Evolving Role of Dual-Energy CT in the Clinical Workup of Gout: A Retrospective Study

Tophaceous Gout Presenting as a Fluorodeoxyglucose-Avid Anterior Mediastinal Mass

In this case report, we describe an unusual presentation of gout, manifesting as a fluorodeoxyglucose-avid anterior mediastinal mass mimicking a malignant neoplasm on positron emission tomography/computed tomography. The unusual observation of tophaceous gout in the anterior mediastinum is of relevance to chest physicians and surgeons as well as to radiologists and pathologists evaluating patients with lesions in the mediastinum.

The comprehensive role of dual-energy CT in gout as an advanced diagnostic innovation

Gout is a common and growing health concern globally, marked by the deposition of monosodium urate (MSU) crystals in joints and soft tissues. While diagnosis relies on synovial fluid analysis, it is limited by technical difficulties and a notable rate of false negatives. Over the past decade, dual-energy computed tomography (DECT) has emerged as a highly sensitive and less-invasive modality for detecting MSU crystals. DECT offers several advantages, including the ability to visualize both intra- and extra-articular MSU deposits and to monitor crystal burden over time. It also aids in treatment planning by accurately assessing the therapeutic response. However, sensitivity of DECT can be lower in early-stage gout, and artifacts can occasionally result in false positives. Recent studies have highlighted new values of using DECT, such as predicting future flares in gout patients. In this review, we focus on the comprehensive clinical utility of DECT and its potential pitfalls in the diagnosis and management of gout.

Application of improved urate analysis algorithm based on spectral parameters in Podagra: A prospective study

OBJECTIVES

To explore whether the improved urate analysis (IUA) algorithm based on spectral parameters can reduce false positives in CT gout images compared with current urate analysis (CUA) algorithm.

MATERIALS AND METHODS

This prospective study was performed from May 2022 to May 2023. Spectral feet CT images of suspected gout participants were reconstructed by IUA and CUA algorithm. Qualitative diagnosis of IUA and CUA images was recorded and compared with the reference standard (ultrasound + conventional CT). Artifacts on IUA and CUA images of non-gout participants were recorded and compared; the maximum cross-sectional area of the maximum tophi (SIT-max) on IUA and CUA images of participants with gout were measured and compared.

RESULTS

There are 65 participants (mean age, 43.9 years ± 13.1 [SD]; 65 men) with 114 feet studies in the gout group, and 33 participants (mean age, 43.4 years ± 15.0 [SD]; 30 men) with 65 feet studies in the non-gout group. For all 179 feet studies, IUA images had higher specificity (19.2-86.6 % vs. 1.3-44.3 %) and accuracy (63.1-88.8 % vs. 41.3-57.0 %) than CUA images (P < 0.001). In the non-gout group, the reduction rates of artifacts from the nail bed, skin, beam hardening, vascular structures, tendons, and total artifacts on the IUA images compared to the CUA images was 40.5 %, 48.9 %, 74.3 %, 99.2 %, 99.6 %, and 80.0 %, respectively (P < 0.001). For 82 feet studies with tophi, SIT-max was higher on CUA images than IUA images (P < 0.05).

CONCLUSION

The improved urate analysis algorithm based on spectral parameters can reduce image artifacts and improve diagnostic efficacy.

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

Background

Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs.

Methods

DECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics.

Results

Segresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively.

Conclusion

In this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.

Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence

Introduction

Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.

Methods

We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.

Results

We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.

Conclusions

The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

An ultrasonographic study of gouty arthritis: Synovitis and its relationship to clinical symptoms: A retrospective analysis

Background and Aims

Joint pain is the main symptom of acute attacks in patients with gout, which if not managed properly, can develop into chronic gout. The aim of this study was to investigate the correlation between ultrasound (US) features of gouty arthritis (GA) and its clinical manifestations to provide a basis for diagnosing and evaluating the disease.

Methods

We retrospectively analyzed 182 sites in 139 patients with GA diagnosed by the Rheumatology and Immunology Department. Degree of pain was evaluated using the visual analog scale (VAS). Patients with GA were divided into active and inactive arthritis groups. Statistical differences between the two groups and the correlation between US features and clinical manifestations of the affected joints in patients with GA were analyzed.

Results

The groups had statistical significance in joint effusion, power Doppler ultrasonography (PDS), double contour sign, and bone erosion (p = 0.02, 0.001, 0.04, 0.04, respectively). Correlation analysis in this study showed that joint effusion and PDS were positively correlated with degree of pain (r s = 0.275, 0.269; p < 0.001, <0.001, respectively). Additionally, PDS was positively correlated with synovitis, joint effusion, bone erosion, and aggregates (r s = 0.271, 0.281, 0.222, 0.281; p < 0.001, <0.001, 0.003, <0.001, respectively).

Conclusions

Pathological US features, such as joint effusion, synovitis, PDS and bone erosion were more likely to be detected in GA with clinical signs and symptoms. PDS was positively correlated with joint effusion and synovitis, pain was closely related to PDS and joint effusion, which suggested that the clinical symptoms of GA were related to inflammation, reflecting the patient's condition to some extent. Therefore, musculoskeletal US is a useful clinical tool for managing patients with GA and can provide a reliable reference for diagnosing and treating GA.

Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging-An Intraindividual Comparison in 253 Oncologic Patients

OBJECTIVES

Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients.

MATERIALS AND METHODS

A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m²) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m² (n = 110), pre-obese: 25-29.9 kg/m² (n = 73), and obese: >30 kg/m² (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view.

RESULTS

While arterial contrast phases in DECT were associated with a higher CTDI_vol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R² = 0.738) and SECT (R² = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%).

CONCLUSION

DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.

Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT

Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.