Quantitative evaluation of natural progression of fatty infiltration and muscle atrophy in chronic rotator cuff tears without tear extension using magnetic resonance imaging.

Background: The pathology of and mechanisms underlying muscle degeneration remain unclear. We aimed to quantitatively evaluate the natural changes in fatty infiltration and muscle atrophy in patients with chronic rotator cuff tears using 3-dimensional 2-point Dixon magnetic resonance imaging. Methods: Thirty patients with nonoperatively observed rotator cuff tears without tear extension were evaluated using multiple magnetic resonance imaging examinations with a minimum interval of 2 years. The fatty infiltration ratio (%fat) and muscle volume of the rotator cuff muscles were compared between the 2 examinations in those with supraspinatus (SSP) tear <2 cm (<2 cm SSP group), SSP tear ≥2 cm (≥2 cm SSP group), and massive tear (massive group). The SSP) infraspinatus, and teres minor (ISP + TM), and subscapularis muscles were evaluated. Results: The massive group showed a significantly greater %fat than the <2 and ≥2 cm SSP groups in the SSP (P = .002) and ISP + TM muscles (P < .001). The total muscle volume did not differ among the 3 groups for all rotator cuff muscle components. The %fat values did not change in any rotator cuff components during the follow-up period in all groups. The total muscle volume in the massive group significantly decreased in the SSP (P = .018) and ISP + TM muscles (P = .013). Conclusion: The present results indicate that fatty infiltration of the torn muscle occurs in the early phase after a rotator cuff tear, whereas muscle atrophy appears to progress gradually in chronic rotator cuff tears. Early intervention before muscle degeneration should be considered if the tear involves the infraspinatus tendon.

RASopathies for Radiologists.

RASopathies are a heterogeneous group of genetic syndromes caused by germline mutations in a group of genes that encode components or regulators of the Ras/mitogen-activated protein kinase (MAPK) signaling pathway. RASopathies include neurofibromatosis type 1, Legius syndrome, Noonan syndrome, Costello syndrome, cardiofaciocutaneous syndrome, central conducting lymphatic anomaly, and capillary malformation-arteriovenous malformation syndrome. These disorders are grouped together as RASopathies based on our current understanding of the Ras/MAPK pathway. Abnormal activation of the Ras/MAPK pathway plays a major role in development of RASopathies. The individual disorders of RASopathies are rare, but collectively they are the most common genetic condition (one in 1000 newborns). Activation or dysregulation of the common Ras/MAPK pathway gives rise to overlapping clinical features of RASopathies, involving the cardiovascular, lymphatic, musculoskeletal, cutaneous, and central nervous systems. At the same time, there is much phenotypic variability in this group of disorders. Benign and malignant tumors are associated with certain disorders. Recently, many institutions have established multidisciplinary RASopathy clinics to address unique therapeutic challenges for patients with RASopathies. Medications developed for Ras/MAPK pathway-related cancer treatment may also control the clinical symptoms due to an abnormal Ras/MAPK pathway in RASopathies. Therefore, radiologists need to be aware of the concept of RASopathies to participate in multidisciplinary care. As with the clinical manifestations, imaging features of RASopathies are overlapping and at the same time diverse. As an introduction to the concept of RASopathies, the authors present major representative RASopathies, with emphasis on their imaging similarities and differences. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Fast abdominal magnetic resonance elastography with simultaneous encoding of three-dimensional displacements.

