A literature-based treatment algorithm for low-grade neuroendocrine liver metastases

BackgroundThe optimal timing of treatment of liver metastases from low-grade neuroendocrine tumors (LG-NELM) varies significantly due to numerous treatment modalities and the literature supporting various treatment(s). This study sought to create and validate a literature-based treatment algorithm for LG-NELM.MethodsA treatment algorithm to maximize overall survival (OS) was designed using peer-reviewed articles evaluating treatment of LG-NELM. This algorithm was retrospectively applied to patients treated for LG-NELM at our institution. Deviation was determined based on whether or not a patient received treatment consistent with that recommended by the algorithm. Patients who did and did not deviate from the algorithm were compared with respect to OS and number of treatments.ResultsApplying our algorithm to a 149-patient cohort, 57 (38%) deviated from recommended treatment. Deviation occurred in the form of alternative (28, 49%) versus additional procedures (29, 51%). Algorithm deviators underwent significantly more procedures than non-deviators (median 1 vs. 2, p < 0.001). Cox model indicated no difference in OS associated with algorithm deviation (HR 1.19, p = 0.58) when controlling for age and tumor characteristics.ConclusionThis literature-based algorithm helps standardize treatment protocols in patients with LG-NELM and can reduce cost and risk by minimizing unnecessary procedures. Prospective implementation and validation is required.     

EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer—2020 Update. Part 1: Screening,Diagnosis, and Local Treatment with Curative Intent

ObjectiveTo present a summary of the 2020 version of the European Association of Urology (EAU)-European Association of Nuclear Medicine (EANM)-European Society for Radiotherapy and Oncology (ESTRO)-European Society of Urogenital Radiology (ESUR)-International Society of Geriatric Oncology (SIOG) guidelines on screening, diagnosis, and local treatment of clinically localised prostate cancer (PCa).Evidence acquisitionThe panel performed a literature review of new data, covering the time frame between 2016 and 2020. The guidelines were updated and a strength rating for each recommendation was added based on a systematic review of the evidence.Evidence synthesisA risk-adapted strategy for identifying men who may develop PCa is advised, generally commencing at 50 yr of age and based on individualised life expectancy. Risk-adapted screening should be offered to men at increased risk from the age of 45 yr and to breast cancer susceptibility gene (BRCA) mutation carriers, who have been confirmed to be at risk of early and aggressive disease (mainly BRAC2), from around 40 yr of age. The use of multiparametric magnetic resonance imaging in order to avoid unnecessary biopsies is recommended. When a biopsy is performed, a combination of targeted and systematic biopsies must be offered. There is currently no place for the routine use of tissue-based biomarkers. Whilst prostate-specific membrane antigen positron emission tomography computed tomography is the most sensitive staging procedure, the lack of outcome benefit remains a major limitation. Active surveillance (AS) should always be discussed with low-risk patients, as well as with selected intermediate-risk patients with favourable International Society of Urological Pathology (ISUP) 2 lesions. Local therapies are addressed, as well as the AS journey and the management of persistent prostate-specific antigen after surgery. A strong recommendation to consider moderate hypofractionation in intermediate-risk patients is provided. Patients with cN1 PCa should be offered a local treatment combined with long-term hormonal treatment.ConclusionsThe evidence in the field of diagnosis, staging, and treatment of localised PCa is evolving rapidly. The 2020 EAU-EANM-ESTRO-ESUR-SIOG guidelines on PCa summarise the most recent findings and advice for their use in clinical practice. These PCa guidelines reflect the multidisciplinary nature of PCa management.Patient summaryUpdated prostate cancer guidelines are presented, addressing screening, diagnosis, and local treatment with curative intent. These guidelines rely on the available scientific evidence, and new insights will need to be considered and included on a regular basis. In some cases, the supporting evidence for new treatment options is not yet strong enough to provide a recommendation, which is why continuous updating is important. Patients must be fully informed of all relevant options and, together with their treating physicians, decide on the most optimal management for them.     

