Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers.

Protocol of the Berlin Long-term Observation of Vascular Events (BeLOVE): a prospective cohort study with deep phenotyping and long-term follow up of cardiovascular high-risk patients

INTRODUCTION

The Berlin Long-term Observation of Vascular Events is a prospective cohort study that aims to improve prediction and disease-overarching mechanistic understanding of cardiovascular (CV) disease progression by comprehensively investigating a high-risk patient population with different organ manifestations.

METHODS AND ANALYSIS

A total of 8000 adult patients will be recruited who have either suffered an acute CV event (CVE) requiring hospitalisation or who have not experienced a recent acute CVE but are at high CV risk. An initial study examination is performed during the acute treatment phase of the index CVE or after inclusion into the chronic high risk arm. Deep phenotyping is then performed after ~90 days and includes assessments of the patient's medical history, health status and behaviour, cardiovascular, nutritional, metabolic, and anthropometric parameters, and patient-related outcome measures. Biospecimens are collected for analyses including 'OMICs' technologies (e.g., genomics, metabolomics, proteomics). Subcohorts undergo MRI of the brain, heart, lung and kidney, as well as more comprehensive metabolic, neurological and CV examinations. All participants are followed up for up to 10 years to assess clinical outcomes, primarily major adverse CVEs and patient-reported (value-based) outcomes. State-of-the-art clinical research methods, as well as emerging techniques from systems medicine and artificial intelligence, will be used to identify associations between patient characteristics, longitudinal changes and outcomes.

ETHICS AND DISSEMINATION

The study was approved by the Charité-Universitätsmedizin Berlin ethics committee (EA1/066/17). The results of the study will be disseminated through international peer-reviewed publications and congress presentations.

STUDY REGISTRATION

First study phase: Approved WHO primary register: German Clinical Trials Register: https://drks.de/search/de/trial/DRKS00016852; WHO International Clinical Registry Platform: http://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00016852. Recruitment started on July 18, 2017.Second study phase: Approved WHO primary register: German Clinical Trials Register DRKS00023323, date of registration: November 4, 2020, URL: http://www.drks.de/ DRKS00023323. Recruitment started on January 1, 2021.

Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study

In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.

Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.

Sarcopenia - Definition, Radiological Diagnosis, Clinical Significance

BACKGROUND

 Sarcopenia is an age-related syndrome characterized by a loss of muscle mass and strength. As a result, the independence of the elderly is reduced and the hospitalization rate and mortality increase. The onset of sarcopenia often begins in middle age due to an unbalanced diet or malnutrition in association with a lack of physical activity. This effect is intensified by concomitant diseases such as obesity or metabolic diseases including diabetes mellitus.

METHOD

 With effective preventative diagnostic procedures and specific therapeutic treatment of sarcopenia, the negative effects on the individual can be reduced and the negative impact on health as well as socioeconomic effects can be prevented. Various diagnostic options are available for this purpose. In addition to basic clinical methods such as measuring muscle strength, sarcopenia can also be detected using imaging techniques like dual X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), and sonography. DXA, as a simple and cost-effective method, offers a low-dose option for assessing body composition. With cross-sectional imaging techniques such as CT and MRI, further diagnostic possibilities are available, including MR spectroscopy (MRS) for noninvasive molecular analysis of muscle tissue. CT can also be used in the context of examinations performed for other indications to acquire additional parameters of the skeletal muscles (opportunistic secondary use of CT data), such as abdominal muscle mass (total abdominal muscle area - TAMA) or the psoas as well as the pectoralis muscle index. The importance of sarcopenia is already well studied for patients with various tumor entities and also infections such as SARS-COV2.

RESULTS AND CONCLUSION

 Sarcopenia will become increasingly important, not least due to demographic changes in the population. In this review, the possibilities for the diagnosis of sarcopenia, the clinical significance, and therapeutic options are described. In particular, CT examinations, which are repeatedly performed on tumor patients, can be used for diagnostics. This opportunistic use can be supported by the use of artificial intelligence.

