Artificial intelligence and machine learning in axial spondyloarthritis

PURPOSE OF REVIEW

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.

RECENT FINDINGS

Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.

SUMMARY

Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions

This study compares 2 large language models and their performance vs that of competing open-source models.

Comparison of diffusion-weighted MRI and [18F]FDG PET/MRI for treatment monitoring in pediatric Hodgkin and non-Hodgkin lymphoma

OBJECTIVE

To compare tumor therapy response assessments with whole-body diffusion-weighted imaging (WB-DWI) and 18F-fluorodeoxyglucose ([18F]FDG) PET/MRI in pediatric patients with Hodgkin lymphoma and non-Hodgkin lymphoma.

MATERIALS AND METHODS

In a retrospective, non-randomized single-center study, we reviewed serial simultaneous WB-DWI and [18F]FDG PET/MRI scans of 45 children and young adults (27 males; mean age, 13 years ± 5 [standard deviation]; age range, 1-21 years) with Hodgkin lymphoma (n = 20) and non-Hodgkin lymphoma (n = 25) between February 2018 and October 2022. We measured minimum tumor apparent diffusion coefficient (ADCmin) and maximum standardized uptake value (SUVmax) of up to six target lesions and assessed therapy response according to Lugano criteria and modified criteria for WB-DWI. We evaluated the agreement between WB-DWI- and [18F]FDG PET/MRI-based response classifications with Gwet's agreement coefficient (AC).

RESULTS

After induction chemotherapy, 95% (19 of 20) of patients with Hodgkin lymphoma and 72% (18 of 25) of patients with non-Hodgkin lymphoma showed concordant response in tumor metabolism and proton diffusion. We found a high agreement between treatment response assessments on WB-DWI and [18F]FDG PET/MRI (Gwet's AC = 0.94; 95% confidence interval [CI]: 0.82, 1.00) in patients with Hodgkin lymphoma, and a lower agreement for patients with non-Hodgkin lymphoma (Gwet's AC = 0.66; 95% CI: 0.43, 0.90). After completion of therapy, there was an excellent agreement between WB-DWI and [18F]FDG PET/MRI response assessments (Gwet's AC = 0.97; 95% CI: 0.91, 1).

CONCLUSION

Therapy response of Hodgkin lymphoma can be evaluated with either [18F]FDG PET or WB-DWI, whereas patients with non-Hodgkin lymphoma may benefit from a combined approach.

CLINICAL RELEVANCE STATEMENT

Hodgkin lymphoma and non-Hodgkin lymphoma exhibit different patterns of tumor response to induction chemotherapy on diffusion-weighted MRI and PET/MRI.

KEY POINTS

• Diffusion-weighted imaging has been proposed as an alternative imaging to assess tumor response without ionizing radiation. • After induction therapy, whole-body diffusion-weighted imaging and PET/MRI revealed a higher agreement in patients with Hodgkin lymphoma than in those with non-Hodgkin lymphoma. • At the end of therapy, whole-body diffusion-weighted imaging and PET/MRI revealed an excellent agreement for overall tumor therapy responses for all lymphoma types.

Tyrosine kinase inhibitor therapy in pediatric sarcoma: Prognostic implications of pulmonary metastatic cavitation

PURPOSES

This study aims to ascertain the prevalence of cavitations in pulmonary metastases among pediatric and young adult patients with sarcoma undergoing tyrosine kinase inhibitor (TKI) therapy, and assess whether cavitation can predict clinical response and survival outcomes.

METHODS

In a single-center retrospective analysis, we examined chest computed tomography (CT) scans of 17 patients (median age 16 years; age range: 4-25 years) with histopathologically confirmed bone (n = 10) or soft tissue (n = 7) sarcoma who underwent TKI treatment for lung metastases. The interval between TKI initiation and the onset of lung nodule cavitation and tumor regrowth were assessed. The combination of all imaging studies and clinical data served as the reference standard for clinical responses. Progression-free survival (PFS) was compared between patients with cavitating and solid nodules using Kaplan-Meier survival analysis and log-rank test.

RESULTS

Five out of 17 patients (29%) exhibited cavitation of pulmonary nodules during TKI therapy. The median time from TKI initiation to the first observed cavitation was 79 days (range: 46-261 days). At the time of cavitation, all patients demonstrated stable disease. When the cavities began to fill with solid tumor, 60% (3/5) of patients exhibited progression in other pulmonary nodules. The median PFS for patients with cavitated pulmonary nodules after TKI treatment (6.7 months) was significantly longer compared to patients without cavitated nodules (3.8 months; log-rank p-value = .03).

CONCLUSIONS

Cavitation of metastatic pulmonary nodules in sarcoma patients undergoing TKI treatment is indicative of non-progressive disease, and significantly correlates with PFS.

Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals

Background

The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community.

Purpose

To examine the gender and country distribution among academic editors in leading computer science and AI journals.

Material and Methods

This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability.

Result

Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329; p = .004). The U.S., the U.K., and China comprised 50% of editors, while Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia had the highest women editor representation per million women population.

Conclusion

Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.

Sex-specific diagnostic efficacy of MRI in axial spondyloarthritis: challenging the 'One Size Fits All' notion

OBJECTIVES

Sex-specific differences in the presentation of axial spondyloarthritis (axSpA) may contribute to a diagnostic delay in women. The aim of this study was to investigate the diagnostic performance of MRI findings comparing men and women.

METHODS

Patients with back pain from six different prospective cohorts (n=1194) were screened for inclusion in this post hoc analysis. Two blinded readers scored the MRI data sets independently for the presence of ankylosis, erosion, sclerosis, fat metaplasia and bone marrow oedema. Χ2 tests were performed to compare lesion frequencies. Contingency tables were used to calculate markers for diagnostic performance, with clinical diagnosis as the standard of reference. The positive and negative likelihood ratios (LR+/LR-) were used to calculate the diagnostic OR (DOR) to assess the diagnostic performance.

