapy and in those with preexisting CVD risk factors. Coronary artery calcium (CAC), a strong independent CVD risk factor, can be automatically quantified on radiotherapy planning computed tomography (CT) scans and may help identify patients at increased CVD risk. Objective: To evaluate the association of CAC with CVD and coronary artery disease (CAD) in patients with breast cancer. Design, Setting, and Participants: In this multicenter cohort study of 15915 patients with breast cancer receiving radiotherapy between 2005 and 2016 who were followed until December 31, 2018, age, calendar year, and treatment-adjusted Cox proportional hazard models were used to evaluate the association of CAC with CVD and CAD. Exposures: Overall CAC scores were automatically extracted from planning CT scans using a deep learning algorithm. Patients were classified into Agatston risk categories (0, 1-10, 11-100, 101-399, >400 units). Main Outcomes and Measures: Occurrence of fatal and nonfatal CVD and CAD were obtained from national registries. Results: Of the 15915 participants included in this study, the mean (SD) age at CT scan was 59.0 (11.2; range, 22-95) years, and 15879 (99.8%) were women. Seventy percent (n = 11179) had no CAC. Coronary artery calcium scores of 1 to 10, 11 to 100, 101 to 400, and greater than 400 were present in 10.0% (n = 1584), 11.5% (n = 1825), 5.2% (n = 830), and 3.1% (n = 497) respectively. After a median follow-up of 51.2 months, CVD risks increased from 5.2% in patients with no CAC to 28.2% in patients with CAC scores higher than 400. After adjustment, CVD risk increased with higher CAC score (hazard ratio [HR]CAC = 1-10 = 1.1; 95% CI, 0.9-1.4; HRCAC = 11-100 = 1.8; 95% CI, 1.5-2.1; HRCAC = 101-400 = 2.1; 95% CI, 1.7-2.6; and HRCAC>400 = 3.4; 95% CI, 2.8-4.2). Coronary artery calcium was particularly strongly associated with CAD (HRCAC>400 = 7.8; 95% CI, 5.5-11.2). The association between CAC and CVD was strongest in patients treated with anthracyclines (HRCAC>400 = 5.8; 95% CI, 3.0-11.4) and patients who received a radiation boost (HRCAC>400 = 6.1; 95% CI, 3.8-9.7). Conclusions and Relevance: This cohort study found that coronary artery calcium on breast cancer radiotherapy planning CT scan results was associated with CVD, especially CAD. Automated CAC scoring on radiotherapy planning CT scans may be used as a fast and low-cost tool to identify patients with breast cancer at increased risk of CVD, allowing implementing CVD risk-mitigating strategies with the aim to reduce the risk of CVD burden after breast cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03206333.

Purpose Vitamin K-dependent proteins are involved in (patho)physiological calcification of the vasculature and the bones. Type 2 diabetes mellitus (DM2) is associated with increased arterial calcification and increased fractures. This study investigates the effect of 6 months vitamin K2 supplementation on systemic arterial calcification and bone mineral density (BMD) in DM2 patients with a history of cardiovascular disease (CVD). Methods In this pre-specified, post hoc analysis of a double-blind, randomized, controlled clinical trial, patients with DM2 and CVD were randomized to a daily, oral dose of 360 µg vitamin K2 or placebo for 6 months. CT scans were made at baseline and follow-up. Arterial calcification mass was quantified in several large arterial beds and a total arterial calcification mass score was calculated. BMD was assessed in all non-fractured thoracic and lumbar vertebrae. Results 68 participants were randomized, 35 to vitamin K2 (33 completed follow-up) and 33 to placebo (27 completed follow-up). The vitamin K group had higher arterial calcification mass at baseline [median (IQR): 1694 (812–3584) vs 1182 (235–2445)] for the total arterial calcification mass). Six months vitamin K supplementation did not reduce arterial calcification progression (β [95% CI]: − 0.02 [− 0.10; 0.06] for the total arterial calcification mass) or slow BMD decline (β [95% CI]: − 2.06 [− 11.26; 7.30] Hounsfield units for all vertebrae) when compared to placebo. Conclusion Six months vitamin K supplementation did not halt progression of arterial calcification or decline of BMD in patients with DM2 and CVD. Future clinical trials may want to pre-select patients with very low vitamin K status and longer follow-up time might be warranted. This trial was registered at clinicaltrials.gov as NCT02839044