KEY POINTS

· Sarcopenia is an age-related syndrome with loss of muscle mass and strength.. · Early detection and therapy can prevent negative effects of sarcopenia.. · In addition to DEXA, cross-sectional imaging techniques (CT, MRI) are available for diagnostic purposes.. · The use of artificial intelligence (AI) offers further possibilities in sarcopenia diagnostics..

CITATION FORMAT

· Vogele D, Otto S, Sollmann N et al. Sarcopenia - Definition, Radiological Diagnosis, Clinical Significance. Fortschr Röntgenstr 2023; DOI: 10.1055/a-1990-0201.

Population-based cohort imaging: skeletal muscle mass by magnetic resonance imaging in correlation to bioelectrical-impedance analysis

BACKGROUND

Skeletal muscle mass is subjected to constant changes and is considered a good predictor for outcome in various diseases. Bioelectrical-impedance analysis (BIA) and magnetic resonance imaging (MRI) are approved methodologies for its assessment. However, muscle mass estimations by BIA may be influenced by excess intramuscular lipids and adipose tissue in obesity. The objective of this study was to evaluate the feasibility of quantitative assessment of skeletal muscle mass by MRI as compared with BIA.

METHODS

Subjects from a population-based cohort underwent BIA (50 kHz, 0.8 mA) and whole-body MRI including chemical-shift encoded MRI (six echo times). Abdominal muscle mass by MRI was quantified as total and fat-free cross-sectional area by a standardized manual segmentation-algorithm and normalized to subjects' body height² (abdominal muscle mass indices: AMMI_MRI ).

RESULTS

Among 335 included subjects (56.3 ± 9.1 years, 56.1% male), 95 (28.4%) were obese (BMI ≥ 30 kg/m² ). MRI-based and BIA-based measures of muscle mass were strongly correlated, particularly in non-obese subjects [r < 0.74 in non-obese (P < 0.001) vs. r < 0.56 in obese (P < 0.001)]. Median AMMI_Total(MRI) was significantly higher in obese as compared with non-obese subjects (3246.7 ± 606.1 mm² /m² vs. 2839.0 ± 535.8 mm² /m² , P < 0.001, respectively), whereas the ratio AMMI_Fat-free /AMMI_Total (by MRI) was significantly higher in non-obese individuals (59.3 ± 10.1% vs. 53.5 ± 10.6%, P < 0.001, respectively). No significant difference was found regarding AMMI_{Fat-free(MRI)} (P = 0.424). In analyses adjusted for age and sex, impaired glucose tolerance and measures of obesity were significantly and positively associated with AMMI_Total(MRI) and significantly and inversely with the ratio AMMI_{Fat-free(MRI)} /AMMI_Total(MRI) (P < 0.001).

CONCLUSIONS

MRI-based assessment of muscle mass is feasible in population-based imaging and strongly correlated with BIA. However, the observed weaker correlation in obese subjects may explain the known limitation of BIA in obesity and promote MRI-based assessments. Thus, skeletal muscle mass parameters by MRI may serve as practical imaging biomarkers independent of subjects' body weight.

Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study: Predictive Capability of Automated Image Quality Assessment for Protocol Repetitions

BACKGROUND

Reproducible image quality is of high relevance for large cohort studies and can be challenging for magnetic resonance imaging (MRI). Automated image quality assessment may contribute to conducting radiologic studies effectively.

PURPOSE

The aims of this study were to assess protocol repetition frequency in population-based whole-body MRI along with its effect on examination time and to examine the applicability of automated image quality assessment for predicting decision-making regarding repeated acquisitions.

MATERIALS AND METHODS

All participants enrolled in the prospective, multicenter German National Cohort (NAKO) study who underwent whole-body MRI at 1 of 5 sites from 2014 to 2016 were included in this analysis (n = 11,347). A standardized examination program of 12 protocols was used. Acquisitions were carried out by certified radiologic technologists, who were authorized to repeat protocols based on their visual perception of image quality. Eleven image quality parameters were derived fully automatically from the acquired images, and their discrimination ability regarding baseline acquisitions and repetitions was tested.

RESULTS

At least 1 protocol was repeated in 12% (n = 1359) of participants, and more than 1 protocol in 1.6% (n = 181). The repetition frequency differed across protocols (P < 0.001), imaging sites (P < 0.001), and over the study period (P < 0.001). The mean total scan time was 62.6 minutes in participants without and 67.4 minutes in participants with protocol repetitions (mean difference, 4.8 minutes; 95% confidence interval, 4.5-5.2 minutes). Ten of the automatically derived image quality parameters were individually retrospectively predictive for the repetition of particular protocols; for instance, "signal-to-noise ratio" alone provided an area under the curve of 0.65 (P < 0.001) for repetition of the Cardio Cine SSFP SAX protocol. Combinations generally improved prediction ability, as exemplified by "image sharpness" plus "foreground ratio" yielding an area under the curve of 0.89 (P < 0.001) for repetition of the Neuro T1w 3D MPRAGE protocol, versus 0.85 (P < 0.001) and 0.68 (P < 0.001) as individual parameters.

CONCLUSIONS

Magnetic resonance imaging protocol repetitions were necessary in approximately 12% of scans even in the highly standardized setting of a large cohort study. Automated image quality assessment shows predictive value for the technologists' decision to perform protocol repetitions and has the potential to improve imaging efficiency.

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.

SHIP-MR and Radiology: 12 Years of Whole-Body Magnetic Resonance Imaging in a Single Center

The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.

Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Subclinical cardiac impairment relates to traditional pulmonary function test parameters and lung volume as derived from whole-body MRI in a population-based cohort study

To evaluate the relationship of cardiac function, including time-volume-curves, with lung volumes derived from pulmonary function tests (PFT) and MRI in subjects without cardiovascular diseases. 216 subjects underwent whole-body MRI and spirometry as part of the KORA-FF4 cohort study. Lung volumes derived semi-automatically using an in-house algorithm. Forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and residual volume were measured. Cardiac parameters derived from Cine-SSFP-sequence using cvi42, while left ventricle (LV) time-volume-curves were evaluated using pyHeart. Linear regression analyses assessed the relationships of cardiac parameters with PFT and MRI-based lung volumes. Mean age was 56.3 ± 9.2 years (57% males). LV and right ventricular (RV) end-diastolic-, end-systolic-, stroke volume, LV peak ejection- and early/late diastolic filling rate were associated with FEV1, FVC, and residual volume (excluding late diastolic filling rate with FEV1, LV end-systolic/stroke volume and RV end-diastolic/end-systolic volumes with residual volume). In contrast, LV end-diastolic volume (ß = - 0.14, p = 0.01), early diastolic filling rate (ß = - 0.11, p = 0.04), and LV/RV stroke volume (ß = - 0.14, p = 0.01; ß = - 0.11, p = 0.01) were inversely associated with MRI-based lung volume. Subclinical cardiac impairment was associated with reduced FEV1, FVC, and residual volume. Cardiac parameters decreased with increasing MRI-based lung volume contrasting the results of PFT.

Distribution patterns of intramyocellular and extramyocellular fat by magnetic resonance imaging in subjects with diabetes, prediabetes and normoglycaemic controls

AIM

To evaluate the distribution of intramyocellular lipids (IMCLs) and extramyocellular lipids (EMCLs) as well as total fat content in abdominal skeletal muscle by magnetic resonance imaging (MRI) using a dedicated segmentation algorithm in subjects with type 2 diabetes (T2D), prediabetes and normoglycaemic controls.

MATERIALS AND METHODS

Subjects from a population-based cohort were classified with T2D, prediabetes or as normoglycaemic controls. Total myosteatosis, IMCLs and EMCLs were quantified by multiecho Dixon MRI as proton-density fat-fraction (in %) in abdominal skeletal muscle.

RESULTS

Among 337 included subjects (median age 56.0 [IQR: 49.0-64.0] years, 56.4% males, median body mass index [BMI]: 27.2 kg/m² ), 129 (38.3%) were classified with an impaired glucose metabolism (T2D: 49 [14.5%]; prediabetes: 80 [23.7%]). IMCLs were significantly higher than EMCLs in subjects without obesity (5.7% [IQR: 4.8%-7.0%] vs. 4.1% [IQR: 2.7%-5.8%], P < .001), whereas the amounts of IMCLs and EMCLs were shown to be equal and significantly higher in subjects with obesity (both 6.7%, P < .001). Subjects with prediabetes and T2D had significantly higher amounts of IMCLs and EMCLs compared with normoglycaemic controls (P < .001). In univariable analysis, prediabetes and T2D were significantly associated with both IMCLs (prediabetes: β: 0.76, 95% CI: 0.28-1.24, P = .002; T2D: β: 1.56, 95% CI: 0.66-2.47, P < .001) and EMCLs (prediabetes: β: 1.54, 95% CI: 0.56-2.51, P = .002; T2D: β: 2.15, 95% CI: 1.33-2.96, P < .001). After adjustment for age and gender, the association of IMCLs with prediabetes attenuated (P = 0.06), whereas for T2D, both IMCLs and EMCLs remained significantly and positively associated (P < .02).

CONCLUSION

There are significant differences in the amount and distribution ratio of IMCLs and EMCLs between subjects with T2D, prediabetes and normoglycaemic controls. Therefore, these patterns of intramuscular fat distribution by MRI might serve as imaging biomarkers in both normal and impaired glucose metabolism.

Extravascular findings on run-off MR angiography: frequency, location and clinical significance

OBJECTIVES

The aim of this study was to analyze the prevalence, location and clinical relevance of extravascular findings (EVFs) on magnetic resonance angiography (MRA) of the run-off vasculature.

METHODS

In this retrospective study, we analyzed run-off MRAs of 194 consecutive patients (45 women and 149 men, median age 68 years, IQR 58-74 years). Our patient cohort consisted predominantly of individuals with known (n = 165, 85%) or suspected (n = 15, 8%) peripheral artery disease (PAD). All MRA examinations were performed between 2012 and 2018 on a 3 Tesla MRI scanner using a standardized protocol. Two radiologists re-evaluated the MRA images to identify EVFs, which were classified into findings with major (category I), moderate (category II) and minor (category III) clinical significance.

RESULTS

A total of 501 EVFs were found in 172 of the 194 patients (89%). Twenty-seven findings (5%) were assigned to category I, 189 (38%) to category II and 285 (57%) to category III. 23 of 194 patients (12%) had at least one EVF with major clinical relevance (category I). Most of the 27 category I EVFs were observed in the soft tissues (n = 13, 48%). The remaining category I EVFs were found in the musculoskeletal (n = 7, 26%), urogenital (n = 4, 15%), lymphatic (n = 2, 7%) and gastrointestinal (n = 1, 4%) system. The majority of the category I EVFs were infectious (n = 14, 52%) or neoplastic (n = 10, 37%) pathologies.

CONCLUSIONS

Clinically relevant EVF can be encountered frequently on run-off MRA examinations. These results illustrate the importance of evaluating all organ systems when reporting MRA examinations, despite the clinical focus being the patients' vascular status.

Vertebral Bone Marrow Fat Is independently Associated to VAT but Not to SAT: KORA FF4-Whole-Body MR Imaging in a Population-Based Cohort