RESULTS

After application of exclusion criteria, 526 patients (379 axSpA (136 women and 243 men) and 147 controls with chronic low back pain) were included. No major sex-specific differences in the diagnostic performance were shown for bone marrow oedema (DOR m: 3.0; f: 3.9). Fat metaplasia showed a better diagnostic performance in men (DOR 37.9) than in women (DOR 5.0). Lower specificity was seen in women for erosions (77% vs 87%), sclerosis (44% vs 66%), fat metaplasia (87% vs 96%).

CONCLUSION

The diagnostic performance of structural MRI markers is substantially lower in female patients with axSpA; active inflammatory lesions show comparable performance in both sexes, while still overall inferior to structural markers. This leads to a comparably higher risk of false positive findings in women.

Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education

The increasing use of artificial intelligence (AI) in medicine is associated with new ethical challenges and responsibilities. However, special considerations and concerns should be addressed when integrating AI applications into medical education, where healthcare, AI, and education ethics collide. This commentary explores the biomedical ethical responsibilities of medical institutions in incorporating AI applications into medical education by identifying potential concerns and limitations, with the goal of implementing applicable recommendations. The recommendations presented are intended to assist in developing institutional guidelines for the ethical use of AI for medical educators and students.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs

BACKGROUND AND OBJECTIVES

Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs.

METHODS

To improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité - University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20).

RESULTS

The final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm.

CONCLUSIONS

Using an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

T2* relaxometry is one of the established methods to measure the effect of superparamagnetic iron oxide nanoparticles on tumor tissues with magnetic resonance imaging (MRI). Iron oxide nanoparticles shorten the T1, T2, and T2* relaxation times of tumors. While the T1 effect is variable based on the size and composition of the nanoparticles, the T2 and T2* effects are usually predominant, and T2* measurements are the most time-efficient in a clinical context. Here, we present our approach to measuring tumor T2* relaxation times, using multi-echo gradient echo sequences, external software, and a standardized protocol for creating a T2* map with scanner-independent software. This facilitates the comparison of imaging data from different clinical scanners, different vendors, and co-clinical research work (i.e., tumor T2* data obtained in mouse models and patients). Once the software is installed, the T2 Fit Map plugin needs to be installed from the plugin manager. This protocol provides step-by-step procedural details, from importing the multi-echo gradient echo sequences into the software, to creating color-coded T2* maps and measuring tumor T2* relaxation times. The protocol can be applied to solid tumors in any body part and has been validated based on preclinical imaging data and clinical data in patients. This could facilitate tumor T2* measurements for multi-center clinical trials and improve the standardization and reproducibility of tumor T2* measurements in co-clinical and multi-center data analyses.

MegaPro, a clinically translatable nanoparticle for in vivo tracking of stem cell implants in pig cartilage defects

Rationale: Efficient labeling methods for mesenchymal stem cells (MSCs) are crucial for tracking and understanding their behavior in regenerative medicine applications, particularly in cartilage defects. MegaPro nanoparticles have emerged as a potential alternative to ferumoxytol nanoparticles for this purpose. Methods: In this study, we employed mechanoporation to develop an efficient labeling method for MSCs using MegaPro nanoparticles and compared their effectiveness with ferumoxytol nanoparticles in tracking MSCs and chondrogenic pellets. Pig MSCs were labeled with both nanoparticles using a custom-made microfluidic device, and their characteristics were analyzed using various imaging and spectroscopy techniques. The viability and differentiation capacity of labeled MSCs were also assessed. Labeled MSCs and chondrogenic pellets were implanted into pig knee joints and monitored using MRI and histological analysis. Results: MegaPro-labeled MSCs demonstrated shorter T2 relaxation times, higher iron content, and greater nanoparticle uptake compared to ferumoxytol-labeled MSCs, without significantly affecting their viability and differentiation capacity. Post-implantation, MegaPro-labeled MSCs and chondrogenic pellets displayed a strong hypointense signal on MRI with considerably shorter T2* relaxation times compared to adjacent cartilage. The hypointense signal of both MegaPro- and ferumoxytol-labeled chondrogenic pellets decreased over time. Histological evaluations showed regenerated defect areas and proteoglycan formation with no significant differences between the labeled groups. Conclusion: Our study demonstrates that mechanoporation with MegaPro nanoparticles enables efficient MSC labeling without affecting viability or differentiation. MegaPro-labeled cells show enhanced MRI tracking compared to ferumoxytol-labeled cells, emphasizing their potential in clinical stem cell therapies for cartilage defects.

Ferumoxytol-Enhanced MRI in Children and Young Adults: State of the Art

Ferumoxytol is an ultrasmall iron oxide nanoparticle that was originally approved by the FDA in 2009 for IV treatment of iron deficiency in adults with chronic kidney disease. Subsequently, its off-label use as an MRI contrast agent increased in clinical practice, particularly in pediatric patients in North America. Unlike conventional MRI contrast agents that are based on the rare earth metal gadolinium (gadolinium-based contrast agents), ferumoxytol is biodegradable and carries no potential risk of nephrogenic systemic fibrosis. At FDA-approved doses, ferumoxytol shows no long-term tissue retention in patients with intact iron metabolism. Ferumoxytol provides unique MRI properties, including long-lasting vascular retention (facilitating high-quality vascular imaging) and retention in reticuloendothelial system tissues, thereby supporting a variety of applications beyond those possible with gadolinium-based contrast agents (GBCAs). This Clinical Perspective describes clinical and early translational applications of ferumoxytol-enhanced MRI in children and young adults through off-label use in a variety of settings, including vascular, cardiac, and cancer imaging, drawing on the institutional experience of the authors. In addition, we describe current advances in pre-clinical and clinical research using ferumoxytol in cellular and molecular imaging as well as the use of ferumoxytol as a novel potential cancer therapeutic agent.

What Does DALL-E 2 Know About Radiology?

