Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 202

Accurate brain-age models for routine clinical MRI examinations

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (&lt; 5 seconds), accurate (mean absolute error [MAE] &lt; 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE &lt; 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p &lt; 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.</p

Becoming a Viking: DNA testing, genetic ancestry and placeholder identity

A consensus has developed among social and biological scientists around the problematic nature of genetic ancestry testing, specifically that its popularity will lead to greater genetic essentialism in social identities. Many of these arguments assume a relatively uncritical engagement with DNA, under ‘highstakes’ conditions. We suggest that in a biosocial society, more pervasive ‘lowstakes’ engagement is more likely. Through qualitative interviews with participants in a study of the genetic legacy of the Vikings in Northern England, we investigate how genetic ancestry results are discursively worked through. The identities formed in ‘becoming a Viking’ through DNA are characterized by fluidity and reflexivity, rather than essentialism. DNA results are woven into a wider narrative of selfhood relating to the past, the value of which lies in its potential to be passed on within families. While not unproblematic, the relatively banal nature of such narratives within contemporary society is characteristic of the ‘biosociable’

Prognostic Utility of Calcium Scoring as an Adjunct to Stress Myocardial Perfusion Scintigraphy in End-Stage Renal Disease

Coronary artery calcium score (CACS) is a strong predictor of adverse cardiovascular events in the general population. Recent data confirm the prognostic utility of single-photon emission computed tomographic (SPECT) imaging in end-stage renal disease, but whether performing CACS as part of hybrid imaging improves risk prediction in this population is unclear. Consecutive patients (n = 284) were identified after referral to a university hospital for cardiovascular risk stratification in assessment for renal transplantation. Participants underwent technetium-99m SPECT imaging after exercise or standard adenosine stress in those unable to achieve 85% maximal heart rate; multislice CACS was also performed (Siemens Symbia T16, Siemens, Erlangen, Germany). Subjects with known coronary artery disease (n = 88) and those who underwent early revascularization (n = 2) were excluded. The primary outcome was a composite of death or first myocardial infarction. An abnormal SPECT perfusion result was seen in 22% (43 of 194) of subjects, whereas 45% (87 of 194) had at least moderate CACS (>100 U). The frequency of abnormal perfusion (summed stress score ≥4) increased with increasing CACS severity (p = 0.049). There were a total of 15 events (8 deaths, and 7 myocardial infarctions) after a median duration of 18 months (maximum follow-up 3.4 years). Univariate analysis showed diabetes mellitus (Hazard ratio [HR] 3.30, 95% CI 1.14 to 9.54; p = 0.028), abnormal perfusion on SPECT (HR 5.32, 95% CI 1.84 to 15.35; p = 0.002), and moderate-to-severe CACS (HR 3.55, 95% CI 1.11 to 11.35; p = 0.032) were all associated with the primary outcome. In a multivariate model, abnormal perfusion on SPECT (HR 4.18, 95% CI 1.43 to 12.27; p = 0.009), but not moderate-to-severe CACS (HR 2.50, 95% CI 0.76 to 8.20; p = 0.130), independently predicted all-cause death or myocardial infarction. The prognostic value of CACS was not incremental to clinical and SPECT perfusion data (global chi-square change = 2.52, p = 0.112). In conclusion, a perfusion defect on SPECT is an independent predictor of adverse outcome in potential renal transplant candidates regardless of the CACS. The use of CACS as an adjunct to SPECT perfusion data does not provide incremental prognostic utility for the prediction of mortality and nonfatal myocardial infarction in end-stage renal disease

Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation

Objective Existing clinical prediction models (CPM) for short-term mortality after transcatheter aortic valve implantation (TAVI) have limited applicability in the UK due to moderate predictive performance and inconsistent recording practices across registries. The aim of this study was to derive a UK-TAVI CPM to predict 30-day mortality risk for benchmarking purposes. Methods A two-step modelling strategy was undertaken: first, data from the UK-TAVI Registry between 2009 and 2014 were used to develop a multivariable logistic regression CPM using backwards stepwise regression. Second, model-updating techniques were applied using the 2013–2014 data, thereby leveraging new approaches to include frailty and to ensure the model was reflective of contemporary practice. Internal validation was performed by bootstrapping to estimate in-sample optimism-corrected performance. Results Between 2009 and 2014, up to 6339 patients were included across 34 centres in the UK-TAVI Registry (mean age, 81.3; 2927 female (46.2%)). The observed 30-day mortality rate was 5.14%. The final UK-TAVI CPM included 15 risk factors, which included two variables associated with frailty. After correction for in-sample optimism, the model was well calibrated, with a calibration intercept of 0.02 (95% CI −0.17 to 0.20) and calibration slope of 0.79 (95% CI 0.55 to 1.03). The area under the receiver operating characteristic curve, after adjustment for in-sample optimism, was 0.66. Conclusion The UK-TAVI CPM demonstrated strong calibration and moderate discrimination in UK-TAVI patients. This model shows potential for benchmarking, but even the inclusion of frailty did not overcome the need for more wide-ranging data and other outcomes might usefully be explored

Writing in Britain and Ireland, c. 400 to c. 800

No abstract available

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research