Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines.

OBJECTIVES: Cellular deconvolution is a valuable computational process that can infer the cellular composition of heterogeneous tissue samples from bulk RNA-sequencing data. Benchmark testing is a crucial step in the development and evaluation of new cellular deconvolution algorithms, and also plays a key role in the process of building and optimizing deconvolution pipelines for specific experimental applications. However, few in vivo benchmarking datasets exist, particularly for whole blood, which is the single most profiled human tissue. Here, we describe a unique dataset containing whole blood gene expression profiles and matched circulating leukocyte counts from a large cohort of human donors with utility for benchmarking cellular deconvolution pipelines. DATA DESCRIPTION: To produce this dataset, venous whole blood was sampled from 138 total donors recruited at an academic medical center. Genome-wide expression profiling was subsequently performed via next-generation RNA sequencing, and white blood cell differentials were collected in parallel using flow cytometry. The resultant final dataset contains donor-level expression data for over 45,000 protein coding and non-protein coding genes, as well as matched neutrophil, lymphocyte, monocyte, and eosinophil counts.

Exploring Risk Factors for Predicting 30-Day Postoperative Morbidity in Musculoskeletal Tumor Surgery.

Background: This study investigates the risk factors associated with postoperative complications in musculoskeletal tumor surgeries and evaluates the impact of benchmarking in enhancing surgical outcomes. Methods: Conducted at a tertiary referral center, this retrospective analysis included 196 patients who underwent surgeries for various musculoskeletal tumors, ranging from soft tissue to bone sarcomas. Patient and tumor characteristics, along with surgical interventions and outcomes, were comprehensively assessed using the Charlson Comorbidity Index and the Clavien-Dindo classification. Results: Key findings indicate that surgical reconstruction, ASA 3 status, bone tumor presence, and the need for multiple erythrocyte transfusions significantly increase postoperative morbidity. Notably, no significant correlation was found between the Charlson Comorbidity Index scores and the occurrence or severity of complications, challenging the utility of this index in predicting short-term surgical outcomes. Conclusions: This study highlights the importance of tailored surgical approaches and emphasizes rigorous preoperative assessments to mitigate risks and enhance patient care. Despite its insights, limitations include its retrospective nature and single-center scope, suggesting a need for broader, multicenter studies to generalize findings. Overall, our results underscore the necessity of integrating clinical assessments with benchmarking data to optimize outcomes in the complex field of musculoskeletal tumor surgery.

Prospective benchmarking of an observational analysis in the SWEDEHEART registry against the REDUCE-AMI randomized trial.

Prospective benchmarking of an observational analysis against a randomized trial increases confidence in the benchmarking process as it relies exclusively on aligning the protocol of the trial and the observational analysis, while the trials findings are unavailable. The Randomized Evaluation of Decreased Usage of Betablockers After Myocardial Infarction (REDUCE-AMI, ClinicalTrials.gov ID: NCT03278509) trial started recruitment in September 2017 and results are expected in 2024. REDUCE-AMI aimed to estimate the effect of long-term use of beta blockers on the risk of death and myocardial following a myocardial infarction with preserved left ventricular systolic ejection fraction. We specified the protocol of a target trial as similar as possible to that of REDUCE-AMI, then emulated the target trial using observational data from Swedish healthcare registries. Had everyone followed the treatment strategy as specified in the target trial protocol, the observational analysis estimated a reduction in the 5-year risk of death or myocardial infarction of 0.8 percentage points for beta blockers compared with no beta blockers; effects ranging from an absolute reduction of 4.5 percentage points to an increase of 2.8 percentage points in the risk of death or myocardial infarction were compatible with our data under conventional statistical criteria. Once results of REDUCE-AMI are published, we will compare the results of our observational analysis against those from the trial. If this prospective benchmarking is successful, it supports the credibility of additional analyses using these observational data, which can rapidly deliver answers to questions that could not be answered by the initial trial. If benchmarking proves unsuccessful, we will conduct a "postmortem" analysis to identify the reasons for the discrepancy. Prospective benchmarking shifts the investigator focus away from an endeavour to use observational data to obtain similar results as a completed randomized trial, to a systematic attempt to align the design and analysis of the trial and the observational analysis.

Earned Outcomes Correlate with Reliability-Adjusted Surgical Mortality after Abdominal Aortic Aneurysm Repair and Predict Future Performance.