Contains fulltext : 251974.pdf (Publisher’s version ) (Closed access)

Contains fulltext : 252007.pdf (Publisher’s version ) (Open Access) Purpose: Ensembles of convolutional neural networks (CNNs) often outperform a single CNN in medical image segmentation tasks, but inference is computationally more expensive and makes ensembles unattractive for some applications. We compared the performance of differently constructed ensembles with the performance of CNNs derived from these ensembles using knowledge distillation, a technique for reducing the footprint of large models such as ensembles. Approach: We investigated two different types of ensembles, namely, diverse ensembles of networks with three different architectures and two different loss-functions, and uniform ensembles of networks with the same architecture but initialized with different random seeds. For each ensemble, additionally, a single student network was trained to mimic the class probabilities predicted by the teacher model, the ensemble. We evaluated the performance of each network, the ensembles, and the corresponding distilled networks across three different publicly available datasets. These included chest computed tomography scans with four annotated organs of interest, brain magnetic resonance imaging (MRI) with six annotated brain structures, and cardiac cine-MRI with three annotated heart structures. Results: Both uniform and diverse ensembles obtained better results than any of the individual networks in the ensemble. Furthermore, applying knowledge distillation resulted in a single network that was smaller and faster without compromising performance compared with the ensemble it learned from. The distilled networks significantly outperformed the same network trained with reference segmentation instead of knowledge distillation. Conclusion: Knowledge distillation can compress segmentation ensembles of uniform or diverse composition into a single CNN while maintaining the performance of the ensemble.

BackgroundA baseline computed tomography (CT) scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC risk and a high CD risk.MethodsParticipant demographics and quantitative CT measures of LC, cardiovascular disease and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting 5-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data were used to perform external validation (n=2287).ResultsOur final CD model outperformed an external pre-scan model (CD Risk Assessment Tool) in both the derivation (area under the curve (AUC) 0.744 (95% CI 0.727–0.761) and 0.677 (95% CI 0.658–0.695), respectively) and validation cohorts (AUC 0.744 (95% CI 0.652–0.835) and 0.725 (95% CI 0.633–0.816), respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096 (27%)) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287 (34%)) were 129 (versus 29) and 1.67 (versus 0.43).ConclusionsEvaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration ap- proaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi- task medical image registration data set for comprehen- sive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, ac- curacy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image regis- tration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state- of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Plasma osteoprotegerin (OPG) and vascular smooth muscle cell (VSMC) derived extracellular vesicles (EVs) are important regulators in the process of vascular calcification (VC). In population studies, high levels of OPG are associated with events. In animal studies, however, high OPG levels result in reduction of VC. VSMC-derived EVs are assumed to be responsible for OPG transport and VC but this role has not been studied. For this, we investigated the association between OPG in plasma and circulating EVs with coronary artery calcium (CAC) as surrogate for VC in symptomatic patients. We retrospectively assessed 742 patients undergoing myocardial perfusion imaging (MPI). CAC scores were determined on the MPI-CT images using a previously developed automated algorithm. Levels of OPG were quantified in plasma and two EV-subpopulations (LDL and TEX), using an electrochemiluminescence immunoassay. Circulating levels of OPG were independently associated with CAC scores in plasma; OR 1.39 (95% CI 1.17–1.65), and both EV populations; EV-LDL; OR 1.51 (95% CI 1.27–1.80) and EV-TEX; OR 1.21 (95% CI 1.02–1.42). High levels of OPG in plasma were independently associated with CAC scores in this symptomatic patient cohort. High levels of EV-derived OPG showed the same positive association with CAC scores, suggesting that EV-derived OPG mirrors the same pathophysiological process as plasma OPG.