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection. Purpose To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA. Materials and Methods This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. P < .05 was considered indicative of statistically significant difference. Results Overall, 593 patients (mean age, 37 years ± 11 [SD]; 302 women) were studied. Inflammatory and structural changes were found in 197 of 477 patients (41%) and 244 of 477 (51%), respectively, in the training set and 25 of 116 patients (22%) and 26 of 116 (22%) in the test set. The AUCs were 0.94 (95% CI: 0.84, 0.97) for all inflammatory changes, 0.88 (95% CI: 0.80, 0.95) for inflammatory changes fulfilling the Assessment of SpondyloArthritis international Society definition, and 0.89 (95% CI: 0.81, 0.96) for structural changes indicative of axSpA. Sensitivity and specificity on the external test set were 22 of 25 patients (88%) and 65 of 91 patients (71%), respectively, for inflammatory changes and 22 of 26 patients (85%) and 70 of 90 patients (78%) for structural changes. Conclusion Deep neural networks can detect inflammatory or structural changes to the sacroiliac joint indicative of axial spondyloarthritis at MRI. © RSNA, 2022 Online supplemental material is available for this article.

Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection

BACKGROUND

The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability.

METHODS

Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps. Two U-ResNets trained for segmentation of anatomy (central gland, peripheral zone) and suspicious lesions for prostate cancer (PCa) with a PI-RADS score of ≥4 served as baseline algorithms. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). The Wilcoxon test with Bonferroni correction was used to evaluate differences in performance. The generalizability of the baseline model was assessed using the open datasets Medical Segmentation Decathlon and PROSTATEx.

RESULTS

Compared to Reader 1, the models achieved a DSC/HD/ASD of 0.88/18.3/2.2 for the central gland, 0.75/22.8/1.9 for the peripheral zone, and 0.45/36.7/17.4 for PCa. Compared with Reader 2, the DSC/HD/ASD were 0.88/17.5/2.6 for the central gland, 0.73/33.2/1.9 for the peripheral zone, and 0.4/39.5/19.1 for PCa. Interrater agreement measured in DSC/HD/ASD was 0.87/11.1/1.0 for the central gland, 0.75/15.8/0.74 for the peripheral zone, and 0.6/18.8/5.5 for PCa. Segmentation performances on the Medical Segmentation Decathlon and PROSTATEx were 0.82/22.5/3.4; 0.86/18.6/2.5 for the central gland, and 0.64/29.2/4.7; 0.71/26.3/2.2 for the peripheral zone.

CONCLUSIONS

We provide an openly accessible, expert-annotated 3T dataset of prostate MRI and a reproducible benchmark to foster the development of prostate segmentation algorithms.

Iron Oxide Nanoparticles for Visualization of Prostate Cancer in MRI

Prostate cancer (PCa) is one of the most common cancers in men. For detection and diagnosis of PCa, non-invasive methods, including magnetic resonance imaging (MRI), can reduce the risk potential of surgical intervention. To explore the molecular characteristics of the tumor, we investigated the applicability of ferumoxytol in PCa in a xenograft mouse model in two different tumor volumes, 500 mm3 and 1000 mm3. Macrophages play a key role in tumor progression, and they are able to internalize iron-oxide particles, such as ferumoxytol. When evaluating T2*-weighted sequences on MRI, a significant decrease of signal intensity between pre- and post-contrast images for each tumor volume (n = 14; p < 0.001) was measured. We, furthermore, observed a higher signal loss for a tumor volume of 500 mm3 than for 1000 mm3. These findings were confirmed by histological examinations and laser ablation inductively coupled plasma-mass spectrometry. The 500 mm3 tumors had 1.5% iron content (n = 14; σ = 1.1), while the 1000 mm3 tumors contained only 0.4% iron (n = 14; σ = 0.2). In vivo MRI data demonstrated a correlation with the ex vivo data (R2 = 0.75). The results of elemental analysis by inductively coupled plasma-mass spectrometry correlated strongly with the MRI data (R2 = 0.83) (n = 4). Due to its long retention time in the blood, biodegradability, and low toxicity to patients, ferumoxytol has great potential as a contrast agent for visualization PCa.

ADAMTS4-specific MR probe to assess aortic aneurysms in vivo using synthetic peptide libraries

The incidence of abdominal aortic aneurysms (AAAs) has substantially increased during the last 20 years and their rupture remains the third most common cause of sudden death in the cardiovascular field after myocardial infarction and stroke. The only established clinical parameter to assess AAAs is based on the aneurysm size. Novel biomarkers are needed to improve the assessment of the risk of rupture. ADAMTS4 (A Disintegrin And Metalloproteinase with ThromboSpondin motifs 4) is a strongly upregulated proteoglycan cleaving enzyme in the unstable course of AAAs. In the screening of a one-bead-one-compound library against ADAMTS4, a low-molecular-weight cyclic peptide is discovered with favorable properties for in vivo molecular magnetic resonance imaging applications. After identification and characterization, it's potential is evaluated in an AAA mouse model. The ADAMTS4-specific probe enables the in vivo imaging-based prediction of aneurysm expansion and rupture.

Microscopic multifrequency magnetic resonance elastography of ex vivo abdominal aortic aneurysms for extracellular matrix imaging in a mouse model

An abdominal aortic aneurysm (AAA) is a permanent dilatation of the abdominal aorta, usually accompanied by thrombus formation. The current clinical imaging modalities cannot reliably visualize the thrombus composition. Remodeling of the extracellular matrix (ECM) during AAA development leads to stiffness changes, providing a potential imaging marker. 14 apolipoprotein E-deficient mice underwent surgery for angiotensin II-loaded osmotic minipump implantation. 4 weeks post-op, 5 animals developed an AAA. The aneurysm was imaged ex vivo by microscopic multifrequency magnetic resonance elastography (µMMRE) with an in-plane resolution of 40 microns. Experiments were performed on a 7-Tesla preclinical magnetic resonance imaging scanner with drive frequencies between 1000 Hz and 1400 Hz. Shear wave speed (SWS) maps indicating stiffness were computed based on tomoelastography multifrequency inversion. As control, the aortas of 5 C57BL/6J mice were examined with the same imaging protocol. The regional variation of SWS in the thrombus ranging from 0.44 ± 0.07 to 1.20 ± 0.31 m/s was correlated fairly strong with regional histology-quantified ECM accumulation (R² = 0.79). Our results suggest that stiffness changes in aneurysmal thrombus reflect ECM remodeling, which is critical for AAA risk assessment. In the future, µMMRE could be used for a mechanics-based clinical characterization of AAAs in patients. STATEMENT OF SIGNIFICANCE: To our knowledge, this is the first study mapping the stiffness of abdominal aortic aneurysms with microscopic resolution of 40 µm. Our work revealed that stiffness critically changes due to extracellular matrix (ECM) remodeling in the aneurysmal thrombus. We were able to image various levels of ECM remodeling in the aneurysm reflected in distinct shear wave speed patterns with a strong correlation to regional histology-quantified ECM accumulation. The generated results are significant for the application of microscopic multifrequency magnetic resonance elastography for quantification of pathological remodeling of the ECM and may be of great interest for detailed characterization of AAAs in patients.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study

BACKGROUND

Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time.