The objective of the current study was to assess the relationship of bone marrow adipose tissue (BMAT) content to abdominal fat depots, including visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), as well as cardiovascular risk factors (CVRF) beyond physical activity in a population-based cohort study undergoing whole-body magnetic resonance (MR) imaging. Subjects of the Cooperative Health Research in the Augsburg Region (KORA) FF4 study without known cardiovascular disease underwent fat fraction quantification in vertebrae (BMAT_L1/L2) via a 2-point T1-weighted volumetric interpolated breath-hold examination (VIBE) Dixon sequence. The same MR sequence was applied to quantify VAT and SAT volume. Subjects’ characteristics, including physical activity, were determined through standardized exams and self-assessment questionnaires. Univariate and multivariate linear regression were applied. In the cohort of 378 subjects (56 ± 9.1years; 42.1% female), BMAT_L1/L2 was 54.3 ± 10.1%, VAT was 4.54 ± 2.71 L, and SAT was 8.10 ± 3.68 L. VAT differed significantly across BMAT_L1/L2 tertiles (3.60 ± 2.76 vs. 4.92 ± 2.66 vs. 5.11 ± 2.48; p < 0.001), there was no significant differences for SAT (p = 0.39). In the fully adjusted model, VAT remained positively associated with BMAT_L1/L2 (β = 0.53, p = 0.03). Furthermore, BMAT_L1/L2 was associated with age (β = 5.40 per 10-years, p < 0.001), hemoglobin A1c (HbA1c; β = 1.55 per 1%, p = 0.04), lipids (β = 0.20 per 10 mg/dL triglycerides; β = 0.40 per 10 mg/dL low-density lipoprotein (LDL); β =−3.21 lipid-lowering medication; all p < 0.05), and less physical activity (β = 3.7 “no or nearly no exercise” as compared to “≥2 h per week, regularly”, p = 0.003); gender was not significantly different (p = 0.57). In the population-based cohort, VAT but not SAT were associated with higher BMAT_L1/L2 independently of physical activity and other cardiovascular risk factors. Further, BMAT_L1/L2 increased with older age, less physical activity, higher HbA1c, and increased lipids but decreased with lipid-lowering medication.

The Duty to Look for Incidental Findings in Imaging Research

Imaging research regularly yields incidental findings that may have personal medical or reproductive decision-making significance to study participants. It is widely assumed that researchers have a moral obligation to disclose at least some kinds of incidental findings to research participants. However, it is also a widely held view that researchers do not have a moral obligation to actively look for abnormalities irrelevant to the aims of their study. This paper challenges that assumption.

Introduction to Radiomics

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

Die Basiserhebung der NAKO Gesundheitsstudie: Teilnahme an den Untersuchungsmodulen, Qualitätssicherung und Nutzung von Sekundärdaten

Hintergrund

Die NAKO Gesundheitsstudie ist ein bundesweites interdisziplinäres Forschungsvorhaben mit dem Ziel, die Ursachen für chronische Krankheiten und deren vorklinische Stadien zu untersuchen. Der Artikel gibt einen Überblick über das Studiendesign, die Methoden, die Teilnahme an den Untersuchungen und ihre Qualitätssicherung zur Halbzeit der Basiserhebung.

Methoden

Für die Basiserhebung wurden mehr als 200.000 Frauen und Männer im Alter von 20–69 Jahren aus Zufallsstichproben der Allgemeinbevölkerung in 18 Studienzentren rekrutiert (2014–2019). Die Basiserhebung beinhaltet Untersuchungen, Befragungen und Biomaterialien für alle Teilnehmerinnen und Teilnehmer (Level 1), ein erweitertes Programm für mindestens 20 % (Level 2) und eine Magnetresonanztomografie (MRT) für 30.000 Teilnehmerinnen und Teilnehmer. Sekundär- und Registerdaten werden über Krankheitsregister, Kranken- und Rentenversicherungen erhoben. Die Auswertung bezieht die Datenbasis zur Halbzeit der Basiserhebung mit 101.839 Teilnehmerinnen und Teilnehmern ein, davon 11.371 mit einer MRT-Untersuchung.

Ergebnisse

Die mittlere Responsequote zur Halbzeit betrug insgesamt 18 %. Die Teilnahme an den Untersuchungen lag überwiegend bei mehr als 95 %. Bei 96 % der MRT-Teilnehmerinnen und Teilnehmer konnten alle 12 MRT-Sequenzen vollständig durchgeführt werden. Der Erschließung und wissenschaftlichen Nutzung ergänzender Sekundär- und Registerdaten stimmten mehr als 90 % der Teilnehmerinnen und Teilnehmer zu.