Position paper on stress cardiac MRI in chronic coronary syndrome: Endorsed by the Société Française de Radiologie (SFR) the Société Française d’Imagerie CardioVasculaire (SFICV) and the Société Française de Cardiologie (SFC)

This position paper was intended to update the former consensus between the French Societies of Radiology and Cardiology about the use of stress cardiac magnetic resonance imaging (MRI) in chronic coronary syndrome published in 2009. The Delphi method was used to build the present consensus. This expert panel consensus includes recommendations for indications, procedure with patient preparation, stress inducing drugs, acquisition protocol, interpretation and risk stratification by stress MRI.     

Current status of first-line therapy,anti-angiogenic therapy and its combinations of other agents for unresectable hepatocellular carcinoma

Globally, hepatocellular carcinoma (HCC) is a frequently diagnosed malignancy with rapidly increasing incidence and mortality rates. Unfortunately, many of these patients are diagnosed in the advanced stages when locoregional treatments are not appropriate. Before 2008, no effective drug treatments existed to prolong survival, until the breakthrough multi-tyrosine kinase inhibitor (TKI) sorafenib was developed. It remained the standard treatment option for advanced HCC for 10 years, with a battery of other candidate drugs in clinical trials failing to produce similar efficacy results. In 2018, the REFLECT trial introduced another multi-TKI, lenvatinib, which has non-inferior overall survival compared with sorafenib. Thus, offering patients and their treating physicians two effective treatment options. Recently, immunotherapy-based drugs, such as atezolizumab and bevacizumab, have shown promising results in patients with unresectable HCC. This review summarizes clinical trial and real-world data studies of sorafenib and lenvatinib in patients with unresectable HCC. We offer guidance on the optimal choice between the two treatments and discuss the potential of immunotherapy-based combination; when more data become available, this will likely make the choice between sorafenib and lenvatinib somewhat obsolete.     

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     

The effect of patient ethnicity on the accuracy of peripheral pulse oximetry in patients with COVID-19 pneumonitis: a single-centre,retrospective analysis

Pulse oximetry is used widely to titrate oxygen therapy and for triage in patients who are critically ill. However, there are concerns regarding the accuracy of pulse oximetry in patients with COVID-19 pneumonitis and in patients who have a greater degree of skin pigmentation. We aimed to determine the impact of patient ethnicity on the accuracy of peripheral pulse oximetry in patients who were critically ill with COVID-19 pneumonitis by conducting a retrospective observational study comparing paired measurements of arterial oxygen saturation measured by co-oximetry on arterial blood gas analysis (SaO₂) and the corresponding peripheral oxygenation saturation measured by pulse oximetry (S_pO₂). Bias was calculated as the mean difference between SaO₂ and S_pO₂ measurements and limits of agreement were calculated as bias ±1.96 SD. Data from 194 patients (135 White ethnic origin, 34 Asian ethnic origin, 19 Black ethnic origin and 6 other ethnic origin) were analysed consisting of 6216 paired SaO₂ and S_pO₂ measurements. Bias (limits of agreement) between SaO₂ and S_pO₂ measurements was 0.05% (−2.21–2.30). Patient ethnicity did not alter this to a clinically significant degree: 0.28% (1.79–2.35), −0.33% (−2.47–2.35) and −0.75% (−3.47–1.97) for patients of White, Asian and Black ethnic origin, respectively. In patients with COVID-19 pneumonitis, S_pO₂ measurements showed a level of agreement with SaO₂ values that was in line with previous work, and this was not affected by patient ethnicity.     

A Mathematical Model of a Thermoelectric Device for the Treatment of Whitlow by Local Hypothermia

Biomedical Engineering - A mathematical model of a thermoelectric device for the treatment of whitlow by local hypothermia is discussed. The model is based on a solution of the heat conduction task...