PURPOSE

To investigate heat production as a function of femur temperature during and after battery-powered percutaneous bone drilling in a porcine in vivo model.

METHODS

We performed 16 femur drillings in 13 domestic pigs with an average age of 22 weeks and an average body temperature of 39.7 °C, using a battery-powered drilling system and an intraosseous temperature monitoring device. The standardized duration of the drilling procedure was 20 s. The bone core specimens obtained were embedded in 4% formalin, stained with haematoxylin and eosin (H&E) and sent for pathological analysis of tissue quality and signs of thermal damage.

RESULTS

No significant changes in the pigs' local temperature were observed after bone drilling with a battery-powered drill device. Across all measurements, the median change in temperature between the initial measurement and the temperature measured after drilling (at 20 s) was 0.1 °C. Histological examination of the bone core specimens revealed no signs of mechanical or thermal damage.

CONCLUSION

Overall, this preliminary study shows that battery-powered, drill-assisted harvesting of bone core specimens does not appear to cause mechanical or thermal damage.

Visualization and Quantification of the Extracellular Matrix in Prostate Cancer Using an Elastin Specific Molecular Probe

Human prostate cancer (PCa) is a type of malignancy and one of the most frequently diagnosed cancers in men. Elastin is an important component of the extracellular matrix and is involved in the structure and organization of prostate tissue. The present study examined prostate cancer in a xenograft mouse model using an elastin-specific molecular probe for magnetic resonance molecular imaging. Two different tumor sizes (500 mm3 and 1000 mm3) were compared and analyzed by MRI in vivo and histologically and analytically ex vivo. The T1-weighted sequence was used in a clinical 3-T scanner to calculate the relative contrast enhancement before and after probe administration. Our results show that the use of an elastin-specific probe enables better discrimination between tumors and surrounding healthy tissue. Furthermore, specific binding of the probe to elastin fibers was confirmed by histological examination and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Smaller tumors showed significantly higher signal intensity (p > 0.001), which correlates with the higher proportion of elastin fibers in the histological evaluation than in larger tumors. A strong correlation was seen between relative enhancement (RE) and Elastica-van Gieson staining (R2 = 0.88). RE was related to inductively coupled plasma-mass spectrometry data for Gd and showed a correlation (R2 = 0.78). Thus, molecular MRI could become a novel quantitative tool for the early evaluation and detection of PCa.

De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer

OBJECTIVES

The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping.

MATERIALS AND METHODS

Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8).

RESULTS

Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error.

CONCLUSIONS

When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using radiomics.

CT diagnostics of pulmonary embolism: Does iodine delivery rate still affect image quality in iterative reconstruction?

BACKGROUND

Computed tomographic (CT) imaging in suspected pulmonary artery embolism represents the standard procedure. Studies without iterative reconstruction proved beneficial using increased iodine delivery rate (IDR). This study compares image quality in pulmonary arteries on iteratively reconstructed CT images of patients with suspected pulmonary embolism using different IDR.

MATERIAL AND METHODS

1065 patients were included in the study. Patients in group A (n = 493) received an iodine concentration of 40 g/100 ml (IDR 1.6 g/s) and patients in group B (n = 572) an iodine concentration of 35 g/100 ml (IDR 1.4 g/s) at a flow rate of 4 ml/s. A 80-detector spiral CT scanner with iterative reconstruction was used. We measured mean density values in truncus pulmonalis, both pulmonary arteries and segmental pulmonary arteries. Subjectively, the contrast of apical and basal pulmonary arteries was determined on a 4-point Likert scale.

RESULTS

Radiodensity was significantly higher in all measured pulmonary arteries using the increased IDR (p < 0.001). TP: 483.0 HU vs. 393.4 HU; APD: 452.1 HU vs. 372.1 HU; APS: 448.2 HU vs. 374.4 HU; ASP: 443.9 vs. 374.4 HU. Subjectively assessed contrast enhancement in apical (p = 0.077) and basal (p = 0.429) lung sections showed no significant differences.

CONCLUSION

Higher IDR improves objective image quality in all patients with significantly higher radiodensities by iterative reconstruction. Subjective contrast of apical and basal lung sections did not differ. The number of non-sufficient scans decreased with high IDR.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports

MOTIVATION

The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results.

RESULTS

Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability  and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine

OBJECTIVES

Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patients. Therefore, we aimed to develop and evaluate deep learning models for the identification of clinically relevant abnormalities in bedside CXRs, using reference standards established by computed tomography (CT) and multiple radiologists.

MATERIALS AND METHODS

In this retrospective study, a dataset consisting of 18,361 bedside CXRs of patients treated at a level 1 medical center between January 2009 and March 2019 was used. All included CXRs occurred within 24 hours before or after a chest CT. A deep learning algorithm was developed to identify 8 findings on bedside CXRs (cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula). For the training dataset, 17,275 combined labels were extracted from the CXR and CT reports by a deep learning natural language processing (NLP) tool. In case of a disagreement between CXR and CT, human-in-the-loop annotations were used. The test dataset consisted of 583 images, evaluated by 4 radiologists. Performance was assessed by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value.