Diskussion

Die Bereitschaft, möglichst alle Untersuchungsmodule durchzuführen, war trotz des zeitlichen Aufwandes außerordentlich hoch. Dadurch wird die NAKO zu einer zentralen Ressource für die epidemiologische Forschung in Deutschland. Sie wird es ermöglichen, neue Strategien zur Früherkennung, Vorhersage und Primärprävention chronischer Krankheiten zu entwickeln.

Association of longitudinal risk profile trajectory clusters with adipose tissue depots measured by magnetic resonance imaging

The objective of the study was to identify associations of longitudinal trajectories of traditional cardiometabolic risk factors with abdominal and ectopic adipose tissue depots measured by magnetic resonance imaging (MRI). We measured total abdominal, visceral, and subcutaneous adipose tissue in liter and intrahepatic, intrapancreatic and renal sinus fat as fat fractions by MRI in 325 individuals free of cardiovascular disease at Exam 3 of a population-based cohort. We related these MRI measurements at Exam 3 to longitudinal risk profile trajectory clusters, based on risk factor measurements from Exam 3, Exam 2 (seven years prior to MRI) and Exam 1 (14 years prior to MRI). Based on the levels and longitudinal trajectories of several risk factors (blood pressure, lipid profile, anthropometric measurements, HbA1c), we identified three different trajectory clusters. These clusters displayed a graded association with all adipose tissue traits after adjustment for potential confounders (e.g. visceral adipose tissue: β_ClusterII = 1.30 l, 95%-CI:[0.84 l;1.75 l], β_ClusterIII = 3.32 l[2.74 l;3.90 l]; intrahepatic: Estimate_ClusterII = 1.54[1.27,1.86], Estimate_ClusterIII = 2.48[1.93,3.16]. Associations remained statistically significant after additional adjustment for the risk factor levels at Exam 1 or Exam 3, respectively. Trajectory clusters provided additional information in explaining variation in the different fat compartments beyond risk factor profiles obtained at individual exams. In conclusion, sustained high risk factor levels and unfavorable trajectories are associated with high levels of adipose tissue; however, the association with cardiometabolic risk factors varies substantially between different ectopic adipose tissues. Trajectory clusters, covering longitudinal risk profiles, provide additional information beyond single-point risk profiles. This emphasizes the need to incorporate longitudinal information in cardiometabolic risk estimation.

Innovative approaches for cancer treatment: current perspectives and new challenges

Every year, cancer is responsible for millions of deaths worldwide and, even though much progress has been achieved in medicine, there are still many issues that must be addressed in order to improve cancer therapy. For this reason, oncological research is putting a lot of effort towards finding new and efficient therapies which can alleviate critical side effects caused by conventional treatments. Different technologies are currently under evaluation in clinical trials or have been already introduced into clinical practice. While nanomedicine is contributing to the development of biocompatible materials both for diagnostic and therapeutic purposes, bioengineering of extracellular vesicles and cells derived from patients has allowed designing ad hoc systems and univocal targeting strategies. In this review, we will provide an in-depth analysis of the most innovative advances in basic and applied cancer research.

Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands

Purpose

To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy.

Methods

A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017–2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC).

Results

Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5).

Conclusion

Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1339-4) contains supplementary material, which is available to authorized users.

How to report incidental findings from population whole-body MRI: view of participants of the German National Cohort

OBJECTIVES

In the German National Cohort (GNC), 30,000 individuals are examined with whole-body MRI (wbMRI), of which about 3000 participants are expected to receive an incidental finding (IF) disclosure. In order to get feedback from participants and to evaluate the IF-management procedure of the wbMRI substudy, a follow-up questionnaire was developed. This single-center pilot trial was aimed to get a first impression on feasibility reproducibility and validity of such a survey in order to take necessary adjustments before initiating the survey among several thousand participants.

METHODS

The questionnaires were sent out in test-retest manner to 86 participants who received a wbMRI examination in January-February 2016 at the imaging center in Neubrandenburg. The ratio of participants with and without IF notification was 1:1. Descriptive statistics was performed.

