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Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and debilitating disease with limited therapeutic options. The aim of this clinical study was to evaluate the safety, efficacy and pharmacokinetics of a novel regimen comprised of metronomic oxaliplatin (O), chronomodulated capecitabine (X) and UGT1A1 genotype-guided dosing of irinotecan (IRI) [OXIRI] as well as its immunomodulatory effects. Thirty-six patients were enrolled into either dose-escalation or expansion cohorts. In the dose escalation phase, capecitabine doses (2000, 2650, 3500 and 4500 mg/day) were administered at midnight on days 1 to 14 while oxaliplatin and irinotecan were intravenously infused at fixed doses of 50 and 75 mg/m

Objective: The COVID-19 pandemic may cause a major mental health impact. We aimed to identify demographic or clinical factors associated with psychiatric admissions where COVID-19 was attributed to contribute to mental state, compared to admissions which did not. Methods: A retrospective cohort study was undertaken of inpatients admitted to Northern Psychiatric Unit 1, Northern Hospital in Melbourne, Victoria, Australia during 27/02/2020 to 08/07/2020. Data were extracted for participants who identified COVID-19 as a stressor compared to participants who did not. Fisher’s exact test and Mann-Whitley rank sum test were used. Results: Thirty six of 242 inpatients reported the COVID-19 pandemic contributed to mental ill health and subsequent admission. Reasons given included social isolation, generalized distress about the pandemic, barriers to support services, disruption to daily routine, impact on employment, media coverage, re-traumatization, cancelled ECT sessions, loss of loved ones, and increased drug use during the lockdown. Chronic medical conditions or psychiatric multimorbidity were positively associated and smoking status was negatively associated with reporting the COVID-19 pandemic as a contributor to mental ill health. Conclusion: Screening and identifying vulnerable populations during and after the global disaster is vital for timely and appropriate interventions to reduce the impact of the pandemic worldwide.

INTRODUCTION Many patients with Crohn's Disease will require surgical resection. While many studies have described outcomes following ileocecectomy, few have evaluated surgical resection of other portions of small bowel. We sought to compare open and minimally invasive surgery (MIS) approaches for small bowel resection excluding ileocecectomy of patients with Crohn's Disease using the National Surgical Quality Improvement Program (NSQIP) database. METHODS The NSQIP database was queried for patients with Crohn's disease or complications related to Crohn's disease who underwent segmental small bowel resection utilizing open or minimally invasive approaches between 2012 and 2018. Patients requiring ileocecectomy or diagnosed with ascites, disseminated cancer, pre-operative sepsis, ASA class 5, and patients requiring mechanical ventilation were excluded. The association of pre-operative variables including patient demographic information and comorbidities with surgical approach were examined using Fishers exact test. Intraoperative, and 30-day post-operative outcomes were compared between the groups using both univariate and multivariate logistical regression models. SAS was used for data analysis with p

Background Little is known regarding the impact of hospital academic status on outcomes following rectal cancer surgery. We compare these outcomes for nonmetastatic rectal adenocarcinoma at academic versus community institutions. Methods The National Cancer Database (2010–2016) was queried for patients with nonmetastatic rectal adenocarcinoma who underwent resection. Propensity score matching was performed across facility cohorts to balance confounding covariates. Kaplan-Meier estimation and Cox-proportional hazards regression were used to analyze survival, other short and long-term outcomes were analyzed by way of logistic regression. Results After matching, 15,096 patients were included per cohort. Academic centers were associated with significantly decreased odds of conversion and positive margins with significantly increased odds of ≥12 regional nodes examined. Academic programs also had decreased odds of 30 and 90-day mortality and decreased 5-year mortality hazard. After matching for facility volume, no significant differences in outcomes between centers was seen. Conclusions No difference between academic and community centers in outcomes following surgery for non-metastatic rectal cancer was seen after matching for facility procedural volume.

