Renal metastasis from esophageal adenocarcinoma: A rare recurrence.

Esophageal cancer, consisting primarily of squamous cell carcinoma and adenocarcinoma pathology, is a leading cause of morbidity and mortality worldwide with rates of metastasis at time of diagnosis up to 50%. Renal metastasis is rare, with most pathological diagnosis yielding squamous cell carcinoma. We present the unique case of a 78-year-old man with biopsy proven adenocarcinoma metastasis to the kidney on routine surveillance following initial esophagectomy, chemoradiation and adjuvant immunotherapy.

Right-to-left ventricle ratio determined by machine learning algorithms on CT pulmonary angiography images predicts prolonged ICU length of stay in operated chronic thromboembolic pulmonary hypertension.

Objective: Right-to-left ventricle diameter ratio (dRV/dLV) on CT pulmonary angiography (CTPA) is a predictor of outcomes in non-operated chronic thromboembolic pulmonary hypertension (CTEPH) patients. The purpose of this study is to evaluate the performance of a novel machine learning (ML) algorithm for dRV/dLV measurement in operated CTEPH patients and its association with post-operative outcomes.

Methods: This retrospective study reviewed consecutive CTEPH patients who underwent pulmonary endarterectomy between 2013 and 2017.

Quantifying uncertainty in machine learning classifiers for medical imaging.

Purpose: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical implications. This paper applied, validated, and explored a technique for assessing uncertainty in convolutional neural networks (CNNs) in the context of MI.

Materials And Methods: We used two publicly accessible imaging datasets: a chest x-ray dataset (pneumonia vs.

Incidence, morbidity, and associated factors for sepsis in women hospitalized for delivery: a nationwide retrospective observational population-based study in Canada.

Purpose: The objective of this research was to examine the epidemiology of maternal sepsis in Canada.

Methods: We conducted a population-based retrospective cohort study of women (≥ 20 weeks gestation) hospitalized for delivery in all Canadian hospitals (excluding Quebec) between 1 April 2004 and 31 March 2017. Data were obtained from the national Canadian Institute for Health Information database and sepsis cases were identified via International Classification of Diseases, Tenth Revision, Canada (ICD-10-CA) codes.

Clinical Implications of Body Composition and Exercise Capacity Following Pulmonary Endarterectomy.

Background: Pulmonary endarterectomy (PEA) is a curative procedure for patients with chronic thromboembolic pulmonary hypertension. Body composition and exercise capacity have been associated with adverse outcomes in patients undergoing cardiothoracic operations, but their significance with PEA is unclear. We evaluated the association of body composition and 6-minute walk distance (6MWD) with disease severity, hospital length of stay, discharge disposition, and postoperative functional recovery.

Effect of Perioperative Active Body Surface Warming Systems on Analgesic and Clinical Outcomes: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

Background: Inadvertent perioperative hypothermia is a common complication of surgery, and active body surface warming (ABSW) systems are used to prevent adverse clinical outcomes. Prior data on certain outcomes are equivocal (ie, blood loss) or limited (ie, pain and opioid consumption). The objective of this study was to provide an updated review on the effect of ABSW on clinical outcomes and temperature maintenance.

Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.

Purpose: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. This paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ML algorithms applied to MI.

Methods: We first selected five classes from the Image Retrieval in Medical Applications (IRMA) dataset, performed multiclass classification using the random forest model (RFM), and then performed binary classification using convolutional neural network (CNN) on a chest X-ray dataset.

Effect of seizure timing on long-term survival in patients with brain tumor.

Objective: Seizures often occur in patients with primary brain tumor (BT). The aim of this study was to determine if there is an association between the time of occurrence of seizures during the course of BT and survival of these patients.

Methods: This retrospective cohort study at Henry Ford Hospital, an urban tertiary referral center, included all patients who were diagnosed with primary BTs at Henry Ford Health System between January 2006 and December 2014.

Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review.

Purpose: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field.

Methods: We conducted a systematic literature search of articles using Medline and Embase with keywords including "machine learning," "image," and "sample size.

Maternal BMI in Twin Pregnancies and Impact on Neonatal Outcomes in the Level I Unit: A Retrospective Cohort Study.

Objective: With maternal obesity rates and twin pregnancies on the rise, the aim of this study was to assess the impact of pre-pregnancy or first trimester BMI on short-term neonatal morbidities in twins admitted to a level I unit.

Methods: This retrospective single-centre cohort study was conducted on twins born between January 1, 2010 and December 31, 2013 and admitted to the level I unit at Mount Sinai Hospital in Toronto, Ontario. Twin pairs were categorized according to maternal BMI: underweight (<18.

Cost-effectiveness of magnetic resonance carotid plaque imaging for primary stroke prevention in Canada.

Objective: Magnetic resonance of the carotid arteries provides important insight into plaque composition and vulnerability in addition to the traditional measure of stenosis. The purpose of this study was to evaluate the cost-effectiveness of MR imaging as a first-line modality to assess carotid disease and guide management for high-risk patients with <50% stenosis.

Methods: Using TreeAge Pro, a cost-effectiveness simulation was conducted comparing two strategies: (a) standard of care first-line carotid duplex ultrasound (DUS) with regular follow-up, vs (b) first-line MR assessment of stenosis and intraplaque haemorrhage (MRIPH) in which patients with IPH received annual DUS surveillance and immediate carotid endarterectomy in case of plaque progression.

Controllable yawning expressed as focal seizures of frontal lobe epilepsy.

Excessive yawning was described in some neurological conditions as part of periictal or ictal manifestations of epilepsy, most commonly temporal lobe. We present the first case of controllable yawning as a primary seizure semiology with dominant frontal lobe involvement in a 20-year-old man. Video electroencephalography recorded 8 yawning episodes accompanied with right arm movement correlating with rhythmic diffuse theta range activity with left hemispheric predominance.