Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation:a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance

Direct integration of micromachined pipettes in a flow channel for single DNA molecule study by optical tweezers

We have developed a micromachined flow cell consisting of a flow channel integrated with micropipettes. The flow cell is used in combination with an optical trap setup (optical tweezers) to study mechanical and structural properties of -DNA molecules. The flow cell was realized using silicon micromachining including the so-called buried channel technology to fabricate the micropipettes, the wet etching of glass to create the flow channel, and the powder blasting of glass to make the fluid connections. The volume of the flow cell is 2 l. The pipettes have a length of 130 m, a width of 5–10 m, a round opening of 1 m and can be processed with different shapes. Using this flow cell we stretched single molecules ( -DNA) showing typical force-extension curves also found with conventional techniques. These pipettes can be also used for drug delivery, for injection of small gas bubbles into a liquid flow to monitor the streamlines, and for the mixing of liquids to study diffusion effects. The paper describes the design, the fabrication and testing of the flow cell

Barriers, facilitators, and other factors associated with health behaviors in childhood, adolescent, and young adult cancer survivors: A systematic review

\ua9 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.Background: Healthy behaviors are paramount in preventing long-term adverse health outcomes in childhood, adolescent, and young adult (CAYA) cancer survivors. We systematically reviewed and synthesized existing literature on barriers, facilitators, and other factors associated with health behaviors in this population. Methods: MEDLINE and PsycInfo were searched for qualitative and quantitative studies including survivors aged 16–50 years at study, a cancer diagnosis ≤25 years and ≥2 years post diagnosis. Health behaviors included physical activity, smoking, diet, alcohol consumption, sun exposure, and a combination of these behaviors (defined as health behaviors in general). Results: Barriers, facilitators, and other factors reported in ≥2 two studies were considered relevant. Out of 4529 studies, 27 were included (n = 31,905 participants). Physical activity was the most frequently examined behavior (n = 12 studies), followed by smoking (n = 7), diet (n = 7), alcohol (n = 4), sun exposure (n = 4), and health behavior in general (n = 4). Relevant barriers to physical activity were fatigue, lack of motivation, time constraints, and current smoking. Relevant facilitators were perceived health benefits and motivation. Influence of the social environment and poor mental health were associated with more smoking, while increased energy was associated with less smoking. No relevant barriers and facilitators were identified for diet, alcohol consumption, and sun exposure. Barriers to healthy behavior in general were unmet information needs and time constraints whereas lifestyle advice, information, and discussions with a healthcare professional facilitated healthy behavior in general. Concerning other factors, women were more likely to be physically inactive, but less likely to drink alcohol and more likely to comply with sun protection recommendations than men. Higher education was associated with more physical activity, and lower education with more smoking. Conclusion: This knowledge can be used as a starting point to develop health behavior interventions, inform lifestyle coaches, and increase awareness among healthcare providers regarding which survivors are most at risk of unhealthy behaviors

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a multinational observational cohort study and ESICM Trials Group Project

Purpose: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). Methods: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. Results: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. Conclusion: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection