Assessment of heart-substructures auto-contouring accuracy for application in heart-sparing radiotherapy for lung cancer.

Objectives: We validated an auto-contouring algorithm for heart substructures in lung cancer patients, aiming to establish its accuracy and reliability for radiotherapy (RT) planning. We focus on contouring an amalgamated set of subregions in the base of the heart considered to be a new organ at risk, the cardiac avoidance area (CAA), to enable maximum dose limit implementation in lung RT planning. Methods: The study validates a deep-learning model specifically adapted for auto-contouring the CAA (which includes the right atrium, aortic valve root, and proximal segments of the left and right coronary arteries). Geometric, dosimetric, quantitative, and qualitative validation measures are reported. Comparison with manual contours, including assessment of interobserver variability, and robustness testing over 198 cases are also conducted. Results: Geometric validation shows that auto-contouring performance lies within the expected range of manual observer variability despite being slightly poorer than the average of manual observers (mean surface distance for CAA of 1.6 vs 1.2 mm, dice similarity coefficient of 0.86 vs 0.88). Dosimetric validation demonstrates consistency between plans optimized using auto-contours and manual contours. Robustness testing confirms acceptable contours in all cases, with 80% rated as "Good" and the remaining 20% as "Useful." Conclusions: The auto-contouring algorithm for heart substructures in lung cancer patients demonstrates acceptable and comparable performance to human observers. Advances in knowledge: Accurate and reliable auto-contouring results for the CAA facilitate the implementation of a maximum dose limit to this region in lung RT planning, which has now been introduced in the routine setting at our institution.

Treatment Response Biomarkers: Working Toward Personalized Radiotherapy for Lung Cancer.

Owing to major advances in the field of radiation oncology, patients with lung cancer can now receive technically individualized radiotherapy treatments. Nevertheless, in the era of precision oncology, radiotherapy-based treatment selection needs to be improved as many patients do not benefit or are not offered optimum therapies. Cost-effective robust biomarkers can address this knowledge gap and lead to individuals being offered more bespoke treatments leading to improved outcome. This narrative review discusses some of the current achievements and challenges in the realization of personalized radiotherapy delivery in patients with lung cancer.

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data.

Objective.As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan.Approach.Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.Results.For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans.Significance.The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.

Association of follow-up imaging frequency with temporal incidence and patterns of distant failure following (chemo) radiotherapy for HPV related oropharyngeal cancer.

OBJECTIVES: Emerging data supports radical intent therapy for oligometastatic (OM) relapsed human papilloma virus (HPV+) related oropharyngeal cancer (OPC). We assess the association of follow-up imaging frequency amongst HPV + OPC, with temporal and spatial patterns of distant relapse, to inform rationalisation of routine post-treatment imaging. MATERIALS AND METHODS: A retrospective single centre cohort study was carried out of consecutive HPV + OPC patients treated with radical intent (chemo)radiotherapy ((CT)RT) between 2011 and 2019. OM state was defined as ≤ 5 metastasis, none larger than 3 cm (OMs) or, if interval from last negative surveillance imaging > 6-months, then ≤ 10 metastasis, none larger than 5 cm, (OMp). Patients not meeting OMs / OMp criteria were deemed to have incurable diffuse metastatic disease (DMdiffuse). RESULTS: 793 HPV-OPC patients were identified with median follow-up 3.15years (range 0.2-8.9). 52 (6.6 %) patients had radiologically identified DM at first failure and were considered for analysis. The median time to recurrence was 15.1 months (range: 2.6-63 months). 87 % of distant metastasis (DM) occurred in the first two years after treatment. Twenty-seven (52 %) patients had OM (OMs or OMp) at time of failure, with 31 % having OMs. The median time from completion of treatment to diagnosis of DMdiffuse vs OM was 22.2 months (range: 2.6-63.1 months) vs 11.6 months (range: 3.5-32.5 months). The probability of being diagnosed with OM vs DMdiffuse increased with reducing interval from last negative surveillance scan to imaging identifying DM (≤6 months 88.9 %, 7-12 months 71.4 %, 13-24 months 35 %, > 24 months 22.2 %). CONCLUSION: We demonstrate that a reduced interval between last negative imaging and subsequent radiological diagnosis of DM is associated with increased likelihood of identification of OM disease. Consideration of increased frequency of surveillance imaging during the first two years of follow up is supported, particularly for patients at high risk of distant failure.

Connexin-43 hemichannels orchestrate NOD-like receptor protein-3 (NLRP3) inflammasome activation and sterile inflammation in tubular injury.

BACKGROUND: Without a viable cure, chronic kidney disease is a global health concern. Inflammatory damage in and around the renal tubules dictates disease severity and is contributed to by multiple cell types. Activated in response to danger associated molecular patterns (DAMPs) including ATP, the NOD-like receptor protein-3 (NLRP3) inflammasome is integral to this inflammation. In vivo, we have previously observed that increased expression of Connexin 43 (Cx43) is linked to inflammation in chronic kidney disease (CKD) whilst in vitro studies in human proximal tubule cells highlight a role for aberrant Cx43 hemichannel mediated ATP release in tubule injury. A role for Cx43 hemichannels in priming and activation of the NLRP3 inflammasome in tubule epithelial cells remains to be determined. METHODS: Using the Nephroseq database, analysis of unpublished transcriptomic data, examined gene expression and correlation in human CKD. The unilateral ureteral obstruction (UUO) mouse model was combined with genetic (tubule-specific Cx43 knockout) and specific pharmacological blockade of Cx43 (Peptide5), to explore a role for Cx43-hemichannels in tubule damage. Human primary tubule epithelial cells were used as an in vitro model of CKD. RESULTS: Increased Cx43 and NLRP3 expression correlates with declining glomerular filtration rate and increased proteinuria in biopsies isolated from patients with CKD. Connexin 43-tubule deletion prior to UUO protected against tubular injury, increased expression of proinflammatory molecules, and significantly reduced NLRP3 expression and downstream signalling mediators. Accompanied by a reduction in F4/80 macrophages and fibroblast specific protein (FSP1+) fibroblasts, Cx43 specific hemichannel blocker Peptide5 conferred similar protection in UUO mice. In vitro, Peptide5 determined that increased Cx43-hemichannel activity primes and activates the NLRP3 inflammasome via ATP-P2X7 receptor signalling culminating in increased secretion of chemokines and cytokines, each of which are elevated in individuals with CKD. Inhibition of NLRP3 and caspase 1 similarly decreased markers of tubular injury, whilst preventing the perpetual increase in Cx43-hemichannel activity. CONCLUSION: Aberrant Cx43-hemichannel activity in kidney tubule cells contributes to tubule inflammation via activation of the NLRP3 inflammasome and downstream paracrine mediated cell signalling. Use of hemichannel blockers in targeting Cx43-hemichannels is an attractive future therapeutic target to slow or prevent disease progression in CKD. Video Abstract.

Cardiac Function Modifies the Impact of Heart Base Dose on Survival: A Voxel-Wise Analysis of Patients With Lung Cancer From the PET-Plan Trial.

INTRODUCTION: Heart dose has emerged as an independent predictor of overall survival in patients with NSCLC treated with radiotherapy. Several studies have identified the base of the heart as a region of enhanced dose sensitivity and a potential target for cardiac sparing. We present a dosimetric analysis of overall survival in the multicenter, randomized PET-Plan trial (NCT00697333) and for the first time include left ventricular ejection fraction (EF) at baseline as a metric of cardiac function. METHODS: A total of 205 patients with inoperable stage II or III NSCLC treated with 60 to 72 Gy in 2 Gy fractions were included in this study. A voxel-wise image-based data mining methodology was used to identify anatomical regions where higher dose was significantly associated with worse overall survival. Univariable and multivariable Cox proportional hazards models tested the association of survival with dose to the identified region, established prognostic factors, and baseline cardiac function. RESULTS: A total of 172 patients remained after processing and censoring for follow-up. At 2-years posttreatment, a highly significant region was identified within the base of the heart (p < 0.005), centered on the origin of the left coronary artery and the region of the atrioventricular node. In multivariable analysis, the number of positron emission tomography-positive nodes (p = 0.02, hazard ratio = 1.13, 95% confidence interval: 1.02-1.25) and mean dose to the cardiac subregion (p = 0.02, hazard ratio = 1.11 Gy-1, 95% confidence interval: 1.02-1.21) were significantly associated with overall survival. There was a significant interaction between EF and region dose (p = 0.04) for survival, with contrast plots revealing a larger effect of region dose on survival in patients with lower EF values. CONCLUSIONS: This work validates previous image-based data mining studies by revealing a strong association between dose to the base of the heart and overall survival. For the first time, an interaction between baseline cardiac health and heart base dose was identified, potentially suggesting preexisting cardiac dysfunction exacerbates the impact of heart dose on survival.

Larynx cancer survival model developed through open-source federated learning.

INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.

Bayesian methods provide a practical real-world evidence framework for evaluating the impact of changes in radiotherapy.

PURPOSE: Retrospective studies have identified a link between the average set-up error of lung cancer patients treated with image-guided radiotherapy (IGRT) and survival. The IGRT protocol was subsequently changed to reduce the action threshold. In this study, we use a Bayesian approach to evaluate the clinical impact of this change to practice using routine 'real-world' patient data. METHODS AND MATERIALS: Two cohorts of NSCLC patients treated with IGRT were compared: pre-protocol change (N = 780, 5 mm action threshold) and post-protocol change (N = 411, 2 mm action threshold). Survival models were fitted to each cohort and changes in the hazard ratios (HR) associated with residual set-up errors was assessed. The influence of using an uninformative and a skeptical prior in the model was investigated. RESULTS: Following the reduction of the action threshold, the HR for residual set-up error towards the heart was reduced by up to 10%. Median patient survival increased for patients with set-up errors towards the heart, and remained similar for patients with set-up errors away from the heart. Depending on the prior used, a residual hazard ratio may remain. CONCLUSIONS: Our analysis found a reduced hazard of death and increased survival for patients with residual set-up errors towards versus away from the heart post-protocol change. This study demonstrates the value of a Bayesian approach in the assessment of technical changes in radiotherapy practice and supports the consideration of adopting this approach in further prospective evaluations of changes to clinical practice.

Role of Real-World Data in Assessing Cardiac Toxicity After Lung Cancer Radiotherapy.

Radiation-induced heart disease (RIHD) is a recent concern in patients with lung cancer after being treated with radiotherapy. Most of information we have in the field of cardiac toxicity comes from studies utilizing real-world data (RWD) as randomized controlled trials (RCTs) are generally not practical in this field. This article is a narrative review of the literature using RWD to study RIHD in patients with lung cancer following radiotherapy, summarizing heart dosimetric factors associated with outcome, strength, and limitations of the RWD studies, and how RWD can be used to assess a change to cardiac dose constraints.

Iodinated Contrast Media Extravasation Injuries: Should Osmolality and the Pattern of Distribution Affect Management?: A Literature Review and Case Report Comparison.

Background: Contrast media extravasation injuries are uncommon, and both conservative and surgical management approaches have been previously described. Over time the use of lower osmolar contrast solutions has prompted fewer complications, whereas the use of automated infusion systems has increased the overall incidence. Local radiology departments frequently have their own protocols for the initial management of extravasation injuries, but if the injury is considered more severe or results in soft tissue compromise, the plastic surgery department is often consulted. Surgical management options depend on the nature of the agent and the degree of extravasation. Stab incisions of the overlying skin followed by the application of pressure have been described for injuries which are more severe. Methods: Two cases were compared in the context of the prevailing literature. One of these was a large volume extravasation of an iodine-based imaging contrast agent with a diffuse distribution pattern, rendering it unsuitable for this method of evacuation. This is contrasted with a case with a more discrete collection better suited to acute evacuation. Results: This review found that current literature does not account for distribution patterns of extravasation medium in the decision-making process around surgical intervention. Conclusions: A review of the relevant literature suggests that the pattern of distribution should be accounted for when considering surgical management.

