18F-Fluciclovine PET/CT: A Potential Imaging Biomarker for the Evaluation of Multiple Myeloma.

Incidental concomitant second primary malignancy may be detected on PET/CT imaging. We present an 18F-fluciclovine PET/CT of a patient undergoing evaluation of biochemically recurrent prostate cancer with incidental radiotracer uptake within lytic osseous lesions confirmed to be multiple myeloma. We present the 18F-fluciclovine PET/CT images of an 83-year-old man with prostate cancer treated in 2005 who presented with back pain and a CT scan revealing multiple lytic osseous lesions concerning for metastases versus a plasma cell neoplasm.

F-FDG PET/CT for Response Assessment in Lung Cancer.

Treatment response assessment in lung cancer is crucial in the management strategy and outcome of patients. Accurate treatment response assessment can guide the treating physicians and improve patient survival. Anatomic and metabolic tumor response assessments have been evaluated extensively, showing a positive impact in the management of these patients.

Utility of F-Fluciclovine PET/CT for Detecting Prostate Cancer Recurrence in Patients With Low (< 1 ng/mL) or Very Low (< 0.3 ng/mL) Prostate-Specific Antigen Levels.

We reviewed a retrospective series of 126 F-fluciclovine PET/CT studies of patients with biochemically recurrent prostate cancer at low (< 1 ng/mL) and very low (< 0.3 ng/mL) prostate-specific antigen (PSA) levels. The rate of PET/CT positivity was 33% (15/46) in patients with low PSA levels and 0% (0/17) in patients with very low PSA levels.

Hybrid imaging of the abdomen and pelvis.

Accurate imaging is crucial for lesion detection in abdominal organs, for the noninvasive characterization of focal and diffuse abnormalities, and for surgical planning. To accomplish these tasks, several imaging modalities such as multidetector computer tomography (MDCT), magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) are used for abdominal imaging, providing important morphological, functional or metabolic information. More recently, PET/MRI has been gaining attention due to the possibility of combining high-resolution imaging with metabolic imaging.

[Hybrid imaging of the abdomen and pelvis. German version].

Accurate imaging is crucial for lesion detection in abdominal organs, for the noninvasive characterization of focal and diffuse abnormalities, and for surgical planning. To accomplish these tasks, several imaging modalities such as multidetector computer tomography (MDCT), magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) are used for abdominal imaging, providing important morphological, functional or metabolic information. More recently, PET/MRI has been gaining attention due to the possibility of combining high-resolution imaging with metabolic imaging.

Positron emission tomography as an adjuvant diagnostic test in the evaluation of checkpoint inhibitor-associated acute interstitial nephritis.

Background: Acute interstitial nephritis is an immune-related adverse event that can occur in patients receiving immune checkpoint inhibitor therapy. Differentiating checkpoint inhibitor-associated acute interstitial nephritis from other causes of acute kidney injury in patients with cancer is challenging and can lead to diagnostic delays and/or unwarranted immunosuppression. In this case report, we assess the use of F-flourodeoxyglucose positron-emission tomography imaging as an alternative diagnostic modality in the evaluation of potential acute interstitial nephritis.

Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction.

Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired.

An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets.

Owing to improvements in image recognition via deep learning, machine-learning algorithms could eventually be applied to automated medical diagnoses that can guide clinical decision-making. However, these algorithms remain a 'black box' in terms of how they generate the predictions from the input data. Also, high-performance deep learning requires large, high-quality training datasets.

Mandated health insurance increases rates of elective knee surgery.

Background: The recent federal ruling to against Affordable Care Act (ACA), specifically the mandate requiring people to buy insurance, has once again brought the healthcare reform debate to the spotlight. The ACA increased the number of insured Americans through the development of subsidized healthcare plans and health insurance exchanges. Insurance-based differences in the rate of upper extremity elective orthopaedic surgery have been described before and after healthcare reform in Massachusetts, where a similar mandate was put into place years before the ACA was passed.

Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.

Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model to predict sex based on hand radiographs of children and adults aged between 5 and 70 years. Of the 1531 radiographs tested, the algorithm predicted sex correctly in 95.

Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.

Objective: Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional neural networks. We compared the BAA performance of a cohort of pediatric radiologists with and without AI assistance.

Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools, how they can help them improve their practice, and be able to effectively supervise their use. Radiology training programs will need to develop curricula to help trainees acquire the knowledge to carry out this new supervisory duty of radiologists.

Delayed-type hypersensitivity to vaccine aluminum adjuvant causing subcutaneous leg mass and urticaria in a child.

A 3-year-old girl presented with a 7-month history of a waxing and waning left thigh mass associated with pruritus and erythema at the site of two previous DTaP-HepB-IPV vaccinations. Patch testing was positive to aluminum chloride, supporting a diagnosis of vaccine granuloma secondary to aluminum allergy; her symptoms had been well controlled with antihistamines and topical steroids. Injection site granulomas are a benign but potentially bothersome reaction to aluminum-containing immunizations that can be supportively managed, and we encourage strict adherence to the recommended vaccine schedule in this setting.

A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.

A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant.

Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.

Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an "eyeball test" to assess whether patients will tolerate major surgery or chemotherapy, "eyeballing" is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality.

Impact of Clinical Decision Support on Radiography for Acute Ankle Injuries: A Randomized Trial.

Introduction: While only 15-20% of patients with foot and ankle injuries presenting to urgent care centers have clinically significant fractures, most undergo radiography. We examined the impact of electronic point-of-care clinical decision support (CDS) on adherence to the Ottawa Ankle Rules (OAR), as well as use and yield of foot and ankle radiographs in patients with acute ankle injury.

Methods: We obtained institutional review board approval for this randomized controlled study performed April 18, 2012-December 15, 2013.

Fully Automated Deep Learning System for Bone Age Assessment.

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA.

Lower extremity venous reflux.

Venous incompetence in the lower extremity is a common clinical problem. Basic understanding of venous anatomy, pathophysiologic mechanisms of venous reflux is essential for choosing the appropriate treatment strategy. The complex interplay of venous pressure, abdominal pressure, venous valvular function and gravitational force determine the venous incompetence.

Impact of a clinical decision support tool on adherence to the Ottawa Ankle Rules.

Objective: The objective of the study is to determine impact of a clinical decision support (CDS) tool on documented adherence to the Ottawa Ankle Rules (OAR) and utilization and yield of ankle/foot radiography, for emergency department patients with acute ankle injury.

Methods: This is a before-and-after intervention study conducted at a 793-bed, quaternary care, academic hospital from August 2012 to October 2013. Emergency department visits from adults with acute ankle injury 6 months before and 8 months after the intervention were included.