OBJECTIVES: Cumulative, probability-based metrics are regularly used to measure quality in professional sports, but these methods have not been applied to health care delivery. These techniques have the potential to be particularly useful in describing surgical quality, where case volume is variable and outcomes tend to be dominated by statistical "noise". The established statistical technique used to adjust for differences in case volume is reliability-adjustment which emphasizes statistical "signal"but has several limitations. We sought to validate a novel measure of surgical quality based on earned outcomes methods (deaths above average, DAA) against reliability-adjusted mortality rates, using abdominal aortic aneurysm (AAA) repair outcomes to illustrate the measure's performance. METHODS: Earned outcomes methods were used to calculate the outcome of interest for each patient: deaths above average (DAA). Hospital-level DAA were calculated for non-ruptured open AAA repair and EVAR in the Vascular Quality Initiative (VQI) database from 2016-2019. DAA for each center is the sum of observed - predicted risk of death for each patient; predicted risk of death was calculated using established multivariable logistic regression modeling. Correlations of DAA with reliability-adjusted mortality rates and procedure volume were determined. Because an accurate quality metric should correlate with future results, outcomes from 2016-2017 were used to categorize hospital quality based on (1) risk-adjusted mortality, (2) risk- and reliability-adjusted mortality, and (3) DAA. The best performing quality metric was determined by comparing the ability of these categories to predict 2018-2019 risk-adjusted outcomes. RESULTS: During the study period, 3,734 patients underwent open repair (106 hospitals), and 20,680 patients underwent EVAR (183 hospitals). DAA was closely correlated with reliability-adjusted mortality rates for open repair (r=0.94, P<0.001) and EVAR (r=0.99, P<0.001). DAA also correlated with hospital case volume for open repair (r=-.54, P<0.001), but not EVAR (r=0.07, P=0.3). In 2016-2017, most hospitals had 0% mortality (55% open repair, 57% EVAR), making it impossible to evaluate these hospitals using traditional risk-adjusted mortality rates alone. Further, zero mortality hospitals in 2016-2017 did not demonstrate improved outcomes in 2018-2019 for open repair (3.8% vs 4.6%, P=0.5) or EVAR (0.8% vs 1.0%, P=0.2) compared to all other hospitals. In contrast to traditional risk-adjustment, 2016-2017 DAA evenly divided centers into quality quartiles which predicted 2018-2019 performance with increased mortality rate associated with each decrement in quality quartile (Q1 3.2%, Q2 4.0%, Q3 5.1%, Q4 6.0%). There was a significantly higher risk of mortality at worst quartile open repair hospitals compared to best quartile hospitals (OR 2.01, [95% CI 1.07-3.76], P=0.03). Using 2016-2019 DAA to define quality, highest quality quartile open repair hospitals had lower median DAA compared to lowest quality quartile hospitals (-1.18 DAA vs +1.32 DAA, P<0.001), correlating with lower median reliability-adjusted mortality rates (3.6% vs 5.1%, P<0.001). CONCLUSIONS: Adjustment for differences in hospital volume is essential when measuring hospital-level outcomes. Earned outcomes accurately categorize hospital quality and correlate with reliability-adjustment but are easier to calculate and interpret. From 2016-2019, highest quality open AAA repair hospitals prevented >40 perioperative deaths compared to the average hospital, and >80 perioperative deaths compared to lowest quality hospitals.

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

United Kingdom oral and maxillofacial surgeons support changes to current specialty recruitment process, which could include 'Walport' style local selection and national benchmarking.

Recruitment to oral and maxillofacial Surgical (OMFS) specialty training was centralised in 2010. The 'flexibility' for OMFS to respond to specialty specific recruitment issues is reducing and many Specialty Trainees' (ST) posts are left unfilled. The National Institute for Health and Care Research (NIHR) appointment process designed to address the problem of recruiting and appointing academic surgeons with local selection with national benchmarking has worked. Using a database of all UK OMFS consultants/trainees, an electronic questionnaire was shared by e-mail, WhatsApp, and other social media. Of 306 replies, 125 (41%) were Consultants/post-certificate of completion training (CCT) individuals, 66 (22%) ST, 61 (20%) second degree students, 27 (9%) pre-second degree, 26 (9%) dual degree pre-ST trainees, and one did not indicate their status. A total of 249 (76%) studied dentistry first and 230 (75%) were male. Of those replying, 147 (48%) had no direct experience of national selection. 120 (39%) had experience as a candidate, 20 (7%) as a selector only, 17 (6%) as a candidate and selector, and two did not record their experience. Of 250 expressing an opinion, 156 (62%) supported local selection with 140 (56%) supporting local selection and national benchmarking, which is a process used for research training posts by the NIHR. Geographical continuity was most important for 78% of pre-second-degree trainees, 45% of STs, and 54% of second-degree students. A total of 57 respondents completed free text comments. There is support for changes in OMFS ST selection including creating OMFS posts which include Foundation and second-degree training in NIHR style locally recruited nationally benchmarked posts.