RESULTS

Areas under the receiver operating characteristic curve for cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula were 0.90 (95% confidence interval [CI], 0.87-0.93; 3 radiologists on the receiver operating characteristic [ROC] curve), 0.95 (95% CI, 0.93-0.96; 3 radiologists on the ROC curve), 0.85 (95% CI, 0.82-0.89; 1 radiologist on the ROC curve), 0.92 (95% CI, 0.89-0.95; 1 radiologist on the ROC curve), 0.99 (95% CI, 0.98-0.99), 0.99 (95% CI, 0.98-0.99), 0.98 (95% CI, 0.97-0.99), and 0.99 (95% CI, 0.98-1.00), respectively.

CONCLUSIONS

A deep learning model used specifically for bedside CXRs showed similar performance to expert radiologists. It could therefore be used to detect clinically relevant findings during after-hours and help emergency and intensive care physicians to focus on patient care.

Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting

Objective

This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging.

Methods

A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O₂-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of <0.05 was considered statistically significant.

Results

The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups.

Conclusions

The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19.

Is lung density associated with severity of COVID-19?

PURPOSE

Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19.

MATERIAL AND METHODS

Thirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities.

RESULTS

Intensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, p = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease.

CONCLUSIONS

The amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.

Simultaneous molecular MRI of extracellular matrix collagen and inflammatory activity to predict abdominal aortic aneurysm rupture

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with an up to 80% mortality in case of rupture. Current biomarkers fail to account for size-independent risk of rupture. By combining the information of different molecular probes, multi-target molecular MRI holds the potential to enable individual characterization of AAA. In this experimental study, we aimed to examine the feasibility of simultaneous imaging of extracellular collagen and inflammation for size-independent prediction of risk of rupture in murine AAA. The study design consisted of: (1) A outcome-based longitudinal study with imaging performed once after one week with follow-up and death as the end-point for assessment of rupture risk. (2) A week-by-week study for the characterization of AAA development with imaging after 1, 2, 3 and 4 weeks. For both studies, the animals were administered a type 1 collagen-targeted gadolinium-based probe (surrogate marker for extracellular matrix (ECM) remodeling) and an iron oxide-based probe (surrogate marker for inflammatory activity), in one imaging session. In vivo measurements of collagen and iron oxide probes showed a significant correlation with ex vivo histology (p < 0.001) and also corresponded well to inductively-coupled plasma-mass spectrometry and laser-ablation inductively-coupled plasma mass spectrometry. Combined evaluation of collagen-related ECM remodeling and inflammatory activity was the most accurate predictor for AAA rupture (sensitivity 80%, specificity 100%, area under the curve 0.85), being superior to information from the individual probes alone. Our study supports the feasibility of a simultaneous assessment of collagen-related extracellular matrix remodeling and inflammatory activity in a murine model of AAA.

The role of visceral adiposity in the severity of COVID-19: Highlights from a unicenter cross-sectional pilot study in Germany

BACKGROUND AND AIMS

Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity.

METHODS

Thirty patients with COVID-19 and a mean age of 65.6 ± 13.1 years from a level-one medical center in Berlin, Germany, were included in the present cross-sectional analysis. COVID-19 was confirmed by polymerase chain reaction (PCR) from nasal and throat swabs. A severe clinical course of COVID-19 was defined by hospitalization in the intensive care unit (ICU) and/or invasive mechanical ventilation. Fat was measured at the level of the first lumbar vertebra on routinely acquired low-dose chest computed tomography (CT).

RESULTS

An increase in visceral fat area (VFA) by ten square centimeters was associated with a 1.37-fold higher likelihood of ICU treatment and a 1.32-fold higher likelihood of mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference was associated with a 1.13-fold higher likelihood of ICU treatment and a 1.25-fold higher likelihood of mechanical ventilation.

CONCLUSIONS

Our proof-of-concept study suggests that visceral adipose tissue and upper abdominal circumference specifically increase the likelihood of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routine chest CTs may therefore be a simple tool for risk assessment in COVID-19 patients.

Comparing different deep learning architectures for classification of chest radiographs

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.

Multiparametric Assessment of Changes in Renal Tissue after Kidney Transplantation with Quantitative MR Relaxometry and Diffusion-Tensor Imaging at 3 T

Background: Magnetic resonance relaxometry (MRR) offers highly reproducible pixel-wise parametric maps of T1 and T2 relaxation times, reflecting specific tissue properties, while diffusion-tensor imaging (DTI) is a promising technique for the characterization of microstructural changes, depending on the directionality of molecular motion. Both MMR and DTI may be used for non-invasive assessment of parenchymal changes caused by kidney injury or graft dysfunction. Methods: We examined 46 patients with kidney transplantation and 16 healthy controls, using T1/T2 relaxometry and DTI at 3 T. Twenty-two early transplants and 24 late transplants were included. Seven of the patients had prior renal biopsy (all of them dysfunctional allografts; 6/7 with tubular atrophy and 7/7 with interstitial fibrosis). Results: Compared to healthy controls, T1 and T2 relaxation times in the renal parenchyma were increased after transplantation, with the highest T1/T2 values in early transplants (T1: 1700 ± 53 ms/T2: 83 ± 6 ms compared to T1: 1514 ± 29 ms/T2: 78 ± 4 ms in controls). Medullary and cortical ADC/FA values were decreased in early transplants and highest in controls, with medullary FA values showing the most pronounced difference. Cortical renal T1, mean medullary FA and corticomedullary differentiation (CMD) values correlated best with renal function as measured by eGFR (cortical T1: r = −0.63, p < 0.001; medullary FA: r = 0.67, p < 0.001; FA CMD: r = 0.62, p < 0.001). Mean medullary FA proved to be a significant predictor for tubular atrophy (p < 0.001), while cortical T1 appeared as a significant predictor of interstitial fibrosis (p = 0.003). Conclusion: Cortical T1, medullary FA, and FA CMD might serve as new imaging biomarkers of renal function and histopathologic microstructure.

Concurrent Molecular Magnetic Resonance Imaging of Inflammatory Activity and Extracellular Matrix Degradation for the Prediction of Aneurysm Rupture

BACKGROUND

Molecular magnetic resonance imaging is a promising modality for the characterization of abdominal aortic aneurysms (AAAs). The combination of different molecular imaging biomarkers may improve the assessment of the risk of rupture. This study investigates the feasibility of imaging inflammatory activity and extracellular matrix degradation by concurrent dual-probe molecular magnetic resonance imaging in an AAA mouse model.

