Implementation of perioperative FLOT compared to ECX/EOX chemotherapy regimens in resectable esophagogastric adenocarcinomas: an analysis of real-world data.

BACKGROUND AND PURPOSE: Perioperative 5-FU, leucovorin, oxaliplatin, and docetaxel (FLOT) is recommended in resectable esophagogastric adenocarcinoma based on randomised trials. However, the effectiveness of FLOT in routine clinical practice remains unknown as randomised trials are subject to selection bias limiting their generalisability. The aim of this study was to evaluate the implementation of FLOT in real-world patients. METHODS: Retrospectively collected data were analysed in consecutive patients treated before or after the implementation of FLOT. The primary endpoint was complete pathological response (pCR) and secondary endpoints were margin-free resection (R0), overall survival (OS), relapse-free survival (RFS) tolerability of chemotherapy and surgical complications. RESULTS: Mean follow-up time for patients treated with FLOT (n = 205) was 37.7 versus 47.0 months for epirubicin, cis- or oxaliplatin, and capecitabine (ECX/EOX, n = 186). Surgical resection was performed in 88.0% versus 92.0%; pCR were observed in 3.8% versus 2.4%; and R0 resections were achieved in 78.0% versus 86.0% (p = 0.03) in the ECX/EOX and FLOT cohorts, respectively. Survival analysis indicated no significant difference in RFS (p = 0.17) or OS (p = 0.37) between the cohorts with a trend towards increased OS in performance status 0 (hazard ratio [HR] = 0.73, 95% confidence interval [CI]: 0.50-1.04). More patients treated with ECX/EOX completed chemotherapy (39% vs. 28%, p = 0.02). Febrile neutropenia was more common in the FLOT cohort (3.8% vs. 11%, p = 0.0086). 90-days mortality (1.2% vs. 0%) and frequency of anastomotic leakage (8% vs. 6%) were equal and low. INTERPRETATION: Patients receiving FLOT did not demonstrate improved pCR, RFS or OS. However, R0 rate was improved and patients in good PS trended towards improved OS.

Synergistic Effects of Warming and Internal Nutrient Loading Interfere with the Long-Term Stability of Lake Restoration and Induce Sudden Re-eutrophication.

Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring data set covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes.

Predicting ecosystem state changes in shallow lakes using an aquatic ecosystem model: Lake Hinge, Denmark, an example.

In recent years, considerable efforts have been made to restore turbid, phytoplankton-dominated shallow lakes to a clear-water state with high coverage of submerged macrophytes. Various dynamic lake models with simplified physical representations of vertical gradients, such as PCLake, have been used to predict external nutrient load thresholds for such nonlinear regime shifts. However, recent observational studies have questioned the concept of regime shifts by emphasizing that gradual changes are more common than sudden shifts. We investigated if regime shifts would be more gradual if the models account for depth-dependent heterogeneity of the system by including the possibility of vertical gradients in the water column and sediment layers for the entire depth. Hence, bifurcation analysis was undertaken using the 1D hydrodynamic model GOTM, accounting for vertical gradients, coupled to the aquatic ecosystem model PCLake, which is implemented in the framework for aquatic biogeochemical modeling (FABM). First, the model was calibrated and validated against a comprehensive data set covering two consecutive 7-yr periods from Lake Hinge, a shallow, eutrophic Danish lake. The autocalibration program Auto-Calibration Python (ACPy) was applied to achieve a more comprehensive adjustment of model parameters. The model simulations showed excellent agreement with observed data for water temperature, total nitrogen, and nitrate and good agreement for ammonium, total phosphorus, phosphate, and chlorophyll a concentrations. Zooplankton and macrophyte coverage were adequately simulated for the purpose of this study, and in general the GOTM-FABM-PCLake model simulations performed well compared with other model studies. In contrast to previous model studies ignoring depth heterogeneity, our bifurcation analysis revealed that the spatial extent and depth limitation of macrophytes as well as phytoplankton chlorophyll-a responded more gradually over time to a reduction in the external phosphorus load, albeit some hysteresis effects still appeared. In a management perspective, our study emphasizes the need to include depth heterogeneity in the model structure to more correctly determine at which external nutrient load a given lake changes ecosystem state to a clear-water condition.

Simulation-Based Training of Ultrasound-Guided Procedures in Radiology - A Systematic Review.