Three-dimensional (3D) magnetic resonance elastography (MRE) is more accurate than two-dimensional (2D) MRE; however, it requires long-term acquisition. This study aimed to reduce the acquisition time of abdominal 3D MRE using a new sample interval modulation (short-SLIM) approach that can acquire all three motions faster while reducing the prolongation of echo time and flow compensation. To this end, two types of phantom studies and an in vivo test of the liver in three healthy volunteers were performed to compare the performances of conventional spin-echo echo-planar (SE-EPI) MRE, conventional SLIM and short-SLIM. One phantom study measured the mean amplitude and shear modulus within the overall region of a homogeneous phantom by changing the mechanical vibration power to assess the robustness to the lowered phase-to-noise ratio in short-SLIM. The other measured the mean shear modulus in the stiff and background materials of a phantom with an embedded stiffer rod to assess the performance of short-SLIM for complex wave patterns with wave interference. The Spearman's rank correlation coefficient was used to assess similarity of elastograms in the rod-embedded phantom and liver between methods. The results of the phantom study changing the vibration power indicated that there was little difference between conventional MRE and short-SLIM. Moreover, the elastogram pattern and the mean shear modulus in the rod-embedded phantom in conventional SLIM and short-SLIM did not change for conventional MRE; the liver test also showed a small difference between the acquisition techniques. This study demonstrates that short-SLIM can provide MRE results comparable to those of conventional MRE. Short-SLIM can reduce the total acquisition time by a factor of 2.25 compared to conventional 3D MRE time, leading to an improvement in the accuracy of shear modulus estimation by suppressing the patient movements.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

The Prospective Natural History Study of Patients with Intractable Venous Malformation and Klippel-Trenaunay Syndrome to Guide Designing a Proof-of-Concept Clinical Trial for Novel Therapeutic Intervention.

Background: The natural history of venous malformation (VM) and Klippel-Trenaunay Syndrome (KTS) has not been quantitatively studied. To obtain benchmarks to guide designing clinical trials to assess safety and efficacy of novel drug candidates, the clinical course of the patients was followed for 6 months. Methods and Results: This is a multicenter prospective observational study evaluating the change rate in lesion volume from baseline with magnetic resonance images, as the primary endpoint. In addition, disease severities, performance status (PS), pain visual analog scale (VAS) score, quality of life (QoL), infections, and coagulation markers were also evaluated. Thirty-four patients (VM = 17, KTS = 17, 1-53 of age; median 15.9 years) with measurable lesion volume were analyzed. There was no statistically significant difference in the lesion volume between baseline and day 180, and the mean change rate (standard deviation) was 1.06 (0.28). There were no baseline characteristics that affected the change in lesion volume over 6 months. However, there were patients who showed more than 20% volume change and it was suggested that the lesion volume was largely impacted by local infection. There were no statistically significant changes in pain VAS score, severity, PS, QoL score, D-dimer, and platelet count over 6 months within all patients analyzed. Conclusion: The results showed the representative natural course of VM and KTS for a 6-month period with objective change of lesion volume and other factors, suggesting that it is scientifically reasonable to conduct a Phase 2 proof-of-concept study without a placebo arm, using the results of this study as the control. Clinical Trial Registration: NCT04285723, NCT04589650.

Pediatric Ribs at Chest Radiography: Normal Variants and Abnormalities.

Normal variants and abnormalities of the ribs are frequently encountered on chest radiographs. Accurate identification of normal variants is crucial to avoid unnecessary investigations. A meticulous evaluation of rib abnormalities can provide valuable insights into the patient's symptoms, and even when no osseous condition is suspected, rib abnormalities may offer critical clues to underlying conditions. Rib abnormalities are associated with various conditions, including benign tumors, malignant tumors, infectious and inflammatory conditions, vascular abnormalities, metabolic disorders, nonaccidental injuries, malformation syndromes, and bone dysplasias. Abnormalities of the ribs are classified into three groups based on their radiographic patterns: focal, multifocal, and diffuse changes. Focal lesions are further subdivided into nonaggressive lesions, aggressive lesions, and infectious and inflammatory disorders. Radiologists should be aware of individual disorders of the pediatric ribs, including their imaging findings, relevant clinical information, and underlying pathogenesis. Differential diagnoses are addressed as appropriate. Since chest radiographs can suffice for diagnosis in certain cases, the authors emphasize a pattern recognition approach to radiographic interpretation. However, additional cross-sectional imaging may be necessary for focal lesions such as tumors or inflammatory conditions. Awareness of disease-specific imaging findings helps ascertain the nature of the lesion and directs appropriate management. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Quantitative Assessment of Calf Muscle Volume, Strength, and Quality After Achilles Tendon Rupture Repair: A 1-Year Prospective Follow-up Study.