METHODS

Osmotic minipumps with a continuous infusion of Ang II (angiotensin II; 1000 ng/[kg·min]) to induce AAAs were implanted in apolipoprotein-deficient mice (N=58). Animals were assigned to 2 groups. In group 1 (longitudinal group, n=13), imaging was performed once after 1 week with a clinical dose of a macrophage-specific iron oxide-based probe (ferumoxytol, 4 mgFe/kg, surrogate marker for inflammatory activity) and an elastin-specific gadolinium-based probe (0.2 mmol/kg, surrogate marker for extracellular matrix degradation). Animals were then monitored with death as end point. In group 2 (week-by-week-group), imaging with both probes was performed after 1, 2, 3, and 4 weeks (n=9 per group). Both probes were evaluated in 1 magnetic resonance session.

RESULTS

The combined assessment of inflammatory activity and extracellular matrix degradation was the strongest predictor of AAA rupture (sensitivity 100%; specificity 89%; area under the curve, 0.99). Information from each single probe alone resulted in lower predictive accuracy. In vivo measurements for the elastin- and iron oxide-probe were in good agreement with ex vivo histopathology (Prussian blue-stain: R²=0.96, P<0.001; Elastica van Giesson stain: R²=0.79, P<0.001). Contrast-to-noise ratio measurements for the iron oxide and elastin-probe were in good agreement with inductively coupled mass spectroscopy ( R²=0.88, R²=0.75, P<0.001) and laser ablation coupled to inductively coupled plasma-mass spectrometry.

CONCLUSIONS

This study demonstrates the potential of the concurrent assessment of inflammatory activity and extracellular matrix degradation by dual-probe molecular magnetic resonance imaging in an AAA mouse model. Based on the combined information from both molecular probes, the rupture of AAAs could reliably be predicted.

Quantitative 3D Assessment of 68Ga-DOTATOC PET/MRI with Diffusion-Weighted Imaging to Assess Imaging Markers for Gastroenteropancreatic Neuroendocrine Tumors: Preliminary Results

⁶⁸Ga-DOTATOC PET/MRI combines the advantages of PET in the acquisition of metabolic-functional information with the high soft-tissue contrast of MRI. SUVs in tumors have been suggested to be a measure of somatostatin receptor expression. A challenge with receptor ligands is that the distribution volume is confined to tissues with tracer uptake, potentially limiting SUV quantification. In this study, various functional 3-dimensional SUV apparent diffusion coefficient (ADC) parameters and arterial tumor enhancement were tested for ability to characterize gastroenteropancreatic (GEP) neuroendocrine tumors (NETs). Methods: For this single-center, cross-sectional study, 22 patients with 24 histologically confirmed GEP NET lesions (15 men and 7 women; median age, 61 y; range, 43-81 y) who underwent hybrid ⁶⁸Ga-DOTA PET/MRI at 3 T between January 2017 and July 2019 met the eligibility criteria. SUV, tumor-to-background ratio, total functional tumor volume, and mean and minimum ADC were measured on the basis of volumes of interest and examined with receiver-operating-characteristic analysis to determine cutoffs for differentiation between low- and intermediate-grade GEP NETs. The Spearman rank correlation coefficient was used to assess correlations between functional imaging parameters. Results: The ratio of PET-derived SUV_mean and diffusion-weighted imaging-derived minimum ADC was introduced as a combined variable to predict tumor grade, outperforming single predictors. On the basis of a threshold ratio of 0.03, tumors could be classified as grade 2 with a sensitivity of 86% and a specificity of 100%. SUV and functional ADCs, as well as arterial contrast enhancement parameters, showed nonsignificant and mostly negligible correlations. Conclusion: Because receptor density and tumor cellularity appear to be independent, potentially complementary phenomena, the combined ratio of PET/MRI and SUV_mean/ADC_min may be used as a novel biomarker allowing differentiation between grade 1 and grade 2 GEP NETs.

Use of quantitative T2 mapping for the assessment of renal cell carcinomas: first results

Background

Correct staging and grading of patients with clear cell renal cell carcinoma (cRCC) is of clinical relevance for the prediction of operability and for individualized patient management. As partial or radial resection with postoperative tumor grading currently remain the methods of choice for the classification of cRCC, non-invasive preoperative alternatives to differentiate lower grade from higher grade cRCC would be beneficial.

Methods

This institutional-review-board approved cross-sectional study included twenty-seven patients (8 women, mean age ± SD, 61.3 ± 14.2) with histopathologically confirmed cRCC, graded according to the International Society of Urological Pathology (ISUP). A native, balanced steady-state free precession T2 mapping sequence (TrueFISP) was performed at 1.5 T. Quantitative T2 values were measured with circular 2D ROIs in the solid tumor portion and also in the normal renal parenchyma (cortex and medulla). To estimate the optimal cut-off T2 value for identifying lower grade cRCC, a Receiver Operating Characteristic Curve (ROC) analysis was performed and sensitivity and specificity were calculated. Students’ t-tests were used to evaluate the differences in mean values for continuous variables, while intergroup differences were tested for significance with two-tailed Mann-Whitney-U tests.

Results

There were significant differences between the T2 values for lower grade (ISUP 1–2) and higher grade (ISUP 3–4) cRCC (p < 0.001), with higher T2 values for lower grade cRCC compared to higher grade cRCC. The sensitivity and specificity for the differentiation of lower grade from higher grade tumors were 83.3% (95% CI: 0.59–0.96) and 88.9% (95% CI: 0.52–1.00), respectively, using a threshold value of ≥110 ms. Intraobserver/interobserver agreement for T2 measurements was excellent/substantial.

Conclusions

Native T2 mapping based on a balanced steady-state free precession MR sequence might support an image-based distinction between lower and higher grade cRCC in a two-tier-system and could be a helpful addition to multiparametric imaging.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0222-8) contains supplementary material, which is available to authorized users.

