Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE:Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS:We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS:The random forest methodology estimated cellular density with R² = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R² = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS:Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Increased cellular density (CD) is a hallmark of cancer and a key feature in histologic glioma analysis.¹ Mapping cellular density throughout a tumor would be a valuable tool to probe how tumors infiltrate and analyze the transition between diseased and healthy brain. However, measuring CD requires tissue, which entails additional risks and is expensive to obtain. There is no currently accepted clinical algorithm to translate imaging data into quantitative assessments of CD.There is great need for a method to estimate CD noninvasively in human patients with gliomas. In this article, we describe the development of such a method using MR imaging data inputs by correlating with multiple biopsy specimens acquired during a prospective human clinical trial. We obtained comprehensive MR imaging, including conventional, diffusion, perfusion, and permeability imaging sequences. We used machine learning approaches to correlate imaging findings with CD measurements from pathology, devised an algorithm to estimate CD from MR imaging inputs, and generated CD maps for the visual display of the predictions. We identified the most informative imaging data subset. This work has multiple applications in the diagnosis and treatment of patients with gliomas: For example, the method can be used to guide biopsy, resection, and surgery and delineate tumor borderzones both pre- and postoperatively.²     

Optimize the management of urological tube-related emergencies during the coronavirus disease 2019 (COVID-19) pandemic

BackgroundTo introduce and determine the value of optimized strategies for the management of urological tube-related emergencies with increased incidence, complexity and operational risk during the global spread of coronavirus disease 2019 (COVID-19).MethodsAll emergent urological patients at Tongji Hospital, Wuhan, during the period of January 23 (the beginning of lockdown in Wuhan) to March 23, 2020, and the corresponding period in 2019 were recruited to form this study’s COVID-19 group and control group, respectively. Tongji Hospital has the most concentrated and strongest Chinese medical teams to treat the largest number of severe COVID-19 patients. Patients in the control group were routinely treated, while patients in the COVID-19 group were managed following the optimized principles and strategies. The case incidence for each type of tube-related emergency was recorded. Baseline characteristics and management outcomes (surgery time, secondary complex operation rate, readmission rate, COVID-19 infection rate) were analyzed and compared across the control and COVID-19 periods.ResultsThe total emergent urological patients during the COVID-19 period was 42, whereas during the control period, it was 124. The incidence of tube-related emergencies increased from 53% to 88% (P<0.001) during the COVID-19 period. In particular, the incidence of nephrostomy tube-related (31% vs. 15%, P=0.027) and single-J stent-related problems (19% vs. 6%, P=0.009) increased significantly. The mean surgery times across the two periods were comparable. The number of secondary complex operations increased from 12 (18%) to 14 (38%) (P=0.028) during the COVID 19-period. The number of 2-week postoperative readmission decreased from 10 (15%) to 1 (3%) (P=0.049). No participants contracted during the COVID-19 period.ConclusionsUrological tube-related emergencies have been found to have a higher incidence and require more complicated and dangerous operations during the COVID-19 pandemic. However, the optimized management strategies introduced in this study are efficient, and safe for both urologists and patients.     

Timing of elective surgery and risk assessment after SARS-CoV-2 infection: an update

The impact of vaccination and new SARS-CoV-2 variants on peri-operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS-CoV-2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS-CoV-2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS-CoV-2 infection; surgical factors). Asymptomatic SARS-CoV-2 infection with previous variants increased peri-operative mortality risk three-fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS-CoV-2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate-to-severe COVID-19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS-CoV-2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision-making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised.     

Generalisierter Krampfanfall während der Anlage einer axillären Plexusblockade

Die Anaesthesiologie - Eine junge Patientin erleidet während der Anlage einer axillären Plexusblockade einen generalisierten Krampfanfall. Die Mechanismen, im Wesentlichen die vermutlich...