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias field to remove intensity inhomogeneity as a preprocessing step to segmentation, we propose to perform segmentation using a convolutional neural network that exploits images with synthetically introduced intensity inhomogeneity as data augmentation. The method first uses a CNN to extract the intracranial volume. Thereafter, another CNN with the same architecture is employed to segment the extracted volume into seven brain tissue classes: cerebellum, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter and extracerebral cerebrospinal fluid. To make the method applicable to slices showing intensity inhomogeneity artifacts, the training data was augmented by applying a combination of linear gradients with random offsets and orientations to image slices without artifacts., Comment: Published in Magnetic Resonance Imaging, 2019

Purpose The purpose of this study was to elucidate the impact of being unmarried on coronary computed tomography (CT) imaging biomarkers and mortality in a lung cancer screening population. Materials and methods In this retrospective case-control study, 5707 subjects (3777 married; mean age: 61.9±5.1 y and 1930 unmarried; mean age: 61.9±5.3 y) underwent low-dose CT as part of the National Lung Screening Trial (NLST). The median follow-up time was 6.5 (Q1-Q3: 5.6 to 6.9) years. Being unmarried was defined as never married, widowed, separated, or divorced. Being married was defined as married or living as married. Our primary endpoint was cardiovascular disease (CVD)-related death; our secondary endpoint was all-cause mortality. Coronary CT imaging biomarkers (calcium score, density, and volume) on low-dose chest CT scan were calculated using dedicated automatic software. Weighted Cox proportional-hazards regression was performed to examine the association between marital status and death. Kaplan-Meier curves were generated to visualize subject survival. Results Being unmarried was significantly associated with an increased risk for CVD-related death (hazard ratio [HR]: 1.58; 95% confidence interval [CI]: 1.31-1.91) and all-cause mortality (HR: 1.39; 95% CI: 1.26-1.53), which remained significant even after adjusting for traditional cardiovascular risk factors (HR CVD death: 1.75; 1.44-2.12 and HR all-cause mortality: 1.58; 95% CI: 1.43-1.74) and coronary calcium score (HR CVD death: 1.58; 95% CI: 1.31-1.91 and HR all-cause mortality: 1.40; 95% CI: 1.27-1.54). Conclusions Being unmarried is associated with an increased CVD-related death and all-cause mortality mainly due to cardiovascular etiology. On the basis of this, marital status might be taken into consideration when assessing individuals' health status.

Objectives The aim of this study was to investigate sex differences in the prevalence, extent, and association of coronary artery calcium (CAC) and thoracic aorta calcium (TAC) scores with cardiovascular mortality in a population eligible for lung screening. Background CAC and TAC scores derived from chest computed tomography (CT) might be useful biomarkers for individualized cardiovascular disease prevention and could be especially relevant in high-risk populations such as heavy smokers. Therefore, it is important to know the prevalence of arterial calcifications in male and female heavy smokers, and if there are differences in the predictive value calcifications carry. Methods We performed a nested case–control study with 5,718 participants of the CT arm of the NLST (National Lung Screening Trial). Prevalence and extent of CAC and TAC were resampled to the full cohort to provide unbiased estimates of the typical calcium burden of male and female heavy smokers. Weighted Cox proportional hazards regression was used to assess differences in the association of CAC and TAC scores with all-cause and cardiovascular mortality. Results CAC was substantially more common and more severe in men (prevalence: 81% vs. 60%; median volume: 104 mm³ vs. 12 mm³). Women had CAC comparable to that of men who were 10 years younger. TAC was equally common in men and women, with a tendency to be more pronounced in women (prevalence: 92% vs. 93%; median volume: 388 mm³ vs. 404 mm³). Both types of calcification were associated with increased cardiovascular and all-cause mortality. TAC scores improved the prediction of coronary heart disease mortality over CAC in men, but not in women. In both sexes, TAC, but not CAC, was associated with cardiovascular mortality other than coronary heart disease. Conclusions CAC develops later in women, whereas TAC develops equally in both sexes. CAC is strongly associated with coronary heart disease, whereas TAC is especially associated with extracardiac vascular mortality in either sex.

Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing time-consuming intermediate CAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings., Comment: IEEE Transactions on Medical Imaging (In press)

Contains fulltext : 235733.pdf (Publisher’s version ) (Closed access) Purpose: To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality. Materials and Methods: This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test. Results: The prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69; P = .049). Best results were obtained when combining all variables (C statistic, 0.76; P < .001). Conclusion: Five-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT.(c) RSNA, 2021.