Right colon diverticulitis is a rare disease process for which there are no established treatment guidelines, and outcomes following surgical management are underreported in the literature. We sought to describe the demographics of patients undergoing ileocecectomy for right colon diverticulitis and compare short-term postoperative outcomes between open and minimally invasive approaches. The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) was queried for patients with diverticulitis of the colon who underwent ileocecectomy between 2012 and 2019. Patients with ascites, disseminated cancer, ASA class 5, and patients requiring mechanical ventilation were excluded. Preoperative, intraoperative, and 30-day postoperative outcomes were compared between the groups using both univariable chi-square or t-tests and multivariable logistical regression models. 484 patients met inclusion criteria, 150 (31%) of whom underwent open surgery and 334 (69%) who underwent minimally invasive surgery with an 18% conversion rate. 71% of patients were White, 11% of were Black, 7% were Hispanic, and 5% were Asian. The indication for surgery differed significantly by approach with acute diverticulitis representing 47% of indications for open cases and 25% for MIS cases (p

Minimally invasive resection of colon cancer at the splenic flexure can be technically challenging with concerns for a suboptimal oncologic outcome. We aimed to compare open and minimally invasive approaches following curative resection. The National Cancer Database was queried for patients with non-metastatic colon adenocarcinoma at the splenic flexure who underwent resection from 2010 to 2016. Cohorts were separated into open and minimally invasive approaches, and demographic and clinicopathologic variables were compared. Propensity-score matching (PSM) was utilized to balance potential confounding covariates between cohorts to elucidate the independent association between surgical approach and outcomes. Kaplan–Meier estimation and Cox-proportional hazards regression were used to analyze survival. Secondary outcomes were analyzed by way of logistic regression or Mann–Whitney U test. After matching, 842 patients were compared between approaches. Patients who underwent minimally invasive surgery had no significant difference in regional nodes ≥ 12 examined, positive margins, negative circumferential margins, unplanned 30-day readmission, or time from surgery to initiation of chemotherapy when compared to patients who underwent open surgery. Minimally invasive surgery was significantly associated with decreased odds of 30-day mortality, 90-day mortality, and decreased mortality hazard for 5-year overall survival compared to open surgery. The optimal approach for surgical management of splenic flexure colon cancer has not been standardized given its rarity and exclusion from randomized controlled trials. Our retrospective review suggests that minimally invasive resection of splenic flexure colon cancers in carefully selected patients is associated with equivalent oncologic outcomes as well as improved short and long-term survival compared to an open approach.

Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm $^2$ for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.

Laryngotracheal separation injuries are a rare but serious condition, as survival from such injuries relies on proper airway management. As a result, recommendations for management have been based on small case reports and expert opinion. We reviewed our last 10 years of experience with managing laryngotracheal separation injuries and identified 6 cases for chart review. Awake tracheostomy or videolaryngobronchoscopy was used in each case to initially obtain the airway. Surgical repair was then performed immediately using nonabsorbable monofilament suture or a miniplate, and a low fenestrated tracheostomy was placed. All of our patients who followed up were decannulated, eating regular diets, and had satisfactory voice quality at 3 months postoperatively. Review of the literature revealed that, while management strategies have changed over time, treatment still varies widely depending on surgeon preference and the details of each injury. Outcomes from our series suggest that our described techniques and management strategies can be used with good outcomes. We believe that this is due to securing a safe airway, early surgical intervention with no unnecessary tissue dissection, effective reconstruction of the airway, and the fenestrated tracheostomy technique.

Objective Many patients with advanced illness are unrealistically optimistic about their prognosis. We test for the presence of several cognitive biases, including optimism bias, illusion of superiority, self-deception, misattribution, and optimistic update bias, that could explain unrealistically optimistic prognostic beliefs among advanced cancer patients and quantifies the extent to which hope exacerbates these biases. Methods A cross-sectional survey was administered to 200 advanced cancer patients with physician-estimated prognoses of one year or less. Hope was measured using the Herth Hope Index (HHI). Hypotheses were tested using linear and logistic regressions and a structural-equation model. Results Results are consistent with the presence of optimism bias, illusion of superiority, self-deception, and misattribution. All of these biases are amplified by higher levels of hope. Each 1-point higher HHI is associated with a 6% (OR: 1.06; 95% CI: 1.01-1.11) greater odds of believing their illness is curable, a 0.33-year (95% CI: 0.17-0.49) longer expected survival, a 6% (OR: 1.06; 95% CI: 1.02-1.11) higher probability of believing that survival outcomes are better than the average patient, a 5% higher odds of believing primary intent of treatment is curative (OR: 1.05; 95% CI: 1.00-1.10), and a 12% (OR: 1.12; 95% CI: 1.05-1.17) higher odds of believing they are well-informed. Mediation analyses revealed that hope significantly mediates the effect of mental-well-being and loneliness on expected survival. Conclusions Results suggest advanced cancer patients succumb to several cognitive biases which are exacerbated by greater levels of hope. As a result, they are susceptible to possible over-treatment and regret.