BACKGROUND: A number of studies have reported that calf muscle atrophy is a common long-term problem after Achilles tendon repair; however, there is still a lack of data concerning early postoperative morphological changes in the calf muscle after surgery. PURPOSE: To investigate changes over time in calf muscle volume and fatty degeneration during 1 year after Achilles tendon repair. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: A prospective 1-year serial magnetic resonance imaging study was carried out with 20 patients who underwent tendon repair for unilateral acute Achilles tendon rupture. The magnetic resonance imaging assessment in addition to clinical and functional evaluations was performed at 1, 3, 6, and 12 months after surgery. The muscle volume of the medial and lateral gastrocnemius, soleus, and flexor hallucis longus (FHL) and fatty degeneration of the medial and lateral gastrocnemius and soleus were measured for the calf muscles, and the relative volume and fatty degeneration changes in the affected leg compared with the healthy contralateral leg were calculated as a percentage ([injured/healthy control] × 100) to assess structural changes over time. RESULTS: Muscle volumes of the medial gastrocnemius, lateral gastrocnemius, soleus, and FHL were 92.3%, 92.8%, 84.6%, and 95.9% at 12 months after surgery, respectively. Medial and lateral gastrocnemius and FHL muscle volumes improved over time and recovered to almost equal to that of the healthy side at 12 months after surgery. The soleus muscle volume did not recover significantly over time and was statistically significantly smaller than that of the healthy side at 12 months (P = .029). Fatty degeneration rates of the medial gastrocnemius, lateral gastrocnemius, and soleus were 118.2%, 113.9% and 121.1% at 12 months after surgery, respectively. Fatty degeneration of the medial and lateral gastrocnemius did not change significantly, but there was a statistically significant increase in fatty degeneration of the soleus over time (P < .001). CONCLUSION: Within the triceps surae muscle, the soleus was the most negatively affected by injury and repair for both muscle volume and fatty degeneration. Postoperative management to recover the soleus muscle function before a return to sporting activities should be considered in the future.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

Imaging findings of juvenile idiopathic arthritis and autoinflammatory diseases in children.

Juvenile idiopathic arthritis (JIA) is a collective term for pediatric inflammatory arthritis of unknown etiology, which presents diverse clinical and imaging findings. The pathogenesis is complex; however, most cases stem from an autoimmune mechanism. Herein we provide a short review of imaging findings of JIA. Imaging assessment begins with plain radiography demonstrating joint swelling, periarticular osteopenia, and juxtaarticular bone erosion. Bone erosion occurs later in JIA. Instead, aberrant epimetaphyseal growth often gives the first clue to the diagnosis. US and MRI can demonstrate the details of the synovium, cartilage, and subchondral bone. JIA is subdivided into oligoarthritis, polyarthritis (rheumatoid factor-negative and positive), psoriatic arthritis, enthesitis-related arthritis, and systemic JIA. Awareness of the different clinical characteristics, pathogenic background, and prognosis of each subtype facilitates a more advanced, imaging-based diagnosis. Unlike the other types, systemic JIA is an autoinflammatory disease accompanied by inflammatory cytokinemia and systemic symptoms stemming from aberrant activation of the innate immunity. Other autoinflammatory diseases, both monogenic (e.g., NOMID/CINCA) and multifactorial (e.g., CRMO), are also discussed.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Correlation of muscle ultrasound with clinical and pathological findings in idiopathic inflammatory myopathies.