PURPOSE: To perform a systematic review of the effect of simulation-based training (SBT) of percutaneous abdominal and thoracic ultrasound-guided procedures and to assess the transfer of procedural competence to a clinical context. MATERIALS AND METHODS: This systematic review was conducted in accordance with the PRISMA statement. Pubmed, Embase, Web of Science, and the Cochrane Library were searched for studies assessing procedural competence after SBT. Two authors independently reviewed all studies and extracted data. Risk of bias was assessed using the Cochrane tool for randomized studies (RoB) and non-randomized studies (ROBINS-I). Quality of evidence was assessed using the GRADE approach. RESULTS: 42 studies were included. 6 were randomized controlled, 3 non-randomized controlled, and 33 non-randomized non-controlled. 26 studies examined US-guided abdominal procedures, 13 examined thoracic procedures, and 3 examined both. The results favored SBT compared to other educational interventions and found that training was superior to no training. Only two studies examined the transfer of procedural skills to a clinical context. All studies had a high or critical risk of bias. Thus, the quality of evidence for the effect of SBT on procedural competence was low, and evidence for its transfer to a clinical context was very low. CONCLUSION: The evidence supporting SBT of percutaneous abdominal and thoracic US-guided procedures remains insufficient due to methodological problems and a high risk of bias. Future studies should be randomized and single-blinded, use assessment tools supported by validity evidence, compare different educational strategies, and examine the transfer of skills to a clinical setting.

Rigorous optimisation of multilinear discriminant analysis with Tucker and PARAFAC structures.

BACKGROUND: We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant Analysis (MDA) and demonstrate their usage on Electroencephalography (EEG) and simulated data. While existing MDA methods use heuristic optimisation procedures based on an ambiguous Tucker structure, we propose a rigorous approach via optimisation on the cross-product of Stiefel manifolds. We also introduce MDA methods with the PARAFAC structure. We compare the proposed approaches to existing MDA methods and unsupervised multilinear decompositions. RESULTS: We find that manifold optimisation substantially improves MDA objective functions relative to existing methods and on simulated data in general improve classification performance. However, we find similar classification performance when applied to the electroencephalography data. Furthermore, supervised approaches substantially outperform unsupervised mulitilinear methods whereas methods with the PARAFAC structure perform similarly to those with Tucker structures. Notably, despite applying the MDA procedures to raw Brain-Computer Interface data, their performances are on par with results employing ample pre-processing and they extract discriminatory patterns similar to the brain activity known to be elicited in the investigated EEG paradigms. CONCLUSION: The proposed usage of manifold optimisation constitutes the first rigorous and monotonous optimisation approach for MDA methods and allows for MDA with the PARAFAC structure. Our results show that MDA methods applied to raw EEG data can extract discriminatory patterns when compared to traditional unsupervised multilinear feature extraction approaches, whereas the proposed PARAFAC structured MDA models provide meaningful patterns of activity.

Electrophysiological evidence for differences between fusion and combination illusions in audiovisual speech perception.

Incongruent audiovisual speech stimuli can lead to perceptual illusions such as fusions or combinations. Here, we investigated the underlying audiovisual integration process by measuring ERPs. We observed that visual speech-induced suppression of P2 amplitude (which is generally taken as a measure of audiovisual integration) for fusions was similar to suppression obtained with fully congruent stimuli, whereas P2 suppression for combinations was larger. We argue that these effects arise because the phonetic incongruency is solved differently for both types of stimuli.

Further delineation of facioaudiosymphalangism syndrome: Description of a family with a novel NOG mutation and without hearing loss.

Mutations in the NOG gene give rise to a wide range of clinical phenotypes. Noggin, the protein encoded by this gene is a secreted modulator of multiple pathways involved in both bone and joint development. Proximal symphalangism is commonly observed in patients bearing mutations in this gene, however secondary symptomes are often found including typical facies with hemicylindrical nose with bulbous tip, hyperopia, reduced mobility of multiple joints, hearing loss due to stapes fixation, and recurrent pain from affected joints. With large variation of the phenotype both within and between affected families careful delineation of the genotype-phenotype correlation is needed. In this work we describe a Danish family suffering from SYNS1 due to a novel NOG gene mutation (C230Y). We provide detailed clinical description of the family members presenting rare phenotype of the shoulders shared by affected individuals but no hearing loss, further adding to the phenotypic variability of the syndrome. With these findings we broaden the understanding of NOG-related-symphalangism spectrum disorder. © 2016 Wiley Periodicals, Inc.

The early maximum likelihood estimation model of audiovisual integration in speech perception.