Utilizing Data Analytics And Business Intelligence Tools In Laboratory Workflow.

A business intelligence (BI) tool in a laboratory workflow offers various benefits, including data consolidation, real-time monitoring, process optimization, cost analysis, performance benchmarking (quality indicators), predictive analytics, compliance reporting, and decision support. These tools improve operational efficiency, quality control, inventory management, cost analysis, and clinical decision-making. This write up reveals the workflow process and implementation of BI in a private hospital laboratory. By identifying challenges and overcoming them, laboratories can utilize the power of BI and analytics solutions to accelerate healthcare performance, lower costs, and improve care quality. We used navify (Viewics) as a BI platform which relies on an infinity data warehouse for analytics and dashboards. We applied it for pre-analytic, analytic and post-analytic phases in laboratory. We conclude, digitalization is crucial for innovation and competitiveness, enhancing productivity, efficiency, and flexibility in future laboratories.

Reducing suicides in mental healthcare: results from a 4-year follow-up implementation study in the Netherlands (SUPRANET).

Objective: In 2016, the SUicide PRevention Action NETwork (SUPRANET) was launched. The SUPRANET intervention aims at better implementing the suicide prevention guideline. An implementation study was developed to evaluate the impact of SUPRANET over time on three outcomes: 1) suicides, 2) registration of suicide attempts, and 3) professionals' knowledge and adherence to the guideline. Methods: This study included 13 institutions, and used an uncontrolled longitudinal prospective design, collecting biannual data on a 2-level structure (institutional and team level). Suicides and suicide attempts were extracted from data systems. Professionals' knowledge and adherence were measured using a self-report questionnaire. A three-step interrupted time series analysis (ITSA) was performed for the first two outcomes. Step 1 assessed whether institutions executed the SUPRANET intervention as intended. Step 2 examined if institutions complied with the four guideline recommendations. Based on steps 1 and 2, institutions were classified as below or above average and after that, included as moderators in step 3 to examine the effect of SUPRANET over time compared to the baseline. The third outcome was analyzed with a longitudinal multilevel regression analysis, and tested for moderation. Results: After institutions were labeled based on their efforts and investments made (below average vs above average), we found no statistically significant difference in suicides (standardized mortality ratio) between the two groups relative to the baseline. Institutions labeled as above average did register significantly more suicide attempts directly after the start of the intervention (78.8 per 100,000 patients, p<0.001, 95%CI=(51.3 per 100,000, 106.4 per 100,000)), and as the study progressed, they continued to report a significantly greater improvement in the number of registered attempts compared with institutions assigned as below average (8.7 per 100,000 patients per half year, p=0.004, 95%CI=(3.3 per 100,000, 14.1 per 100,000)). Professionals working at institutions that invested more in the SUPRANET activities adhered significantly better to the guideline over time (b=1.39, 95%CI=(0.12,2.65), p=0.032). Conclusion: Institutions labeled as above average registered significantly more suicide attempts and also better adhered to the guideline compared with institutions that had performed less well. Although no convincing intervention effect on suicides was found within the study period, we do think that this network is potentially able to reduce suicides. Continuous investments and fully implementing as many guideline recommendations as possible are essential to achieve the biggest drop in suicides.

The evolving regulatory system of advanced therapy medicinal products in China: a documentary analysis using the World Health Organization Global Benchmarking Tool standards.