Non-alcoholic fatty liver disease in underweight patients with inflammatory bowel disease: A case-control study

Non-alcoholic fatty liver disease (NAFLD) was shown to also occur in lean and underweight patients. So far, the prevalence of NAFLD in underweight individuals with and without inflammatory bowel disease (IBD) is insufficiently enlightened. In this cross-sectional age, gender and disease-matched case-control study, underweight patients (BMI<18.5 kg/m2) with inflammatory bowel disease (IBD), who underwent abdominal MRI at 1.5 T/3 T with fat-saturated fast-spin-echo imaging from 10/2005-07/2018 were analysed (control-to-case-ratio 1:1, n = 130). All patients were additionally investigated for duration, history of surgery, medical treatment, laboratory values, liver and spleen diameters. On MRI, liver fat was quantified by two observers based on the relative signal loss on T2-weighted fast spin-echo MR images with fat saturation compared to images without fat saturation. The prevalence of NAFLD/liver steatosis, defined as a measured intrahepatic fat content of at least 5%, was significantly higher in underweight IBD patients than in normal weight patients (87.6% versus 21.5%, p<0.001). Compared to the cases, the liver fat content of the controls was reduced by -0.19 units on average (-19%; 95%Cl: -0.20; -0.14). Similar results were obtained for the subgroup of non-IBD individuals (n = 12; -0.25 units on average (-25%); 95%Cl: -0.35; -0.14). Patients with extremely low body weight (BMI <17.5 kg/m2) showed the highest liver fat content (+0.15 units on average (+15%) compared to underweight patients with a BMI of 17.5-18.5 kg/m2 (p<0.05)). Furthermore, underweight patients showed slightly increased liver enzymes and liver diameters. There were no indications of significant differences in disease duration, type of medications or surgery between cases and controls and also, there were no significant differences between observers or field strengths (p>0.05). The prevalence of liver steatosis was higher among underweight IBD and non-IBD patients compared to normal weight controls. Also, underweight patients showed slightly increased liver enzymes and liver diameters, hinting at initial metabolic disturbances.

Differentiation of Predominantly Osteoblastic and Osteolytic Spine Metastases by Using Susceptibility-weighted MRI

Purpose To evaluate the use of susceptibility-weighted MRI for the differentiation of predominantly osteoblastic and osteolytic spine metastases. Materials and Methods For this prospective study, 53 study participants (mean age, 54.5 years ± 14.3 [range, 22-88 years]; 27 men with a mean age of 55.3 years ± 12.7 [range, 22-72 years] and 26 women with a mean age of 53.8 years ± 15.7 [range, 23-88 years]) with clinically suspected spine metastases underwent imaging with standard MRI sequences, susceptibility-weighted MRI, and CT. Sensitivities and specificities of MRI sequences for the detection of predominantly osteoblastic and osteolytic metastases were determined by using CT as the reference standard. The metastases-to-vertebral body signal intensity ratio (MVR) was calculated to compare modalities. Phantom measurements were obtained to correlate bone densities between MRI sequences and CT. Results A total of 64 metastases (38 predominantly osteoblastic, 26 predominantly osteolytic) were detected. Susceptibility-weighted MRI achieved a sensitivity of 100% (38 of 38) and specificity of 96% (25 of 26) for predominantly osteoblastic metastases and a sensitivity of 96% (25 of 26) and specificity of 100% (38 of 38) for predominantly osteolytic metastases. Standard MRI sequences achieved a sensitivity of 89% (34 of 38) and specificity of 73% (19 of 26) for predominantly osteoblastic metastases and a sensitivity of 73% (19 of 26) and specificity of 92% (35 of 38) for predominantly osteolytic metastases. MVR measurements obtained with susceptibility-weighted MRI demonstrated a strong correlation with those obtained with CT (R² = 0.75), whereas those obtained with T1-weighted MRI, T2-weighted MRI, and turbo inversion-recovery magnitude MRI showed a weak to moderate correlation (R² = 0.00, R² = 0.35, and R² = 0.39, respectively). Susceptibility-weighted MRI showed a strong correlation with CT with regard to metastases size (R² = 0.91). In phantom measurements, susceptibility-weighted MRI enabled the reliable differentiation of different degrees of mineralization (R² = 0.92 compared with CT). Conclusion Susceptibility-weighted MRI enables the reliable differentiation between predominantly osteoblastic and osteolytic spine metastases with a higher accuracy than standard MRI sequences. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Schweitzer in this issue.

Assessing venous thrombus in renal cell carcinoma: preliminary results for unenhanced 3D-SSFP MRI

AIM

To test the potential of unenhanced cardiac- and respiratory-motion-corrected three-dimensional steady-state free precession (3D-SSFP) magnetic resonance imaging (MRI) for the assessment of inferior vena cava (IVC) thrombus in patients with clear-cell renal cell carcinoma (cRCC), compared to standard contrast-enhanced (CE)-MRI and CE-computed tomography (CT).

MATERIALS AND METHODS

Eighteen patients with cRCC and IVC thrombus, who received CE-MRI and 3D-SSFP at 1.5 T between June 2015 and December 2017, were included. The diagnostic performance of 3D-SSFP in determining the level of thrombus extension, contrast-to-noise ratio (CNR), and image quality were compared with standard MRI/CT and validated against intraoperative and histopathology results.

RESULTS

There was 100% agreement between 3D-SSFP, 83.3% agreement between CE-MRI, and 71.4% agreement between CE-CT and surgical findings regarding the level of IVC thrombus. In addition, 3D-SSFP showed a slightly superior estimate of pathological IVC volume. 3D-SSFP reached a significantly higher CNR in the supra- and infrarenal IVC compared to the morphological sequence T2-weighted half-Fourier axial single-shot fast spin-echo (T2-HASTE) and all phases of CE-MRI. More specifically, 3D-SSFP showed a significantly higher CNR in the infrarenal IVC (mean CNR of 10.09±5.74 versus 4.21±2.33 in the delayed phase, p≤0.001) and in the suprarenal IVC (mean CNR of 9.22±4.11 versus 4.84±5.74 in the late arterial phase, p=0.015). CE-CT also was significantly inferior to 3D-SSFP (p≤0.01) and slightly inferior to CE-MRI (p>0.05). The thrombus delineation score for 3D-SSFP (4.38±0.67) was higher compared to CE-MRI (3.76±0.56, p=0.005).