RESULTS

A first response of 94% and completion proportion of 99% were achieved. Participants were satisfied with the examination procedure. Ninety-five percent of participants considered it very important to receive notification of IFs. Participants reported minimal stress levels while waiting for a possible IF notification letter, but high stress levels when an IF letter was received. Phrasing of the IF reports was rated in 97% as well understandable and in 55% as beneficial to health status.

CONCLUSIONS

This questionnaire will serve researchers within the GNC as a fundamental instrument not only for quality management analyses but also for the investigation of still unacknowledged scientific and ethical questions contributing to evidence-based guidelines concerning the complex approach to IFs in future population-based imaging.

KEY POINTS

• Evidence-based guidelines for reporting incidental findings in population whole-body MRI are lacking. • Pilot-testing of a questionnaire for the evaluation of practical and ethical aspects of the procedure to report incidental findings in the German National Cohort shows a high level of acceptance and high return rate by participants. • Participants reported minimal stress levels while waiting for a possible incidental finding notification letter, which increased significantly, when such a letter was received.

Body Composition Profiling in the UK Biobank Imaging Study

OBJECTIVE

This study aimed to investigate the value of imaging-based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels.

METHODS

The first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modeling.

RESULTS

Matched control analysis showed that higher VAT and MFI were associated with CHD and T2D (P < 0.001). Higher liver fat was associated with T2D (P < 0.001) and lower liver fat with CHD (P < 0.05), matching on VAT. Multivariable modeling showed that lower VAT and MFI were associated with metabolic health (P < 0.001), and liver fat was nonsignificant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity.

CONCLUSIONS

Body composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.

MRI adipose tissue and muscle composition analysis-a review of automation techniques

MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease

OBJECTIVES

Whole-body MR imaging is increasingly utilised; although for lung dedicated sequences are often not included, the chest is typically imaged. Our objective was to determine the clinical utility of lung volumes derived from non-dedicated MRI sequences in the population-based KORA-FF4 cohort study.

METHODS

400 subjects (56.4 ± 9.2 years, 57.6% males) underwent whole-body MRI including a coronal T1-DIXON-VIBE sequence in inspiration breath-hold, originally acquired for fat quantification. Based on MRI, lung volumes were derived using an automated framework and related to common predictors, pulmonary function tests (PFT; spirometry and pulmonary gas exchange, n = 214) and obstructive lung disease.

RESULTS

MRI-based lung volume was 4.0 ± 1.1 L, which was 64.8 ± 14.9% of predicted total lung capacity (TLC) and 124.4 ± 27.9% of functional residual capacity. In multivariate analysis, it was positively associated with age, male, current smoking and height. Among PFT indices, MRI-based lung volume correlated best with TLC, alveolar volume and residual volume (RV; r = 0.57 each), while it was negatively correlated to FEV₁/FVC (r = 0.36) and transfer factor for carbon monoxide (r = 0.16). Combining the strongest PFT parameters, RV and FEV₁/FVC remained independently and incrementally associated with MRI-based lung volume (β = 0.50, p = 0.04 and β = - 0.02, p = 0.02, respectively) explaining 32% of the variability. For the identification of subjects with obstructive lung disease, height-indexed MRI-based lung volume yielded an AUC of 0.673-0.654.

CONCLUSION

Lung volume derived from non-dedicated whole-body MRI is independently associated with RV and FEV₁/FVC. Furthermore, its moderate accuracy for obstructive lung disease indicates that it may be a promising tool to assess pulmonary health in whole-body imaging when PFT is not available.