Ensemble runs of high-resolution (~10 km; N1280) global climate simulations (2005–2010) with the Met Office HadGEM3 model are analysed over large urban areas in the south-east UK (London) and south-east China (Shanghai, Hangzhou, Nanjing region). With a focus on urban areas, we compare meteorological observations to study the response of modelled surface heat fluxes and screen-level temperatures to urbanisation. HadGEM3 has a simple urban slab scheme with prescribed, globally fixed bulk parameters. Misrepresenting the magnitude or the extent of urban land cover can result in land-surface model bias. As urban land-cover fractions are severely under-estimated in China, this impacts surface heat-flux partitioning and quintessential features such as the urban heat island. Combined with the neglect of anthropogenic heat emissions, this can result in misrepresentation of heat-wave intensities (or cold spells) in cities. The model performance in urban areas could be improved if bulk parameters are modelled instead of prescribed, but this necessitates the availability of local morphology data on a global level. Improving land-cover information and providing more flexible ways to account for differences between cities (e.g. anthropogenic emission; morphology) is essential for realistic future projections of city climates, especially if model output is intended for urban climate services.

OBJECTIVE: Only a handful of case reports exist describing posttraumatic sutural diastasis in the calvarium and none report concurrent temporal bone involvement. We aim to describe diastasis along the temporal bone suture lines in the setting of temporal bone trauma and to identify clinical sequelae. STUDY DESIGN: Retrospective case review. SETTING: Tertiary Level 1 trauma center. PATIENTS: Forty-four patients aged 18 and younger who suffered a temporal bone fracture from 2013 to 2018 were identified. Diastasis and diastasis with displacement at the occipitomastoid, lambdoid, sphenosquamosal and petro-occipital sutures, and synchondroses were determined. MAIN OUTCOME MEASURES: The presence of temporal bone suture and synchondrosal diastasis following temporal bone trauma. Diastasis was defined as sutural separation of a distance greater than 1 mm in comparison to the contralateral side. RESULTS: Using our diastasis diagnostic criteria, diastasis occurred in 41.5% of temporal bone fractures. Transverse fracture types were significantly associated with diastasis (p ≤ 0.001). Lower Glasgow Coma Scale (GCS) and loss of consciousness (LOC) were associated with the presence of diastasis with displacement and diastasis (p = 0.034 and p = 0.042, respectively). Otic capsule violation was more common in fractures with diastasis but did not reach statistical significance. There were two cases of cerebrospinal fluid otorrhea and three deaths in cases that featured diastasis. CONCLUSION: Our findings indicate that diastasis is a positive predictor for higher disruptive force injuries and more severe outcomes and complications. Posttraumatic temporal bone suture diastasis may represent a separate clinico-pathologic entity in addition to the usual temporal bone fracture classification types.

Objective To determine sound levels resulting from aural suctioning of the external auditory canal. Methods Unweighted decibels (dB) and A‐weighted decibels (dBA) sound pressure level measurements were recorded using a retrotympanic microphone in cadaveric human temporal bones. Sound measurements were made with common otologic suctions, size 3, 5, and 7 French, within the external ear canal at the tympanic membrane, 5, and 10 mm from the tympanic membrane in the dry condition. In the wet condition, the ear canal was filled with fluid and completely suctioned clear to determine sound effects of suctioning liquid from the ear canal. Results Sound levels generated from ear canal suctioning ranged from 68.3 to 97 dB and 62.6 to 95.1 dBA. Otologic suctions positioned closer to the tympanic membrane resulted in louder sound levels, but was not statistically significant (P > .05). Using larger diameter suctions generated louder dB and dBA sound levels (P

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities.

PURPOSE: To design and evaluate an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps with use of synthetic T1-weighted images for MRI relaxation–based contrast augmentation. MATERIALS AND METHODS: This retrospective study included MRI scans acquired between 2016 and 2019 from 100 patients (mean age ± SD, 55 years ± 13; 72 men) across various clinical abnormalities with use of a modified Look-Locker inversion recovery, or MOLLI, sequence to quantify native T1 (T1(native)), postcontrast T1 (T1(post)), and extracellular volume (ECV). Data were divided into training (n = 60) and internal (n = 40) test subsets. “Synthetic” T1-weighted images were generated from the T1 exponential inversion-recovery signal model at a range of optimal inversion times, yielding high blood-myocardium contrast, and were used for contrast-based image augmentation during training and testing of a convolutional neural network for myocardial segmentation. Automated segmentation, T1, and ECV were compared with experts with use of Dice similarity coefficients (DSCs), correlation coefficients, and Bland-Altman analysis. An external test dataset (n = 147) was used to assess model generalization. RESULTS: Internal testing showed high myocardial DSC relative to experts (0.81 ± 0.08), which was similar to interobserver DSC (0.81 ± 0.08). Automated segmental measurements strongly correlated with experts (T1(native), R = 0.87; T1(post), R = 0.91; ECV, R = 0.92), which were similar to interobserver correlation (T1(native), R = 0.86; T1(post), R = 0.94; ECV, R = 0.95). External testing showed strong DSC (0.80 ± 0.09) and T1(native) correlation (R = 0.88) between automatic and expert analysis. CONCLUSION: This deep learning method leveraging synthetic contrast augmentation may provide accurate automated T1 and ECV analysis for cardiac MRI data acquired across different abnormalities, centers, scanners, and T1 sequences. Keywords: MRI, Cardiac, Tissue Characterization, Segmentation, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning Supplemental material is available for this article. © RSNA, 2022

