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. Imaging features of the solitary renal metastasis highly mimicked a primary renal cell carcinoma. Additional unique features included renal pelvis invasion and disease recurrence despite adjuvant immunotherapy. This case underscores the role of routine surveillance in this patient population, varied radiologic appearance, and importance for pathologic diagnosis.

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. ML calculated dRV/dLV on pre-operative CTPA and compared with manual measures. Associations of dRV/dLV with patient characteristics and post-operative outcomes were evaluated including intensive care (ICU) and hospital length of stay (LOS) using multivariable linear regression analysis. Prolonged LOS was defined as greater than median. RESULTS: ML segmented the ventricles in 99/125 (79%) patients. The most common cause of failure was misidentification of the moderator band as the interventricular septum (7.9%). Mean dRV/dLV by ML was 1.4 ± 0.4 and strongly correlated with manual measures (r = 0.9-0.96 p < 0.0001). dRV/dLV was moderately correlated with measures of pulmonary hypertension on right heart catheterization and RV dilatation on echocardiogram (r = 0.5-0.6, p < 0.0001). dRV/dLV ≥ 1.2 was associated with proximal Jamieson type disease (p = 0.032), longer cardiopulmonary bypass (p = 0.037), aortic cross-clamp (p = 0.022) and circulatory arrest (p < 0.001) at surgery and dRV/dLV ≥ 1.6 with post-operative ECMO (p = 0.006). dRV/dLV was independently associated with prolonged ICU LOS (OR = 3.79, 95% CI 1.1-13.06, p = 0.035). CONCLUSION: dRV/dLV was associated with CTEPH severity and independently associated with prolonged ICU LOS. This CT parameter may therefore assist in perioperative planning. Further refinement of the ML algorithm or CTPA technique is required to avoid errors in ventricular segmentation. ADVANCES IN KNOWLEDGE: Automated right-to-left ventricle ratio measurement by machine learning is feasible and is independently associated with outcome after pulmonary endarterectomy.

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. control) and a skin cancer imaging dataset (malignant vs. benign) to explore the proposed measure of uncertainty based on experiments with different class imbalance-sample sizes, and experiments with images close to the classification boundary. We also further verified our hypothesis by examining the relationship with other performance metrics and cross-checking CNN predictions and confidence scores with an expert radiologist (available in the Supplementary Information). Additionally, bounds were derived on the uncertainty metric, and recommendations for interpretability were made. RESULTS: With respect to training set class imbalance for the pneumonia MI dataset, the uncertainty metric was minimized when both classes were nearly equal in size (regardless of training set size) and was approximately 17% smaller than the maximum uncertainty resulting from greater imbalance. We found that less-obvious test images (those closer to the classification boundary) produced higher classification uncertainty, about 10-15 times greater than images further from the boundary. Relevant MI performance metrics like accuracy, sensitivity, and sensibility showed seemingly negative linear correlations, though none were statistically significant (p [Formula: see text] 0.05). The expert radiologist and CNN expressed agreement on a small sample of test images, though this finding is only preliminary. CONCLUSIONS: This paper demonstrated the importance of uncertainty reporting alongside predictions in medical imaging. Results demonstrate considerable potential from automatically assessing classifier reliability on each prediction with the proposed uncertainty metric.

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. METHODS: This was a retrospective, single-center cohort study of patients who underwent PEA (January 2014-December 2017). Body composition (skeletal muscle mass and adiposity cross-sectional area) was quantified using thoracic computed tomography with sliceOmatic (TomoVision, Magog, QC, Canada) software. Body mass index was calculated. Association of body composition measures and 6MWD with clinical outcomes was evaluated using multivariable regression models. RESULTS: The study included 127 patients (42% men), aged 58 ± 14 years; body mass index was 31 ± 7 kg/m2 and 6MWD was 361 ± 165 m). Muscle and 6MWD were associated with disease severity measures. Of those surviving hospitalization (n = 125), a greater 6MWD was associated with a shorter hospital stay (1.9 median days per 100 m; p < .001) and higher likelihood of being discharged directly home from hospital (odds ratio, 2.1 per 100 m; P = .004), independent of age, sex, and body mass index. Those with a lower preoperative 6MWD (per 100 m) had a greater increase in their postoperative 6MWD (52 m; P < .0001), independent of age, sex, and body mass index. Body composition measures were not associated with hospital outcomes or exercise capacity in the first year postoperatively. CONCLUSIONS: Exercise capacity was a more prognostic marker of PEA outcomes compared with body composition. Future research is needed to explore pre-PEA rehabilitation strategies.

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. Our primary outcome was the number of hospitalizations for delivery associated with maternal sepsis. We further explored associations between patient characteristics, obstetric procedures/conditions, medical conditions, and maternal sepsis. Associations were presented using odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: There were 4,183 cases of sepsis in 3,653,783 hospitalizations for delivery during the study period, with an incidence of 114 (95% CI, 111 to 118) per 100,000 hospitalizations and a mortality rate of 0.5%. Annual sepsis rates per 100,000 hospitalizations ranged from a high of 160 (95% CI, 146 to 177) in 2004 to 94 (95% CI, 83 to 106) in 2011. Highest regional rates were observed in the Territories (224 per 100,000). Severe sepsis was seen in 14% (n = 568) of all patients with sepsis, which was one or more of septic shock (15%; n = 85), organ failure (61%; n = 345), intensive care unit admission (78%; n = 443), or mortality (3%; n = 19). Multivariable models showed that postpartum hemorrhage (OR, 2.9; 95% CI, 2.7 to 3.2), Cesarean delivery (OR, 3.2; 95% CI, 3.0 to 3.5), anemia (OR, 3.9; 95% CI, 3.5 to 4.3), hysterectomy (OR, 4.9; 95% CI, 3.6 to 6.6), chorioamnionitis (OR, 7.6; 95% CI, 6.9 to 8.3), as well as cardiorespiratory, renal and liver conditions were associated with maternal sepsis. CONCLUSION: Maternal sepsis rates have been decreasing in Canada but remain higher than those in the UK and USA. Our study explored associations with maternal sepsis and shows that one in seven women with sepsis develop severe sepsis-related morbidity, which warrants risk stratification and health policy changes.

RéSUMé: OBJECTIF: L'objectif de cette étude était d'examiner l'épidémiologie du sepsis maternel au Canada. MéTHODE: Nous avons mené une étude de cohorte rétrospective basée sur la population de femmes (≥ 20 semaines de gestation) hospitalisées pour accouchement dans tous les hôpitaux canadiens excluant le Québec entre le 1er avril 2004 et le 31 mars 2017. Les données ont été obtenues à partir de la base de données nationale de l'Institut canadien d'information sur la santé et les cas de sepsis ont été identifiés au moyen des codes de la Classification internationale des maladies, dixième révision, Canada (CIM-10-CA). Notre critère d'évaluation principal était le nombre d'hospitalisations pour accouchement associées à un sepsis maternel. Nous avons également exploré les associations entre les caractéristiques des patientes, les interventions / conditions obstétricales, les conditions médicales et le sepsis maternel. Les associations ont été présentées à l'aide de rapports de cotes (RC) et d'intervalles de confiance (IC) à 95 %. RéSULTATS: Il y a eu 4183 cas de sepsis parmi les 3 653 783 hospitalisations pour accouchement au cours de la période à l'étude, avec une incidence de 114 (IC 95 %, 111 à 118) pour 100 000 hospitalisations et un taux de mortalité de 0,5 %. Les taux annuels de sepsis pour 100 000 hospitalisations variaient d'un pic de 160 (IC 95 %, 146 à 177) en 2004 à 94 (IC 95 %, 83 à 106) en 2011. Les taux régionaux les plus élevés ont été observés dans les Territoires (224 pour 100 000). Un sepsis sévère a été observé chez 14 % (n = 568) des patientes atteintes de sepsis. Cette complication s'est manifestée en choc septique (15 %; n = 85), défaillance d'organe (61 %; n = 345), admission aux soins intensifs (78 %; n = 443), ou mortalité (3 %; n = 19), ou en combinaison de ces complications. Des modèles multivariés ont montré que l'hémorragie du post-partum (RC, 2,9; IC 95 %, 2,7 à 3,2), l'accouchement par césarienne (RC, 3,2; IC 95 %, 3,0 à 3,5), l'anémie (RC, 3,9; IC 95 %, 3,5 à 4,3), l'hystérectomie (RC, 4,9; IC 95 %, 3,6 à 6,6), la chorioamnionite (RC, 7,6; IC 95 %, 6,9 à 8,3), ainsi que les affections cardiorespiratoires, rénales et hépatiques étaient associées au sepsis maternel. CONCLUSION: Les taux de sepsis maternel ont diminué au Canada, mais demeurent plus élevés qu'au Royaume-Uni et aux États-Unis. Notre étude a exploré les associations avec le sepsis maternel et montre qu'une femme sur sept atteinte de sepsis développe une morbidité sévère liée au sepsis, ce qui justifie une stratification des risques et des changements dans les politiques de santé.

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. METHODS: We conducted a systematic review of randomized controlled trials evaluating ABSW systems compared to nonactive warming controls in noncardiac surgeries. Outcomes studied included postoperative pain scores and opioid consumption (primary outcomes) and other perioperative clinical variables such as temperature changes, blood loss, and wound infection (secondary outcomes). We searched Ovid MEDLINE daily, Ovid MEDLINE, EMBASE, CINHAL, Cochrane CENTRAL, and Web of Science from inception to June 2019. Quality of evidence (QoE) was rated according to the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Subgroup analysis sought to determine the effect of preoperative + intraoperative warming versus intraoperative warming alone. Metaregression evaluated the effect of year of publication, use of neuromuscular blockers, anesthesia, and surgery type on outcomes. RESULTS: Fifty-four articles (3976 patients) were included. Pooled results demonstrated that ABSW maintained normothermia compared to controls, during surgery (30 minutes postinduction [mean difference {MD}: 0.3°C, 95% confidence interval {CI}, 0.2-0.4, moderate QoE]), end of surgery (MD: 1.1°C, 95% CI, 0.9-1.3, high QoE), and up to 4 hours postoperatively (MD: 0.3°C, 95% CI, 0.2-0.5, high QoE). ABSW was not associated with difference in pain scores (<24 hours postoperatively, moderate to low QoE) or perioperative opioid consumption (very low QoE). ABSW increased patient satisfaction (MD: 2.2 points, 95% CI, 0.9-3.6, moderate QoE), reduced blood transfusions (odds ratio [OR] = 0.6, 95% CI, 0.4-1.0, moderate QoE), shivering (OR = 0.2, 95% CI, 0.1-0.4, high QoE), and wound infections (OR = 0.3, 95% CI, 0.2-0.7, high QoE). No significant differences were found for fluid administration (low QoE), blood loss (very low QoE), major adverse cardiovascular events (very low QoE), or mortality (very low QoE). Subgroup analysis and metaregression suggested increased temperature benefit with pre + intraoperative warming, use of neuromuscular blockers, and recent publication year. ABSW seemed to confer less temperature benefit in cesarean deliveries and neurosurgical/spinal cases compared to abdominal surgeries. CONCLUSIONS: ABSW is effective in maintaining physiological normothermia, decreasing wound infections, shivering, blood transfusions, and increasing patient satisfaction but does not appear to affect postoperative pain and opioid use.

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. An imbalanced class was created in the training set by varying the number of images in that class. Methods tested to mitigate class imbalance included oversampling, undersampling, and changing class weights of the RFM. Model performance was assessed by overall classification accuracy, overall F1 score, and specificity, recall, and precision of the imbalanced class. RESULTS: A close-to-balanced training set resulted in the best model performance, and a large imbalance with overrepresentation was more detrimental to model performance than underrepresentation. Oversampling and undersampling methods were both effective in mitigating class imbalance, and efficacy of oversampling techniques was class specific. CONCLUSION: This study systematically demonstrates the effect of class imbalance on two public X-ray datasets on RFM and CNN, making these findings widely applicable as a reference. Furthermore, the methods employed here can guide researchers in assessing and addressing the effects of class imbalance, while considering the data-specific characteristics to optimize imbalance mitigating methods.