Advanced therapy medicinal products (ATMPs) are rapidly evolving to offer new treatment options. The scientific, technical, and clinical complexities subject drug regulatory authorizes to regulatory challenges. To advance the regulatory capacity for ATMPs, the National Medical Products Administration in China made changes to the drug regulatory system and developed regulatory science with the goal of addressing patient needs and encouraging innovation. This study aimed to systematically identify the regulatory evidence on ATMPs in China under the guidance of an overarching framework from the World Health Organization Global Benchmarking Tool. It was found that China's administrative authorities at all levels have issued a number of policy documents to promote the development of ATMPs, covering biopharmaceutical products research and development (n = 14), biopharmaceutical industry development (n = 9), high-quality development of medical institutions (n = 1), specific development plans/projects (n = 6) and specific regional development (n = 4). The legal and regulatory framework of ATMPs in China has been established and is subject to continuous adjustment in various aspects including regulations (n = 3), departmental rules or administrative normative documents (n = 22), and technical guidance (n = 15). As the regulatory reform continues, the drug review processes have been revised, and various technical standards have been launched, which aim to establish a regulatory approach that oversees the full life-cycle development of ATMPs in the country. The limited number of investigational new drug applications and approved ATMPs suggests a lag remains between the translation of advanced therapeutic technologies into clinically available medical products. To accelerate the translational research of ATMP in countries such as China, developing and adopting real-world evidence generated from clinical use in designated healthcare facilities to support scientific decision-making in ATMP regulation is warranted. The enhancement of regulatory capacity building and multi-stakeholder collaborations should also be encouraged to facilitate the timely evaluation of promising ATMPs to meet more patient needs.

Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions.

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (nProfile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth-resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo-climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo-climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo-climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process-oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo-climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

Opportunities for Consistent and Holistic Metrics to Support Food Systems Transformation: A Summary of a Symposium Presented at Nutrition 2023.

There is an urgent need for global food systems transformation to realize a future where planetary health reaches its full potential. Paramount to this vision is the ability of stakeholders across sectors to understand how foods and dietary patterns impact food systems inclusive of all domains of sustainability-environmental, nutrition/health, economic and social. This article is a synopsis of presentations by 3 food systems experts to share the latest science in a session entitled "How do you measure sustainability? Opportunities for consistent and holistic metrics to support food systems transformation" at the American Society for Nutrition's 2023 annual conference. As summarized here, global population data showing widespread malnutrition underscore the important role of dietary diversity through a balance of plant- and animal-source foods to achieve nutritionally adequate diets and reduce risk of noncommunicable diseases. Yet, recent international audits of countries, companies, and organizations and their sustainability targets largely demonstrate an underrepresentation of robust nutrition/health metrics to support public nutrition and health progress. Addressing limitations in diet-sustainability modeling systems provides a viable opportunity to accurately reflect the important contributions and trade-offs of diets across all domains of sustainability to ultimately support evidence-based decision making in advancing healthy food systems.

Antipsychotic drugs in first-episode psychosis: A target trial emulation in the FEP-CAUSAL Collaboration.

Good adherence to antipsychotic therapy helps prevent relapses in First Episode Psychosis (FEP). We used data from the FEP-CAUSAL Collaboration, an international consortium of observational cohorts to emulate a target trial comparing antipsychotics with treatment discontinuation as the primary outcome. Other outcomes included all-cause hospitalization. We benchmarked our results to estimates from EUFEST, a randomized trial conducted in the 2000s. We included 1097 patients with a psychotic disorder and less than 2 years since psychosis onset. Inverse probability weighting was used to control for confounding. The estimated 12-month risks of discontinuation for aripiprazole, first-generation agents, olanzapine, paliperidone, quetiapine, and risperidone (95% CI) were: 61.5% (52.5-70.6), 73.5% (60.5-84.9), 76.8% (67.2-85.3), 58.4% (40.4-77.4), 76.5% (62.1-88.5), and 74.4% (67.0-81.2) respectively. Compared with aripiprazole, the 12-month risk differences (95% CI) were -15.3% (-30.0, 0.0) for olanzapine, -12.8% (-25.7, -1.0) for risperidone, and 3.0% (-21.5, 30.8) for paliperidone. The 12-month risks of hospitalization were similar between agents. Our estimates support use of aripiprazole and paliperidone as first-line therapies for FEP. Benchmarking yielded similar results for discontinuation and absolute risks of hospitalization as in the original trial, suggesting that data from the FEP-CAUSAL Collaboration data sufficed to approximately remove confounding for these clinical questions.

A cross-sectional survey of patient perceptions of the National Neurosurgical Audit Programme (NNAP).

BACKGROUND: The National Neurosurgical Audit Programme (NNAP) publishes mortality outcomes of consultants and neurosurgical units across the United Kingdom. It is unclear how useful outcomes data is for patients and whether it influences their decision-making process. Our aim was to identify patients' perceptions and understanding of the NNAP data and its influences. MATERIALS AND METHODS: This single-centre study was conducted in the outpatient neurosurgery clinics at a regional neurosurgical centre. All adult (age ≥ 18) neurosurgical patients, with capacity, were invited to take part. Native and non-native English speakers were eligible. Statistical analyses were performed on SPSS v28 (IBM). Ethical approval was obtained. RESULTS: A total of 84 responses were received (54.7% females). Over half (51.0%) of respondents felt that they understood a consultant's mortality outcomes. Educational level determines respondents' understanding (χ2(8) = 16.870; p = .031). Most respondents were unaware of the NNAP (89.0%). Only a third of respondents (35.1%) understood the funnel plot used to illustrate mortality. CONCLUSIONS: Most patients were unaware of the NNAP and most did not understand the data on the website. Understanding of mortality data seemed to be related to respondents' educational level which would be important to keep in mind when planning how to depict mortality data.

A consensus-based tool for capability benchmarking of emergency medical services in South Africa.

Introduction: Standards for Emergency Medical Services [EMS] have recently been introduced in South Africa in a movement towards the promotion of quality improvement. While these standards identify a minimum set of criteria for EMS quality they do not differentiate between services just meeting them and those exceeding them. Benchmarking may be a helpful exercise in beginning to address the question of comparative levels of capability in EMS beyond a set of minimum standards. The aim of this study was to develop a consensus-based capability benchmarking tool for EMS organizations within the South African context. Methods: A total of 12 experts in the field of EMS in South Africa consented to participate in two Delphi Surveys in order to achieve consensus on the core components of an EMS organization as well as relevant level descriptors for those components. The resulting data was used to develop a consensus-based capability benchmarking tool for EMS organizations in South Africa. Results: A consensus-based capability benchmarking tool was developed that allows organizations to distinguish whether the organization's capability, as a whole, is underdeveloped, developing, or well-developed. This is in addition to identifying how capable they are in all individual components or sub-components. Conclusion: It is recommended that further research be conducted to assess this tool's implementation within different EMS organizations in South Africa, and that this study is used as a stepping-stone for additional research into meaningful quality improvement in emergency medical services in South Africa.

The Getting It right First Time (GIRFT) programme in urology; rationale and methodology.

The Getting It Right First Time (GIRFT) programme is a quality improvement initiative covering the National Health Service in England. The programme aims to standardise clinical practices and improve patient and system level outcomes by utilising data-driven insights and clinically-led recommendations. There are GIRFT workstreams for every medical and surgical specialty, including urology. Defining features of the GIRFT methodology are that it is clinically led by experienced clinicians, data-driven, and specialty specific. Each specialty workstream conducts deep-dive visits to every hospital, analysing performance data and engaging with clinicians and management to identify and share improvement priorities. For urology, GIRFT has completed deep-dive visits and published reports outlining priority areas for development. Reports include recommendations pertaining to streamlining care pathways, reducing the acuity of care environments, enhancing emergency services, optimising utilisation of outpatient services, and workforce training and utilisation. The GIRFT academy provides guides for implementing best practices specific to priority areas of care. These include important disease pathways, and GIRFT-advocated innovations such as urology investigation units and urology area networks. GIRFT offers clinical transformation, cost reduction, equity in access to care, and leaner models of care that are often more environmentally sustainable. Evaluation efforts of the programme have focussed on assessing the adoption of GIRFT recommendations, understanding barriers to change, and modelling the climate impact of advocated practices.

Conducting a National RT-QI Project - Challenges and Opportunities.

Over the past decade, there has been an increased interest in defining and monitoring quality indicators (QI) in the field of oncology including the field of radiation oncology. The comprehensive gathering and analysis of QIs on a multicentric scale offer valuable insights into identifying gaps in clinical practice and fostering continuous improvement. This article delineates the evolution and results of the Belgian national project dedicated to radiotherapy-specific QIs while also exploring the challenges and opportunities inherent in implementing such a multi-centric initiative.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.