INTRODUCTION/AIMS: In idiopathic inflammatory myopathies (IIMs), the change in muscle echogenicity and its histopathological basis are not well understood. We quantitatively measured muscle echogenicity in patients with IIMs and evaluated its correlation with disease activity and histopathological findings. METHODS: This study involved patients with IIMs who underwent both ultrasonography (US) and muscle biopsy, as well as age- and sex-matched rheumatoid arthritis patients as inflammatory disease controls. On US, axial images of the right biceps brachii and vastus medialis were obtained. Standardized histopathological scoring was used to quantitatively measure each pathological domain. RESULTS: Forty-two patients (17 with inclusion body myositis [IBM] and 25 with IIMs other than IBM) and 25 controls were included. The muscle echo intensity (EI) of patients with IIMs was significantly higher than that of controls. Muscle EI showed significant correlations with creatine kinase (r = 0.66, p < .001) and muscle strength (r = -0.73, p < .0001) in patients with non-IBM IIMs. In patients with IBM, moderate correlation was found between muscle EI and quadriceps muscle strength (r = -0.53, p = .028). Histopathologically, the number of infiltrating CD3+ inflammatory cells correlated with muscle EI in the non-IBM group (r = 0.56, p = .017), but not in the IBM group. DISCUSSION: Muscle EI may be useful as a surrogate marker of muscle inflammation in non-IBM IIM. Increased muscle EI may be difficult to interpret in patients with long-standing IBM, which has advanced and complex histopathology.

Ex-vivo 1.5T MR Imaging versus CT in Estimating the Size of the Pathologically Invasive Component of Lung Adenocarcinoma Spectrum Lesions.

PURPOSE: The purpose of this study was to investigate whether ex-vivo MRI enables accurate estimation of the invasive component of lung adenocarcinoma. METHODS: We retrospectively reviewed 32 patients with lung adenocarcinoma who underwent lung lobectomy. The specimens underwent MRI at 1.5T. The boundary between the lesion and the normal lung was evaluated on a 5-point scale in each three MRI sequences, and a one-way analysis of variance and post-hoc tests were performed. The invasive component size was measured histopathologically. The maximum diameter of each solid component measured on CT and MR T1-weighted (T1W) images and the maximum size obtained from histopathologic images were compared using the Wilcoxon signed-rank test. Inter-reader agreement was evaluated using intraclass correlation coefficients (ICC). RESULTS: T1W images were determined to be optimal for the delineation of the lesions (P < 0.001). The histopathologic invasive area corresponded to the area where the T1W ex-vivo MR image showed a high signal intensity that was almost equal to the intravascular blood signal. The maximum diameter of the solid component on CT was overestimated compared with the maximum invasive size on histopathology (mean, 153%; P < 0.05), while that on MRI was evaluated mostly accurately without overestimation (mean, 108%; P = 0.48). The interobserver reliability of the measurements using CT and MRI was good (ICC = 0.71 on CT, 0.74 on MRI). CONCLUSION: Ex-vivo MRI was more accurate than conventional CT in delineating the invasive component of lung adenocarcinoma.

Multidisciplinary management of disseminated Exophiala dermatitidis mycosis in an infant with mixed phenotype acute leukemia: a case report.

BACKGROUND: Exophiala dermatitidis is a dematiaceous fungus isolated from various environmental sources. Systemic E. dermatitidis infections can lead to fatal outcomes, and treatment has not yet been standardized. Although E. dermatitidis is also known to cause cutaneous infection, it has not been previously reported to appear as ecthyma gangrenosum (EG), an uncommon cutaneous lesion in neutropenic patients that is mainly caused by Pseudomonas aeruginosa. CASE PRESENTATION: A 2-month-old male infant with mixed-phenotype acute leukemia presented with prolonged fever unresponsive to antibacterial and antifungal agents during myelosuppression due to remission induction therapy. He also presented with skin lesions on the left wrist and left lower quadrant of the abdomen. The abdominal lesion gradually turned black and necrotic, which was consistent with the findings of the EG. E. dermatitidis was isolated from the blood, stool, wrist skin, and endotracheal aspirate. During hematopoietic recovery, consolidation in both lungs was evident. Multiagent antifungal treatment failed to eliminate E. dermatitidis from blood. In order to salvage the central venous catheter, ethanol lock therapy (ELT) was adopted, following which the blood culture became negative. The abdominal lesion that evolved as a necrotic mass connecting the small intestine and subcutaneous tissue adjacent to the skin was surgically resected. After these interventions, the general condition improved. CONCLUSION: Disseminated E. dermatitidis mycosis in the neutropenic infant was successfully managed with a multidisciplinary treatment consisting of multiagent antifungal treatment, ELT, and surgery.