The performance of deep learning for cardiac magnetic resonance imaging (MRI) segmentation is oftentimes degraded when using small datasets and sparse annotations for training or adapting a pre-trained model to previously unseen datasets. Here, we developed and evaluated an approach to addressing some of these issues to facilitate broader use of deep learning for short-axis cardiac MRI segmentation. We developed a globally optimal label fusion (GOLF) algorithm that enforced spatial smoothness to generate consensus segmentation from segmentation predictions provided by a deep learning ensemble algorithm. The GOLF consensus was entered into an uncertainty-guided coupled continuous kernel cut (ugCCKC) algorithm that employed normalized cut, image-grid continuous regularization, and "nesting" and circular shape priors of the left ventricular myocardium (LVM) and cavity (LVC). In addition, the uncertainty measurements derived from the segmentation predictions were used to constrain the similarity of GOLF and final segmentation. We optimized ugCCKC through upper bound relaxation, for which we developed an efficient coupled continuous max-flow algorithm implemented in an iterative manner. We showed that GOLF yielded average symmetric surface distance (ASSD) 0.2-0.8 mm lower than an averaging method with higher or similar Dice similarity coefficient (DSC). We also demonstrated that ugCCKC incorporating the shape priors improved DSC by 0.01-0.05 and reduced ASSD by 0.1-0.9 mm. In addition, we integrated GOLF and ugCCKC into a deep learning ensemble algorithm by refining the segmentation of an unannotated dataset and using the refined segmentation to update the trained models. With the proposed framework, we demonstrated the capability of using relatively small datasets (5-10 subjects) with sparse (5-25% slices labeled) annotations to train a deep learning algorithm, while achieving DSC of 0.871-0.893 for LVM and 0.933-0.959 for LVC on the LVQuan dataset, and these were 0.844-0.871 for LVM and 0.923-0.931 for LVC on the ACDC dataset. Furthermore, we showed that the proposed approach can be adapted to substantially alleviate the domain shift issue. Moreover, we calculated a number of commonly used LV function measurements using the derived segmentation and observed strong correlations (Pearson r=0.77-1.00, p0.001) between algorithm and manual LV function analyses. These results suggest that the developed approaches can be used to facilitate broader application of deep learning in research and clinical cardiac MR imaging workflow.

Despite the benefits of minimally invasive surgery for colorectal procedures, significant disparities in access to these techniques remain. While these gaps have been well-documented for laparoscopy, few studies have evaluated inequalities in access to robotic surgery. We analyze whether disparities exist in the use of robotic surgery in the management of colon cancer. The U.S. National Cancer Database was queried for patients with non-metastatic colon adenocarcinoma who underwent resection with the robotic platform (2010-2016). Demographic, clinicopathologic, and treatment facility-related variables were analyzed with respect to preferential utilization of robotic surgery with multivariable logistic regression. Patients with metastatic disease, missing or incomplete surgical information, and those who underwent local tumor excision were excluded. 74,984 patients were identified, 3001 (4%) of whom underwent robotic surgery. In multivariable analysis, patients who were older, Black, or were living in an urban area had decreased odds of receiving robotic surgery compared with open or laparoscopic surgery. Patients who were privately insured or living in areas with higher education had increased odds of receiving robotic surgery. Robotic surgery was also preferentially associated with lower clinical stage, more recent year of diagnosis, and hospitals with higher procedural volume. As advantages of the robotic platform are becoming better understood, use of this approach is increasing in popularity for treatment of non-metastatic colon cancer. Despite this, significant disparities exist with respect to patient demographics and socioeconomic factors, and access may only be limited to certain types of hospitals. Further studies are needed to define why these inequalities exist.