Clinical and radiomics prediction of complete response in rectal cancer pre-chemoradiotherapy.

Background and purpose: Patients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables' ability to predict cCR in patients pre chemoradiotherapy. Materials and methods: Using the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced. Results: Using Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69-0.83) in the training set and 0.68 (95% CI 0.57-0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66-0.80) and 0.62 (95% CI 0.51-0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61-0.75) and 0.66 (95% CI 0.56-0.77) in the training and validation sets. Conclusion: The predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.

Impact of Introducing Intensity Modulated Radiotherapy on Curative Intent Radiotherapy and Survival for Lung Cancer.

Background: Lung cancer survival remains poor. The introduction of Intensity-Modulated Radiotherapy (IMRT) allows treatment of more complex tumours as it improves conformity around the tumour and greater normal tissue sparing. However, there is limited evidence assessing the clinical impact of IMRT. In this study, we evaluated whether the introduction of IMRT had an influence on the proportion of patients treated with curative-intent radiotherapy over time, and whether this had an effect on patient survival. Materials and Methods: Patients treated with thoracic radiotherapy at our institute between 2005 and 2020 were retrospectively identified and grouped into three time periods: A) 2005-2008 (pre-IMRT), B) 2009-2012 (selective use of IMRT), and C) 2013-2020 (full access to IMRT). Data on performance status (PS), stage, age, gross tumour volume (GTV), planning target volume (PTV) and survival were collected. The proportion of patients treated with a curative dose between these periods was compared. Multivariable survival models were fitted to evaluate the hazard for patients treated in each time period, adjusting for PS, stage, age and tumour volume. Results: 12,499 patients were included in the analysis (n=2675 (A), n=3127 (B), and n=6697 (C)). The proportion of patients treated with curative-intent radiotherapy increased between the 3 time periods, from 38.1% to 50.2% to 65.6% (p<0.001). When stage IV patients were excluded, this increased to 40.1% to 58.1% to 82.9% (p<0.001). This trend was seen across all PS and stages. The GTV size increased across the time periods and PTV size decreased. Patients treated with curative-intent during period C had a survival improvement compared to time period A when adjusting for clinical variables (HR=0.725 (0.632-0.831), p<0.001). Conclusion: IMRT was associated with to more patients receiving curative-intent radiotherapy. In addition, it facilitated the treatment of larger tumours that historically would have been treated palliatively. Despite treating larger, more complex tumours with curative-intent, a survival benefit was seen for patients treated when full access to IMRT was available (2013-2020). This study highlights the impact of IMRT on thoracic oncology practice, accepting that improved survival may also be attributed to a number of other contributing factors, including improvements in staging, other technological radiotherapy advances and changes to systemic treatment.

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy.

INTRODUCTION: Prediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution. METHODS: Open-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions. RESULTS: 1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71-0.76) and 0.70 (0.66-0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise. CONCLUSION: Open-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation.

Causal relation between heart irradiation and survival of lung cancer patients after radiotherapy.

INTRODUCTION: In a recent study, setup uncertainties in the direction of the heart were shown to impact the overall survival of non-small cell lung cancer (NSCLC) patients after radiotherapy, indicating the causal effect between heart irradiation and survival. The current study aims to externally evaluate this observation within a patient cohort treated using daily IGRT. METHOD: NSCLC patients with locally-advanced disease and daily CBCT were included. For all treatment fractions, the distance between the isocenter and the heart was evaluated based on the clinical setup registrations. The variation in heart position between planning and treatment (DeltaDistance) was estimated from these registrations. The possible impact of DeltaDistance on survival was analysed by a multivariable Cox model of overall survival, allowing for a time-dependent impact of DeltaDistance to allow for toxicity latency. RESULTS: Daily CBCT information was available for 489 patients at Odense University Hospital. The primary Cox model contained GTV volume, patient age, performance status, and DeltaDistance. DeltaDistance significantly impacted overall survival approximately 50 months after radiotherapy. Subanalyses indicated that the observed effect is mainly present among the patients with the least clinical risk factors. CONCLUSION: Our results confirm the impact of setup variations in the direction of the heart on the survival of NSCLC patients, even within a cohort using daily CBCT setup guidance. This result indicates a causal effect between heart irradiation and survival. It will be challenging to reduce the setup uncertainty even further; thus, increased focus on dose constraints on the heart seems warranted.