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. Timing of seizure occurrence, if occurred at presentation or after the tumor diagnosis during follow-up period, in different grades of BTs, and survival of these patients were analyzed. RESULTS: Of the 901 identified patients, 662 (53% male; mean age: 56 years) were included in final analysis, and seizures occurred in 283 patients (43%). Patients with World Health Organization (WHO) grade III BT with seizures as a presenting symptom only had better survival (adjusted hazard ratio (HR): 0.27; 95% confidence interval (CI), 0.11-0.67; P = 0.004). Seizures that occurred after tumor diagnosis only (adjusted HR: 2.11; 95% CI, 1.59-2.81; P < 0.001) in patients with WHO grade II tumors (adjusted HR: 3.41; 95% CI, 1.05-11.1; P = 0.041) and WHO grade IV tumors (adjusted HR: 2.14; 95% CI, 1.58-2.90; P < 0.001) had higher mortality. Seizures that occurred at presentation and after diagnosis also had higher mortality (adjusted HR: 1.34; 95% CI, 1.00-1.80; P = 0.049), in patients with meningioma (adjusted HR: 6.19; 95% CI, 1.30-29.4; P = 0.021) and grade III tumors (adjusted HR: 6.19; 95% CI, 2.56-15.0; P < 0.001). CONCLUSION: Seizures occurred in almost half of the patients with BTs. The association between seizures in patients with BT and their survival depends on the time of occurrence of seizures, if occurring at presentation or after tumor diagnosis, and the type of tumor. Better survival was noted in patients with WHO grade III BTs who had seizures at presentation at the time of diagnosis, while higher mortality was noted in WHO grade II tumors who had seizure at presentation and after tumor diagnosis, and in grade IV tumors after tumor diagnosis.

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." The search included articles published between 1946 and 2018. Data regarding the ML task, sample size, and train-test pipeline were collected. RESULTS: A total of 167 articles were identified, of which 22 were included for qualitative analysis. There were only 4 studies that discussed sample-size determination methodologies, and 18 that tested the effect of sample size on model performance as part of an exploratory analysis. The observed methods could be categorized as pre hoc model-based approaches, which relied on features of the algorithm, or post hoc curve-fitting approaches requiring empirical testing to model and extrapolate algorithm performance as a function of sample size. Between studies, we observed great variability in performance testing procedures used for curve-fitting, model assessment methods, and reporting of confidence in sample sizes. CONCLUSIONS: Our study highlights the scarcity of research in training set size determination methodologies applied to ML in medical imaging, emphasizes the need to standardize current reporting practices, and guides future work in development and streamlining of pre hoc and post hoc sample size approaches.

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 (<18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), and obese (≥30 kg/m2). The primary outcome was combined neonatal morbidities of hypoglycemia and hyperbilirubinemia. The secondary outcome was length of hospital stay. All outcomes were compared between twins in various BMI groups, and data were analyzed using the chi-square test or ANOVA (Canadian Task Force Classification II-2). RESULTS: Data on 700 neonates born to 350 women were analyzed. Baseline maternal and neonatal characteristics were similar between the groups, except for a statistically significantly higher incidence of maternal hypertension (P < 0.02) and a trend towards increased gestational diabetes rates (P = 0.05) in women with overweight or obesity compared with women with underweight or normal weight. No association was noted between maternal BMI and occurrence of neonatal hypoglycemia, hyperbilirubinemia, and length of stay in either twin pair (P > 0.05) CONCLUSION: Maternal BMI had no detectable effect on neonatal morbidities and length of stay in twins admitted to the level I unit in the study centre.

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. RESULTS: For patients aged 70 years old, using a first-line MRIPH strategy resulted in a 16.8% relative risk reduction in strokes compared to DUS (0.080 vs 0.097 strokes per patient per lifetime), and an increased quality-adjusted-life years (12.23 vs 12.20) at an increased cost of $897.33 over a patient's lifetime ($5784.53 vs $4887.20 average total cost per patient per lifetime). The incremental cost-effectiveness ratio was $29,744 per quality-adjusted-life years. MRIPH remained cost-effective below a willingness-to-pay threshold of $50,000 for 91.8% of sensitivity analyses. CONCLUSION: MRIPH was found to be a cost-effective first-line tool to identify asymptomatic patients at high risk for stroke requiring annual surveillance and prompt management. Advances in Knowledge: Using MR imaging as a fist-line method to detect the presence of IPH provides clinically useful and cost-effective information that allows for enhanced risk evaluation and primary stroke prevention.

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. Magnetoencephalography coherence source imaging was consistent with persistent neuronal networks with areas of high coherence reliably present over the left lateral orbitofrontal region. Epileptogenic areas may have widespread networks involving the dominant frontal lobe in unique symptomatogenic areas.