KEY POINTS

• Although whole-body MRI often does not include dedicated lung sequences, lung volume can be automatically derived using dedicated segmentation algorithms • Lung volume derived from whole-body MRI correlates with typical predictors and risk factors of respiratory function including smoking and represents about 65% of total lung capacity and 125% of the functional residual capacity • Lung volume derived from whole-body MRI is independently associated with residual volume and the ratio of forced expiratory volume in 1 s to forced vital capacity and may allow detection of obstructive lung disease.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Promoting Collaborations Between Radiologists and Scientists

Radiology as a discipline thrives on the dynamic interplay between technological and clinical advances. Progress in almost all facets of the imaging sciences is highly dependent on complex tools sourced from physics, engineering, biology, and the clinical sciences to obtain, process, and view imaging studies. The application of these tools, however, requires broad and deep medical knowledge about disease pathophysiology and its relationship with medical imaging. This relationship between clinical medicine and imaging technology, nurtured and fostered over the past 75 years, has cultivated extraordinarily rich collaborative opportunities between basic scientists, engineers, and physicians. In this review, we attempt to provide a framework to identify both currently successful collaborative ventures and future opportunities for scientific partnership. This invited review is a product of a special working group within the Association of University Radiologists-Radiology Research Alliance.

Paediatric population neuroimaging and the Generation R Study: the second wave

Paediatric population neuroimaging is an emerging field that falls at the intersection between developmental neuroscience and epidemiology. A key feature of population neuroimaging studies involves large-scale recruitment that is representative of the general population. One successful approach for population neuroimaging is to embed neuroimaging studies within large epidemiological cohorts. The Generation R Study is a large, prospective population-based birth-cohort in which nearly 10,000 pregnant mothers were recruited between 2002 and 2006 with repeated measurements in the children and their parents over time. Magnetic resonance imaging was included in 2009 with the scanning of 1070 6-to-9-year-old children. The second neuroimaging wave was initiated in April 2013 with a total of 4245 visiting the MRI suite and 4087 9-to-11-year-old children being scanned. The sequences included high-resolution structural MRI, 35-direction diffusion weighted imaging, and a 6 min and 2 s resting-state functional MRI scan. The goal of this paper is to provide an overview of the imaging protocol and the overlap between the neuroimaging data and metadata. We conclude by providing a brief overview of results from our first wave of neuroimaging, which highlights a diverse array of questions that can be addressed by merging the fields of developmental neuroscience and epidemiology.

Subclinical Disease Burden as Assessed by Whole-Body MRI in Subjects With Prediabetes, Subjects With Diabetes, and Normal Control Subjects From the General Population: The KORA-MRI Study

Detailed pathophysiological manifestations of early disease in the context of prediabetes are poorly understood. This study aimed to evaluate the extent of early signs of metabolic and cardio-cerebrovascular complications affecting multiple organs in individuals with prediabetes. Subjects without a history of stroke, coronary artery disease, or peripheral artery disease were enrolled in a case-control study nested within the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and underwent comprehensive MRI assessment to characterize cerebral parameters (white matter lesions, microbleeds), cardiovascular parameters (carotid plaque, left ventricular function, and myocardial late gadolinium enhancement [LGE]), and metabolic parameters (hepatic proton-density fat fraction [PDFF] and subcutaneous and visceral abdominal fat). Among 400 subjects who underwent MRI, 103 subjects had prediabetes and 54 had established diabetes. Subjects with prediabetes had an increased risk for carotid plaque and adverse functional cardiac parameters, including reduced early diastolic filling rates as well as a higher prevalence of LGE compared with healthy control subjects. In addition, people with prediabetes had significantly elevated levels of PDFF and total and visceral fat. Thus, subjects with prediabetes show early signs of subclinical disease that include vascular, cardiac, and metabolic changes, as measured by whole-body MRI after adjusting for cardiometabolic risk factors.

Population-based imaging biobanks as source of big data

Advances of computational sciences over the last decades have enabled the introduction of novel methodological approaches in biomedical research. Acquiring extensive and comprehensive data about a research subject and subsequently extracting significant information has opened new possibilities in gaining insight into biological and medical processes. This so-called big data approach has recently found entrance into medical imaging and numerous epidemiological studies have been implementing advanced imaging to identify imaging biomarkers that provide information about physiological processes, including normal development and aging but also on the development of pathological disease states. The purpose of this article is to present existing epidemiological imaging studies and to discuss opportunities, methodological and organizational aspects, and challenges that population imaging poses to the field of big data research.