CONCLUSION

This preliminary study indicates that 3D-SSFP can achieve an accurate assessment of IVC thrombus in cRCC patients without the need for contrast medium administration, being superior to standard MRI and CT.

Renal cell carcinoma with venous extension: prediction of inferior vena cava wall invasion by MRI

BACKGROUND

Renal cell carcinoma (RCC) are accompanied by inferior vena cava (IVC) thrombus in up to 10% of the cases, with surgical resection remaining the only curative option. In case of IVC wall invasion, the operative procedure is more challenging and may even require IVC resection. This study aims to determine the diagnostic performance of contrast-enhanced magnetic resonance imaging (MRI) for the assessment of wall invasion by IVC thrombus in patients with RCC, validated with intraoperative findings.

METHODS

Data were collected on 81 patients with RCC and IVC thrombus, who received a radical nephrectomy and vena cava thrombectomy between February 2008 and November 2017. Forty eight patients met the inclusion criteria. Sensitivity and specificity as well as the positive and negative predictive values were calculated for preoperative MRI, based on the assessments of the two readers for visual wall invasion. Furthermore, a logistic regression model was used to determine if there was an association between intraoperative wall adherence and IVC diameter.

RESULTS

Complete occlusion of the IVC lumen or vessel breach could reliably assess IVC wall invasion with a sensitivity of 92.3% (95%-CI: 0.75-0.99) and a specificity of 86.4% (95%-CI: 0.65-0.97) (Fisher-test: p-value< 0.001). The positive predictive value (PPV) was 88.9% (95%-CI: 0.71-0.98) and the negative predictive value reached 90.5% (95%-CI: 0.70-0.99). There was an excellent interobserver agreement for determining IVC wall invasion with a kappa coefficient of 0.90 (95%CI: 0.79-1.00).

CONCLUSIONS

The present study indicates that standard preoperative MR imaging can be used to reliably assess IVC wall invasion, evaluating morphologic features such as the complete occlusion of the IVC lumen or vessel breach. Increases in IVC diameter are associated with a higher probability of IVC wall invasion.

Sclerotic bone lesions as a potential imaging biomarker for the diagnosis of tuberous sclerosis complex

Tuberous-sclerosis-complex (TSC) is associated with a high lifetime risk of severe complications. Clinical manifestations are largely variable and diagnosis is often missed. Sclerotic-bone-lesions (SBL) could represent a potential imaging biomarker for the diagnosis of TSC. In this study, computed tomography (CT) data sets of 49 TSC patients (31 females) were included and compared to an age/sex matched control group. Imaging features of SBLs included frequency, size and location pattern. Sensitivities, specificities and cutoff values for the diagnosis of TSC were established for the skull, thorax, and abdomen/pelvis. In TSC patients, 3439 SBLs were detected, including 665 skull SBLs, 1426 thoracal SBLs and 1348 abdominal/pelvic SBLs. In the matched control-collective, 157 SBLs could be found. The frequency of SBLs enabled a reliable differentiation between TSC patients and the control collective with the following sensitivities and specificities. Skull: ≥5 SBLs, 0.783, 1; thorax: ≥4 SBLs, 0.967, 0.967; abdomen/pelvis: ≥5 SBLs: 0.938, 0.906. SBL size was significantly larger compared to controls (p < 0.05). Based on the frequency, size and location pattern of SBLs TSC can be suspected. SBLs may serve as a potential imaging biomarker in the workup of TSC patients.

Distinguishing globally-driven changes from regional- and local-scale impacts: The case for long-term and broad-scale studies of recovery from pollution

Marine ecosystems are subject to anthropogenic change at global, regional and local scales. Global drivers interact with regional- and local-scale impacts of both a chronic and acute nature. Natural fluctuations and those driven by climate change need to be understood to diagnose local- and regional-scale impacts, and to inform assessments of recovery. Three case studies are used to illustrate the need for long-term studies: (i) separation of the influence of fishing pressure from climate change on bottom fish in the English Channel; (ii) recovery of rocky shore assemblages from the Torrey Canyon oil spill in the southwest of England; (iii) interaction of climate change and chronic Tributyltin pollution affecting recovery of rocky shore populations following the Torrey Canyon oil spill. We emphasize that "baselines" or "reference states" are better viewed as envelopes that are dependent on the time window of observation. Recommendations are made for adaptive management in a rapidly changing world.

Detection of vessel wall calcifications in vertebral arteries using susceptibility weighted imaging

PURPOSE

Calcification of the brain supplying arteries has been linked to an increased risk for cerebrovascular disease. The purpose of this study was to test the potential of susceptibility weighted MR imaging (SWMR) for the detection of vertebral artery calcifications, based on CT as a reference standard.

METHODS

Four hundred seventy-four patients, who had received head CT and 1.5 T MR scans with SWMR, including the distal vertebral artery, between January 2014 and December 2016, were retrospectively evaluated and 389 patients were included. Sensitivity and specificity for the detection of focal calcifications and intra- and interobserver agreement were calculated for SWMR and standard MRI, using CT as a standard of reference. The diameter of vertebral artery calcifications was used to assess correlations between imaging modalities. Furthermore, the degree of vessel stenosis was determined in 30 patients, who had received an additional angiography.

RESULTS

On CT scans, 40 patients showed a total of 52 vertebral artery calcifications. While SWMR reached a sensitivity of 94% (95% CI 84-99%) and a specificity of 97% (95% CI 94-98%), standard MRI yielded a sensitivity of 33% (95% CI 20-46%), and a specificity of 93% (95% CI 90-96%). Linear regression analysis of size measurements confirmed a close correlation between SWMR and CT measurements (R ² = 0.74, p < 0.001). Compared to standard MRI (ICC = 0.52; CI 0.45-0.59), SWMR showed a higher interobserver agreement for calcification measurements (ICC = 0.84; CI 0.81-0.87).

CONCLUSIONS

For detection of distal vertebral artery calcifications, SWMR demonstrates a performance comparable to CT and considerably higher than conventional MRI.