A preclinical radiotherapy dosimetry audit using a realistic 3D printed murine phantom.

Preclinical radiation research lacks standardized dosimetry procedures that provide traceability to a primary standard. Consequently, ensuring accuracy and reproducibility between studies is challenging. Using 3D printed murine phantoms we undertook a dosimetry audit of Xstrahl Small Animal Radiation Research Platforms (SARRPs) installed at 7 UK centres. The geometrically realistic phantom accommodated alanine pellets and Gafchromic EBT3 film for simultaneous measurement of the dose delivered and the dose distribution within a 2D plane, respectively. Two irradiation scenarios were developed: (1) a 10 × 10 mm2 static field targeting the pelvis, and (2) a 5 × 5 mm2 90° arc targeting the brain. For static fields, the absolute difference between the planned dose and alanine measurement across all centres was 4.1 ± 4.3% (mean ± standard deviation), with an overall range of - 2.3 to 10.5%. For arc fields, the difference was - 1.2% ± 6.1%, with a range of - 13.1 to 7.7%. EBT3 dose measurements were greater than alanine by 2.0 ± 2.5% and 3.5 ± 6.0% (mean ± standard deviation) for the static and arc fields, respectively. 2D dose distributions showed discrepancies to the planned dose at the field edges. The audit demonstrates that further work on preclinical radiotherapy quality assurance processes is merited.

Excess years of life lost to COVID-19 and other causes of death by sex, neighbourhood deprivation, and region in England and Wales during 2020: A registry-based study.

BACKGROUND: Deaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups. METHODS AND FINDINGS: We used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording. CONCLUSIONS: In this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients' care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.

Excess deaths from COVID-19 and other causes by region, neighbourhood deprivation level and place of death during the first 30 weeks of the pandemic in England and Wales: A retrospective registry study.

BACKGROUND: Excess deaths during the COVID-19 pandemic compared with those expected from historical trends have been unequally distributed, both geographically and socioeconomically. Not all excess deaths have been directly related to COVID-19 infection. We investigated geographical and socioeconomic patterns in excess deaths for major groups of underlying causes during the pandemic. METHODS: Weekly mortality data from 27/12/2014 to 2/10/2020 for England and Wales were obtained from the Office of National Statistics. Negative binomial regressions were used to model death counts based on pre-pandemic trends for deaths caused directly by COVID-19 (and other respiratory causes) and those caused indirectly by it (cardiovascular disease or diabetes, cancers, and all other indirect causes) over the first 30 weeks of the pandemic (7/3/2020-2/10/2020). FINDINGS: There were 62,321 (95% CI: 58,849 to 65,793) excess deaths in England and Wales in the first 30 weeks of the pandemic. Of these, 46,221 (95% CI: 45,439 to 47,003) were attributable to respiratory causes, including COVID-19, and 16,100 (95% CI: 13,410 to 18,790) to other causes. Rates of all-cause excess mortality ranged from 78 per 100,000 in the South West of England and in Wales to 130 per 100,000 in the West Midlands; and from 93 per 100,000 in the most affluent fifth of areas to 124 per 100,000 in the most deprived. The most deprived areas had the highest rates of death attributable to COVID-19 and other indirect deaths, but there was no socioeconomic gradient for excess deaths from cardiovascular disease/diabetes and cancer. INTERPRETATION: During the first 30 weeks of the COVID-19 pandemic there was significant geographic and socioeconomic variation in excess deaths for respiratory causes, but not for cardiovascular disease, diabetes and cancer. Pandemic recovery plans, including vaccination programmes, should take account of individual characteristics including health, socioeconomic status and place of residence. FUNDING: None.