Speech perception is facilitated by seeing the articulatory mouth movements of the talker. This is due to perceptual audiovisual integration, which also causes the McGurk-MacDonald illusion, and for which a comprehensive computational account is still lacking. Decades of research have largely focused on the fuzzy logical model of perception (FLMP), which provides excellent fits to experimental observations but also has been criticized for being too flexible, post hoc and difficult to interpret. The current study introduces the early maximum likelihood estimation (MLE) model of audiovisual integration to speech perception along with three model variations. In early MLE, integration is based on a continuous internal representation before categorization, which can make the model more parsimonious by imposing constraints that reflect experimental designs. The study also shows that cross-validation can evaluate models of audiovisual integration based on typical data sets taking both goodness-of-fit and model flexibility into account. All models were tested on a published data set previously used for testing the FLMP. Cross-validation favored the early MLE while more conventional error measures favored more complex models. This difference between conventional error measures and cross-validation was found to be indicative of over-fitting in more complex models such as the FLMP.

Allogeneic mesenchymal stem cell therapy for dry eye disease in patients with Sjögren's syndrome: A randomized clinical trial.

PURPOSE: This double-blinded randomized clinical trial aimed to evaluate the efficacy of injecting allogeneic adipose-derived mesenchymal stem cells (ASCs) into the lacrimal gland (LG) for the treatment of dry eye disease (DED) secondary to Sjögren's syndrome (SS). METHODS: Fifty-four participants with severe DED secondary to SS were included and allocated to either ASCs (n = 20), vehicle (n = 20), or a non-randomized observation group (n = 14). The intervention groups received a single injection of either ASCs or an active comparator (vehicle, Cryostor® CS10) into the LG in one eye, while the observation group received lubricating eye drops only. The primary outcome measure was changes in Ocular Surface Disease Index (OSDI) score and secondary outcome measures were non-invasive tear break-up time, tear meniscus height, Schirmer's test, and Oxford score within a 12-month follow-up. RESULTS: A significant reduction in OSDI score was observed in the ASCs and vehicle groups compared to the observation group. In addition, the ASCs group demonstrated a significant increase in non-invasive tear break-up time compared to the vehicle group at the 4-week follow-up and to the observation group at the 12-month follow-up. A significant improvement in ocular surface staining, tear osmolarity, and Schirmer test score from baseline was also observed in the ASCs group; however, these changes were not significant compared to the other groups. CONCLUSION: Improvement of subjective and objective signs and symptoms of DED was observed in both intervention groups following injection into the LG compared to the observation group. Future studies should investigate the mode-of-action of both injection treatments.

Diet of Free Ranging American Mink (Neovison vison) in Denmark.

Non-native American mink (Neovison vison) is a widely spread predator in Denmark. The feral population in mainland Denmark consists of captive-born mink that escaped from fur farms and wild mink born in nature, whereas the population on Bornholm is almost all wild-born mink. In this study, the diets of feral mink in mainland Denmark and on the island of Bornholm are analyzed. The aim of this study was to examine (1) whether the diet of the larger captive-born mink differs from that of the smaller wild-born mink, (2) assess the regional variations between the diets of mink in mainland Denmark and on Bornholm, and (3) investigate the seasonal variation in the diet composition of mink. The stomach contents of 364 mink (243 wild-born and 114 captive-born) culled in the years 2019-2022 were analyzed. Of these, 203 mink were from mainland Denmark, and 154 were from Bornholm. No significant differences were found between the diets of captive-born mink and wild-born mink or the mink found in mainland Denmark and on the island of Bornholm. Significantly more empty stomachs were found during spring than during the summer and autumn, suggesting a bottleneck in the diet during spring.

The possible impact of escaped captive American mink (Neogale vison) on the population of feral mink in Denmark.

The Danish feral mink (Neogale vison) population can be divided into wild-born and captive-born mink newly escaped from a farm. The impact of the captive-born mink on the population of feral population is unknown. Captive-born mink has lately been estimated to make up 25-30% of the feral mink population. In December 2020 Danish mink farms were decommissioned until January 2023. The aim of this study was to: 1) Examine whether a supplementation of farmed mink into the feral mink population influence the population growth and extinction rate of the feral mink, 2) Investigate which parameters affect the viability in feral mink populations the most. The age and mortality of 351 mink and the reproduction of 85 adult females culled by hunters from 2019-2022 were determined in three distinct geographic areas of Denmark (Jutland, Zealand, and Bornholm). VORTEX simulations were used to model the population viability and to simulate the impact of a supplementation of captive-born mink into the wild population. Simulations show that changes by 30% in the parameters: fecundity, percentages of breeding females and mortality influenced population size influenced the final population size significantly. The initial population size and inbreeding did not affect the population growth significantly. The simulations showed that the mink population in the regions of Jutland and Zealand could go extinct within 15 to 30 years without any supplementation of captive-born mink to the feral population. The population on Bornholm will however remain stable with current levels of mortality and reproduction even without supplementation of captive mink.

Out-of-hospital multimodal seizure detection: a pilot study.

Background: Out-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings. Methods: Seventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings. Results: In total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold. Conclusion: We found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

Performance and Agreement When Annotating Chest X-ray Text Reports-A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System.

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study.

OBJECTIVE: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection. METHODS: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies. RESULTS: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research. CONCLUSIONS: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG. SIGNIFICANCE: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions.

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans.

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity.

There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.

Long-term Safety of Treatment with Autologous Mesenchymal Stem Cells in Patients with Radiation-Induced Xerostomia: Primary Results of the MESRIX Phase I/II Randomized Trial.

PURPOSE: Mesenchymal stem/stromal cell therapy may reduce radiation-induced xerostomia. We investigated the long-term safety of autologous adipose tissue-derived mesenchymal stem/stromal cell (ASC) injections into the submandibular glands. EXPERIMENTAL DESIGN: An investigator-initiated, randomized, single-center, placebo-controlled trial. Previous patients with oropharyngeal squamous cell carcinoma with radiation-induced xerostomia were randomly (1:1) allocated to receive a 2.8 million ASCs/cm3 injection or placebo in both submandibular glands and followed for a minimum of 2 years. The primary endpoint was number of serious adverse events (SAE). Secondary endpoints included whole saliva flow rates and xerostomia-related symptoms. Data analysis was based on the intention-to-treat population using repeated measures mixed-effects linear models. RESULTS: Thirty-three patients were randomized; 30 patients were treated (ASC group, n = 15; placebo group, n = 15). Long-term safety data were collected from all 30 patients. During follow-up, 6 of 15 (40%) of the ASC-treated patients versus 5 of 15 (33%) of the placebo patients experienced an SAE; no SAEs appeared to be treatment related. Unstimulated whole saliva flow rate increased to 0.20 and 0.16 mL/minute in the ASC and placebo group, respectively, yielding a 0.05 mL/minute (95% confidence interval: 0.00-0.10; P = 0.051) difference between groups. Patient-reported xerostomia symptoms diminished according to a decreased xerostomia questionnaire summary score of 35.0 and 45.1, respectively [-10.1 (-18.1 to -2.2); P = 0.013]. Three of the visual analog scale xerostomia measures indicated clinical benefit following use of ASC. CONCLUSIONS: Our data show that ASC therapy is safe with a clinically relevant effect on xerostomia-related symptoms. Confirmation in larger randomized controlled trials is warranted.

Spinal versus conventional fine needle for ultrasound-guided thyroid nodule biopsy: a protocol for a randomised clinical trial.

INTRODUCTION: Thyroid nodules are very common and constitute an increasing clinical challenge since improved imaging capabilities and utilisation have led to a higher number of incidental findings. Ultrasound-guided fine-needle aspiration biopsy (FNAB) is the standard diagnostic tool in the work-up of thyroid nodules suspected of malignancy. Non-diagnostic results remain common and require repeated FNAB, leading to increased costs and delayed treatment of thyroid diseases, including treatment of thyroid cancer. If cytological diagnoses cannot be achieved, surgery may be warranted, which may potentially lead to overtreatment. Optimisation of the FNAB procedure is therefore essential. Spinal needles with a stylet have been found to lead to fewer non-diagnostic results, but studies on the subject are few. METHODS: This is a multicentre, two-arm, randomised clinical trial. Adults with thyroid nodules suspected of malignancy will be included consecutively. A total of 350 patients will be assigned randomly 1:1 to have a FNAB with either a spinal (25G) or a conventional (25G) needle. The primary outcome is the rate of diagnostic cytological samples according to the Bethesda system. Secondary outcomes are patient-experienced pain, complication rate and sensitivity and specificity. CONCLUSIONS: This trial will explore whether FNAB from thyroid nodules employing spinal needles compared with conventional fine needles improves diagnostic results, thereby providing evidence-based recommendations for a future choice of the FNAB needle. Secondary outcomes are patient-experienced pain, complication rate and sensitivity and specificity. FUNDING: This trial received funding from Erik and Susanna Olesens Fond. The funding source had no influence on trial design, data collection, analysis or publication. CLINICALTRIALS: gov Identifier: NCT04879355. Registration date: 07032021; version: 29062022.

Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays-An Early Step in the Development of a Deep Learning-Based Decision Support System.

Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph's kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph's Kappa, 0.40-0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels.