DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT

A systematic review of interventions to improve knowledge and self-management skills concerning contraception, pregnancy and breastfeeding in people with rheumatoid arthritis

This systematic review aimed to determine the effectiveness of interventions for improving knowledge and/or self-management skills concerning contraception, pregnancy and breastfeeding in people with rheumatoid arthritis (RA). We searched four databases (MEDLINE, CINAHL, Cochrane Trials, PsycINFO) using a comprehensive search strategy. Studies were eligible if they were prospective, published in English from 2004 to 2015, included participants with RA and tested an intervention designed to improve knowledge and/or self-management skills relating to family planning, pregnancy or breastfeeding. As no studies met the latter criterion, the search strategy was expanded to include all prospective studies evaluating RA educational and/or self-management interventions. Data on study characteristics, participant characteristics and programme content were extracted to summarise the evidence base for interventions to support people with RA during their reproductive years. Expanded literature searches identified 2290 papers, of which 68 were eligible. Of these, nine papers (13 %) specifically excluded pregnant women/breastfeeding mothers or recruited only older people.Only one study (1 %) explicitly evaluated pregnancy-focused education via a motherhood decision aid, while eight studies (12 %) incorporated relevant (albeit minor) components within broader RA educational or self-management interventions. Of these, three studies provided methotrexate education in relation to conception/pregnancy/breastfeeding; three incorporated discussions on RA and relationships, impact of RA on the family or sexual advice; one provided information regarding contraception and fertility; and one issued a warning regarding use of biologic therapy in pregnancy/breastfeeding. In conclusion, information regarding family planning, pregnancy or breastfeeding represents a negligible part of published RA educational interventions, with scope to develop targeted resources

Normal and abnormal development of the aortic wall and valve: correlation with clinical entities

Dilation of the wall of the thoracic aorta can be found in patients with a tricuspid (TAV) as well as a bicuspid aortic valve (BAV) with and without a syndromic component. BAV is the most common congenital cardiovascular malformation, with a population prevalence of 0.5–2 %. The clinical course is often characterised by aneurysm formation and in some cases dissection. The non-dilated aortic wall is less well differentiated in all BAV as compared with TAV, thereby conferring inherent developmental susceptibility. Furthermore, a turbulent flow, caused by the inappropriate opening of the bicuspid valve, could accelerate the degenerative process in the aortic wall. However, not all patients with bicuspidy develop clinical complications during their life. We postulate that the increased vulnerability for aortic complications in a subset of patients with BAV is caused by a defect in the early development of the aorta and aortic valve. This review discusses histological and molecular genetic aspects of the normal and abnormal development of the aortic wall and semilunar valves. Aortopathy associated with BAV could be the result of a shared developmental defect during embryogenesis

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT