Full Data Analysis Research Articles

The performance of prognostic models depended on the choice of missing value imputation algorithm: a simulation study

ObjectivesThe development of clinical prediction models is often impeded by the occurrence of missing values in the predictors. Various methods for imputing missing values before modeling have been proposed. Some of them are based on variants of multiple imputations by chained equations, while others are based on single imputation. These methods may include elements of flexible modeling or machine learning algorithms, and for some of them user-friendly software packages are available. The aim of this study was to investigate by simulation if some of these methods consistently outperform others in performance measures of clinical prediction models. Study Design and SettingWe simulated development and validation cohorts by mimicking observed distributions of predictors and outcome variable of a real data set. In the development cohorts, missing predictor values were created in 36 scenarios defined by the missingness mechanism and proportion of noncomplete cases. We applied three imputation algorithms that were available in R software (R Foundation for Statistical Computing, Vienna, Austria): mice, aregImpute, and missForest. These algorithms differed in their use of linear or flexible models, or random forests, the way of sampling from the predictive posterior distribution, and the generation of a single or multiple imputed data set. For multiple imputation, we also investigated the impact of the number of imputations. Logistic regression models were fitted with the simulated development cohorts before (full data analysis) and after missing value generation (complete case analysis), and with the imputed data. Prognostic model performance was measured by the scaled Brier score, c-statistic, calibration intercept and slope, and by the mean absolute prediction error evaluated in validation cohorts without missing values. Performance of full data analysis was considered as ideal. ResultsNone of the imputation methods achieved the model's predictive accuracy that would be obtained in case of no missingness. In general, complete case analysis yielded the worst performance, and deviation from ideal performance increased with increasing percentage of missingness and decreasing sample size. Across all scenarios and performance measures, aregImpute and mice, both with 100 imputations, resulted in highest predictive accuracy. Surprisingly, aregImpute outperformed full data analysis in achieving calibration slopes very close to one across all scenarios and outcome models. The increase of mice's performance with 100 compared to five imputations was only marginal. The differences between the imputation methods decreased with increasing sample sizes and decreasing proportion of noncomplete cases. ConclusionIn our simulation study, model calibration was more affected by the choice of the imputation method than model discrimination. While differences in model performance after using imputation methods were generally small, multiple imputation methods as mice and aregImpute that can handle linear or nonlinear associations between predictors and outcome are an attractive and reliable choice in most situations.

Non-Gaussian Liability Distribution for Depression in the General Population.

Unlike depression sum scores, the underlying risk for depression is typically assumed to be normally distributed across the general population. To assess the true empirical shape of depression risk, we created a continuous-valued estimate of the latent depression density, using the Davidian-Curve Item Response Theory (DC-IRT) and the National Health and Nutrition Examination Survey (NHANES) cohorts from 2005 to 2018 (n = 36,244 on the Nine-item Patient Health Questionnaire; PHQ-9). We conducted simulations to investigate the performance of DC-IRT for large samples and realistic items. The method can recover complex latent-risk distributions even when they are not evident from sum scores. However, estimation accuracy for different sample sizes depends on the method of model selection. In addition to full-data analysis, random samples of a few thousand observations were drawn for analysis. The latent shape of depression was left-skewed and bimodal in both investigations, indicating that the latent-normality assumption does not hold for depression.

Timing of Loop Ileostomy Closure Does Not Play a Pivotal Role in Terms of Complications-Results of the Liquidation of iLEOstomy (LILEO) Study.

Loop ileostomy is commonly performed by colorectal and general surgeons to protect newly created large bowel anastomoses. The optimal timing for ileostomy closure remains debatable. Defining the timing associated with the best postoperative outcomes can significantly improve the clinical results for patients undergoing ileostomy closure. The LILEO study was a prospective multicenter cohort study conducted in Poland from October 2022 to December 2023. Full data analysis involved 159 patients from 19 surgical centers. Patients were categorized based on the timing of ileostomy reversal: early (<4 months), standard (4-6 months), and delayed (>6 months). Data on demographics, clinical characteristics, and perioperative outcomes were analyzed for each group separately and compared. No significant differences were observed in length of hospital stay (p = 0.22), overall postoperative complications (p = 0.43), or 30-day reoperation rates (p = 0.28) across the three groups. Additional analysis of Clavien-Dindo complication grades was performed and did not show significant differences in complication severity (p = 0.95), indicating that the timing of ileostomy closure does not significantly impact perioperative complications or hospital stay. Decisions on ileostomy reversal timing should be personalized and should consider individual clinical factors, including the type of adjuvant oncological treatment and the preventive measures performed for common postoperative complications.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable. Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response. Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time. All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively. The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

Probing atmospheric escape through metastable He I triplet lines in 15 exoplanets observed with SPIRou

For several years, the metastable helium triplet line has been successfully used as a tracer to probe atmospheric escape in transiting exoplanets. This absorption in the near-infrared (1083.3 nm) can be observed from the ground using high-resolution spectroscopy, providing new constraints on the mass-loss rate and the temperature characterizing the upper atmosphere of close-in exoplanets. The aim of this work is to search for the He triplet signature in 15 transiting exoplanets – ranging from super-Earths to ultrahot Jupiters – observed with SPIRou, a high-resolution (R ~ 70 000) near-infrared spectropolarimeter at the CFHT, in order to bring new constraints or to improve existing ones regarding atmospheric escape through a homogeneous study. We developed a full data processing and analysis pipeline to correct for the residual telluric and stellar contributions. We then used two different 1D models based on the Parkerwind equations and nonlocal thermodynamic equilibrium (NLTE) radiative transfer to interpret the observational results. We confirm published He triplet detections for HAT-P-11 b, HD 189733 b, and WASP-69 b. We tentatively detect the signature of escaping He in HD 209458 b, GJ 3470 b, and WASP-76 b. We report new constraints on the mass-loss rate and temperature for our three detections and set upper limits for the tentative and nondetections. We notably report improved constraints on the mass-loss rate and temperature of the escaping gas for TOI-1807 b, and report a nondetection for the debated atmospheric escape in GJ 1214 b. We also conducted the first search for the He signature in GJ 486 b since its discovery and report a nondetection of the He triplet. Finally, we studied the impact of important model assumptions on our retrieved parameters, notably the limitations of 1D models and the influence of the H/He ratio on the derived constraints.

Elucidating the immune active state of HR+HER2- MammaPrint High 2 early breast cancer.

506 Background: The 70-gene signature, MammaPrint (MP), classifies patients (pts) with early-stage breast cancer (EBC) as having an UltraLow, Low, High 1 (H1), or High 2 (H2) risk of distant recurrence. I-SPY 2 showed that pts with MP H2, HR+HER2- tumors have significantly higher response rates to neoadjuvant chemotherapy + immunotherapy relative to pts with MP H1 tumors. Expression-based immune deconvolution provides more detailed information about immune cell function beyond conventional methods enumerating tumor infiltrating lymphocytes. To elucidate the underlying biology that mediates immune therapy response, we performed in silico analysis of full transcriptome data to characterize immune cell frequencies and antigen presentation in HR+HER2-, MP High Risk EBC from pts enrolled in FLEX. Methods: The prospective, observational FLEX Study (NCT03053193) includes stage I-III pts with EBC who received MP testing and consented to full transcriptome and clinical data collection. Women with HR+HER2-, and MP High Risk invasive ductal EBC were included (n = 2916). Tumors were stratified into MP H1 or MP H2. The R-package Limma was used to preprocess gene expression data. The gene signature-based method xCell was used to determine immune cell abundances for each group based on enrichment score. Expression of genes involved in “antigen processing and presentation KEGG pathway”, and immune regulation, were evaluated between groups. Differences in immune cell frequency and gene expression were determined by a t-test with an adjusted p-value &lt; 0.05. Results: MP classified 79% (n=2292) of HR+HER2- EBC as H1 and 21% (n=624) as H2. Pts with H2 EBC were more likely to be ≤ 50 years (32%) compared to pts with H1 tumors (23%). Although low ER staining (1-10%) tumors were more frequent in H2 vs H1 (15% vs 1%), a majority of H2 tumors (75%) had ER staining &gt; 10%. Nearly 30% of H2 tumors were Grade 1 or 2, whereas 22% of H1 tumors were Grade 3. H2 tumors had significantly higher frequency of antigen presenting cells (APCs) including activated dendritic cells and macrophages, CD4+ memory T cells, CD8+ T cells, memory B cells and plasma cells relative to H1 tumors. The genes expressing PD-1 and PDL-1 and genes involved in antigen processing, including B2Mand TAP1/2, and presentation, i.e., major histocompatibility (MHC) class I ( HLA-A, -B, -F) and class II molecules ( HLA-DM, -DQ), were significantly upregulated in H2 vs H1. Conclusions: H2 tumors exhibited a heighted immune active state compared to H1 tumors among HR+HER2- EBC pts. The presence of APCs and increased antigen presentation, which are critical in eliciting T- and B-cell activation may explain the improved response rates to immunotherapy observed in H2 tumors. Selecting pts based on grade or ER% alone may exclude pts likely to benefit from immune therapy. These data support ongoing trials evaluating the response and benefit of adding immunotherapy to standard of care treatment regimens in MP H2 tumors. Clinical trial information: NCT03053193 .

#1030 Addressing the unmet need of patient reported outcomes in ANCA associated vasculitis, in routine clinical practice

Abstract Background and Aims ANCA associated vasculitis (AAV) is associated with increased morbidity. Recent years has seen improvement in clinical outcomes yet little work has been done to better understand the patient's role within this condition. Patient reported outcome measures (PROMs) are increasingly standard in clinical research outcomes, as well as an overall indicator for good quality patient care. With the exception of Robson et al AAV-PRO [1], little research has been done to develop validated disease specific tools for patients living with AAV. We aim to develop a disease specific tool that can be used in clinical practice to aid patients and clinicians in understanding and track changes in health-related quality of life (HRQoL) over time. Method AAV patients from a single centre were invited to take part in focus groups conducted using a semi-structured topic guide. Participants were asked to discuss topics relating to four broad areas; 1) the effects of AAV on everyday life and living, 2) the effects of treatment on everyday life and living, 3) the role of patients in health management and the patient experience 4) a review of existing generic and disease specific PROM tools (SF-36 and AAV-PRO). Audio transcript of each focus group was transcribed verbatim and data was analysed using a combination of inductive and deductive framework analysis [2]. Results Twenty-eight patients with AAV were invited to participate, 19 attended one of three, 2.5 hour focus groups, 2 patients failed to attend, 4 declined and 3 were unavailable. The mean age of participants was 66.6 +9.4 years and there was a near equal male to female split (10:9). The median time from diagnosis as 7.4 years. Three participants had active or newly diagnosed AAV with BVAS scores of &amp;gt;10. Five participants had previously required dialysis with 1 receiving a renal transplant. All participants received glucocorticoid and immunosuppressive therapy (cyclophosphamide and or Rituximab), 15 (78.9%) were still receiving immunotherapy. Full analysis of the qualitative data is being undertaken. Preliminary findings outlined what was most important to participants when considering HRQoL. Areas acknowledged included; self-identity, social roles e.g. fulfilling caring responsibilities, fears of relapse and disease and treatment burden. Fifty-six deductive and inductive codes were identified which have been further categorised. Participants felt existing tools lacked consideration of some key issues. Scoring systems were considered to be negativity worded e.g. mild, moderate, severe, with the term ‘none’ being the only positive option. Participants reported that existing tools overlooked positive factors which impact HRQoL, such as symptom validation, close clinician monitoring and improvement with treatment. Limitations were noted when trying to address transient or inconsistent symptoms and difficulty separating vasculitis from other health conditions when answering questions. Participants expressed a preference for using a PROM as part of their clinical care and felt that completing it prior to clinic would be most helpful and facilitate discussions with healthcare professionals. The use of an electronic-PROM was widely supported and would support clinicians and patients to monitor changes over time. Conclusion The AAV-PRO represents a significant advancement over generic tools such as the SF-36, however, existing PROMs are not without limitations and are primarily used in research practices. Our study revealed that patients desire active participation in HRQoL discussions and further work to incorporate this into the clinic setting is needed. We are continuing to develop a PROM tool that can be used to assess patients HRQoL in relation to their AAV in the outpatient setting. We believe this will allow a deeper understanding of the disease burden and could support a comprehensive, patient-centred approach to decision-making, offering new insights and identifying the need for supportive care. On a broader scale, these insights can be utilized to compare treatments, shed light on inequalities and facilitate enhancements in services.

Cosmological constraints on early dark energy from the full shape analysis of eBOSS DR16

ABSTRACT We evaluate the effectiveness of early dark energy (EDE) in addressing the Hubble tension using the luminous red galaxy (LRG), quasar (QSO), and emission line galaxy (ELG) samples from the completed eBOSS survey. We perform cosmological parameter measurements based on full shape analysis of the power spectrum employing the effective field theory of large-scale structure (EFTofLSS). EDE is known to strongly suffer from volume projection effects, complicating cosmological constraints’ interpretation. To quantify the volume projection effects within an EDE full shape analysis, we explore the impact of different prior choices on the nuisance parameters of EFTofLSS through an extensive mock study. We compare classical Gaussian priors to the non-informative Jeffreys prior, known to mitigate volume projection effects in ΛCDM. Our full shape analysis combines eBOSS and BOSS data with Planck, external Baryon Acoustic Oscillation (BAO), PantheonPlus, and SH0ES supernova data. EDE reduces the tension from 5.2σ to 3σ compared to ΛCDM, yielding $H_0=71.73_{-0.86}^{+0.82}$ km s−1 Mpc−1 with $f_\mathrm{EDE} = 0.1179_{-0.022}^{+0.025}$ (Gaussian priors) and $H_0=72.03_{-0.87}^{+0.82}$ km s−1 Mpc−1 with $f_\mathrm{EDE} = 0.1399_{-0.022}^{+0.023}$ (Jeffreys prior). Although the Hubble tension is mitigated compared to ΛCDM, the inclusion of eBOSS data amplifies the tension within EDE from 2σ to 3σ, in contrast to the full shape analysis of BOSS data with Planck, external BAO, PantheonPlus, and SH0ES. This highlights the significance of incorporating additional large-scale structure data in discussions concerning models aiming to resolve the Hubble tension.

Offshore field experimentation for novel hybrid condition monitoring approaches

This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of components and highly variable operating conditions, makes drive train condition monitoring a challenging task. This paper details the full data measurement and analysis flow from sensor to insights and proposes a hybrid automated pipeline with signal processing and data-driven techniques to address the complexity of dealing with wind turbine vibration data. The vibration signals are directly employed to estimate the wind turbine’s instantaneous angular speed to compensate for any rotation speed fluctuations. Pre-processing is performed on the speed-independent signals to evaluate condition indicators in both the time and spectral domain for the vibration signals and their envelopes. Machine learning is then employed to distinguish the healthy state of the machine from a faulty one using the computed condition indicators. Besides the scalar indicators, also two-dimensional vibration decompositions such as the cyclic spectral correlation maps are used as inputs to the machine learning pipeline. This comprehensive and automated approach ensures both an early and reliable fault detection. Experimental results demonstrate that the fully automated hybrid pipeline can effectively be used for fleet-based health tracking of offshore wind turbine drivetrains.

Safety and efficacy of faecal microbiota transplantation in patients with mild to moderate Parkinson's disease (GUT-PARFECT): a double-blind, placebo-controlled, randomised, phase 2 trial

Dysregulation of the gut microbiome has been implicated in Parkinson's disease (PD). This study aimed to evaluate the clinical effects and safety of a single faecal microbiota transplantation (FMT) in patients with early-stage PD. The GUT-PARFECT trial, a single-centre randomised, double-blind, placebo-controlled trial was conducted at Ghent University Hospital between December 01, 2020 and December 12, 2022. Participants (aged 50-65 years, Hoehn and Yahr stage 2) were randomly assigned to receive nasojejunal FMT with either healthy donor stool or their own stool. Computer-generated randomisation was done in a 1:1 ratio through permutated-block scheduling. Treatment allocation was concealed for participants and investigators. The primary outcome measure at 12 months was the change in the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor score obtained during off-medication evaluations. Intention-to-treat analysis was performed using a mixed model for repeated measures analysis. This completed trial is registered on ClinicalTrials.gov (NCT03808389). Between December 2020 and December 2021, FMT procedures were conducted on 46 patients with PD: 22 in the healthy donor group and 24 in the placebo group. Clinical evaluations were performed at baseline, 3, 6, and 12 months post-FMT. Full data analysis was possible for 21 participants in the healthy donor group and 22 in the placebo group. After 12 months, the MDS-UPDRS motor score significantly improved by a mean of 5.8 points (95% CI-11.4 to-0.2) in the healthy donor group and by 2.7 points (-8.3 to 2.9) in the placebo group (p=0.0235). Adverse events were limited to temporary abdominal discomfort. Our findings suggested a single FMT induced mild, but long-lasting beneficial effects on motor symptoms in patients with early-stage PD. These findings highlight the potential of modulating the gut microbiome as a therapeutic approach and warrant a further exploration of FMT in larger cohorts of patients with PD in various disease stages. Flemish PD patient organizations (VPL and Parkili), Research Foundation Flanders (FWO), Biocodex Microbiota Foundation.

Application analysis of data mining in shopping APP

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platforms commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.

Laser Doppler Fluximetry in Cutaneous Vasculature: Methods for Data Analyses

Introduction: Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators. Methods: This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators. Conclusion: It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.

Statistical and machine learning applied to the universal consolidated database to predict heat transfer coefficient in flow condensation

Due to complex physical mechanisms, and a general lack of accurate modeling techniques, two phase convective heat transfer is hard to characterize. In this article we show the general effectiveness of statistical and machine learning techniques for predicting two-phase heat transfer coefficients. This is a comprehensive study that: performs a full exploratory data analysis (EDA) to determine the range by which we may apply these models, derives an optimal data set as a basis for predicting heat transfer coefficient, tests all models on an independent data set, and assesses model performance on multiple metrics. We find that random forest methods, gradient boosted machines, and multi-layer perceptrons (deep neural networks) perform the best in predicting heat transfer coefficient on the given data set. We find that in general, the increased data base predicts better due to the inclusion of a few important thermophysical and flow properties.

Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting

Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting

Analysis of the impacts of COVID-19 on the performance of Texas transit agencies

The rapid nature of the response to the COVID-19 pandemic resulted in limited real-time understanding of the true impacts of COVID-19 on transit and the actions taken by transit agencies to respond to the conditions and support their riders. To collect empirical information on the impacts and actions associated with transit’s response to COVID 19, we conducted a survey on transit agencies in Texas designed to provide a better, quantifiable picture of transit realities during the early stages of the pandemic. This paper presents the findings derived from a full analysis of the survey data, highlighting important takeaways, and shares the result of follow up study from three focused case studies that present deeper insight into some transit agencies’ responses to these impacts, and specific policy implications that transit may experience during or after the pandemic. Survey findings showed that about 65 percent of the respondents did not have a plan for dealing with a pandemic prior to COVID-19. The cost of cleaning (both labor and supplies) hit large urban agencies and metropolitan transit authorities particularly hard. Among the lessons learned from the case studies, we found that agencies are inclined to retain employees through the pandemic and resulting downturn in ridership, with cuts in hours and allowance to work at home where possible, rather than laying off staff and later having to recruit, hire, and train the staff buildup when pre-pandemic ridership levels resume. We also suggest specific policies in four areas of revenue levels, equitable transit access, transit and employment.

Performance of pre- and post-pembrolizumab vesical imaging–reporting and data system (VI-RADS) to predict the pathological response and efficacy outcomes in muscle-invasive urothelial bladder cancer (MIBC): Full data analysis from the PURE-01 trial.

65 Background: The VI-RADS is a standardized reporting system that uses multiparametric magnetic resonance imaging (mpMRI) parameters to differentiate non muscle-invasive bladder cancer (NMIBC) from MIBC. Limited data on clinical response assessment to neoadjuvant therapy are currently available. In this study, we aimed to analyze the performance of VI-RADS to predict the pT0 or pT≤1 response post-neoadjuvant immunotherapy in MIBC. Methods: In PURE-01 study (NCT02736266) patients (pts) were staged with mpMRI before and after treatment (3 cycles of Pembrolizumab) prior to radical cystectomy (RC). Logistic regression models analyzed pre- and post-pembro VI-RADS, with clinical and tumor-related variables, against ypT≤1N0 (primary endpoint) and ypT0N0 (secondary endpoint). VI-RADS scores were dichotomized between 0-3 (0 = no evidence of disease) and 4-5 (5 = extension to extravescical fat). Event-free survival (EFS) and overall survival (OS) analyses based on VI-RADS were performed. Comprehensive genomic profiling and transcriptome-wide expression profiling data were matched with VI-RADS scores. Model performance was also tested in the ongoing NureCombo (nivolumab+nab-paclitaxel; NCT04876313) and SURE-01 (sacituzumab govitecan; NCT05226117) neoadjuvant studies. Results: In total, 110 patients treated with neoadjuvant pembrolizumab between 02/2017 and 07/2020 had centrally reviewed scans (N=220 mpMRI). Pre-pembrolizumab, 21 patients (19.1%) had no measurable disease (VI-RADS=0), 34 (30.9%) had a VI-RADS 1-3 score, and 55 (50%) had a VI-RADS 4-5 score. Both pre-pembrolizumab and post-pembrolizumab VI-RADS 0-3 scores were the only significant covariates that predicted the ypT≤1N0 endpoint on multivariable analyses (MVA): the strongest effect was seen with post-pembrolizumab VI-RADS 0-3 predicting the ypT≤1N0 response (odds ratio [OR]: 23.4, 95%CI: 7-95.3, p&lt;0.0001). The area-under-the-curve (AUC) of this model was 0.90. Post-pembrolizumab VI-RADS 0-3 also predicted longer EFS (p&lt;0.001) and OS (p=0.044). The scores of several gene signatures from baseline tumors differed between pre-pembrolizumab VI-RADS 0-3 and 4-5 categories. RAF1 mutations were enriched in pre-pembrolizumab VI-RADS 0-3 group (p=0.045). Conclusions: VI-RADS scores post-pembrolizumab revealed a robust association with pathological downstaging and survival. VI-RADS scores were also characterized by distinct biomarker features. These results indicate that VI-RADS is emerging as an important tool to design next-generation trials in MIBC. Clinical trial information: NCT02736266 .

Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer

Positron emission tomography (PET) is a noninvasive imaging technology able to assess the metabolic or functional state of healthy and/or pathological tissues. In clinical practice, PET data are usually acquired statically and normalized for the evaluation of the standardized uptake value (SUV). In contrast, dynamic PET acquisitions provide information about radiotracer delivery to tissue, its interaction with the target and its physiological washout. The shape of the time activity curves (TACs) embeds tissue-specific biochemical properties. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function, and tracer-specific compartmental models to obtain a full quantitative analysis of PET data. This method’s primary disadvantage is the requirement for invasive arterial blood sample collection throughout the whole PET scan. In this study, we employ a variety of deep learning models to illustrate the diagnostic potential of dynamic PET acquisitions of varying lengths for discriminating breast cancer lesions in the absence of arterial blood sampling compared to static PET only. Our findings demonstrate that the use of TACs, even in the absence of arterial blood sampling and even when using only a share of all timeframes available, outperforms the discriminative ability of conventional SUV analysis.

Feasibility of a Career Development Intervention for Veterans in Vocational Rehabilitation: Protocol for a Pilot Randomized Controlled Trial

Background Veterans with psychiatric disorders want additional career development services to support their recovery and pursuit of meaningful employment. However, no career counseling programs have been designed for this specific population. We developed the Purposeful Pathways intervention to fill this need. Objective This study protocol aims to (1) evaluate the feasibility and acceptability of the Purposeful Pathways intervention for veterans living with psychiatric disorders and (2) explore preliminary clinical outcome data. Methods A total of 50 veterans who are participating in transitional work vocational rehabilitation services at a Veterans Affairs hospital will be randomized to either treatment as usual or the augmented treatment condition (treatment as usual plus Purposeful Pathways). Feasibility will be assessed via recruitment rates, clinician fidelity to treatment, retention rates, and acceptability of randomization procedures. Acceptability will be assessed via client satisfaction at treatment termination using quantitative and qualitative data collection. Preliminary clinical and vocational outcomes will be assessed at baseline, 6 weeks, 12 weeks (treatment termination), and a 3-month follow-up via quantitative measures assessing vocational functioning, vocational process, and mental and physical functioning. Results This pilot randomized controlled trial is beginning recruitment in June 2023 and is expected to continue through November 2025. Data collection is expected to be completed by February 2026, with full data analysis completed by March 2026. Conclusions Findings from this study will provide information on the feasibility and acceptability of the Purposeful Pathways intervention, as well as secondary outcomes related to vocational functioning, vocational process, and mental and physical functioning. Trial Registration ClinicalTrials.gov NCT04698967; https://clinicaltrials.gov/ct2/show/NCT04698967 International Registered Report Identifier (IRRID) PRR1-10.2196/47986

Management of Local Community-Based Tourism in West Sekotong Village – West Lombok Regency

The concept of CBT (community base tourism) in the management of a tourism object is a rational choice and answer to various weaknesses with the G + B (government and business world) approach. In CBT's view, management is also carried out by the local community/local community, so the pattern becomes G+B+C. This study aims to describe forms of tourism management by local communities, and their relationship with G+B. Besides that, the research seeks to explore the operationalization of one aspect of Sapta Pesona into programs/activities that have been carried out by the local community. The research location is in West Sekotong Village, West Lombok Regency, with key informants as respondents. The survey was also conducted on domestic tourists to get their opinion in the form of an assessment of the implementation of the Cleanliness Program, and as feedback (feed back) on the CBT management model. Full data analysis using qualitative data. The participation of the private sector. Important indicators in the CBT model are decision-making costs (DMC) and financing of the use of local human resources (utilization of human resource cost, UHRC) in building community participation. Based on a survey of domestic tourists on the Cleaning Program (from Sapta Pesona) in Sekotong Barat Village, it was shown that in general they thought the program was 2839relatively good, with a score of 4.104 from the range 1 (=very bad) – 5 (=very good).

Biology and performance of pre- and post-pembrolizumab (pembro) vesical imaging–reporting and data system (VI-RADS) to predict the pathological response in muscle-invasive urothelial bladder cancer (MIBC): Full data analysis from a clinical trials pipeline.

4591 Background: The VI-RADS is a standardized reporting system that uses multiparametric magnetic resonance imaging (mpMRI) parameters to predict the probability of MIBC. It has been also used in studies using standard neoadjuvant chemotherapy. No studies have analyzed the performance of VI-RADS to predict the pT0 or pT≤1 response post-immunotherapy (IO) in MIBC. Methods: In PURE-01 study patients (pts) were staged with mpMRI before and after treatment (3 cycles of pembro) prior to radical cystectomy (RC). All mpMRI scans were centrally reviewed. Logistic regression models analyzed pre- and post-pembro VI-RADS against pT≤1 (primary endpoint) and pT0 (secondary endpoint). VI-RADS scores were dichotomized between 0-3 (0=no evidence of disease) and 4-5. Covariates included cT-stage, age, gender, PD-L1 combined positive score (CPS) and tumor mutational burden (TMB). Model performance was also tested in the ongoing NureCombo (nivolumab+nab-paclitaxel; NCT04876313) and SURE-01 (sacituzumab govitecan; NCT05226117) studies. Transcriptome-wide expression profiling was matched with VI-RADS scores. Results: In PURE-01, 115 pts had centrally-reviewed MRI scans (N=230 mpMRI), treated between 02/17 and 08/18. In total, 32 (27.8%) had pure/predominant non-urothelial carcinoma (UC) histology, 54 (47%) had a cT3-4N0 MIBC. Pre-pembro: 21 pts (18.3%) had no measurable disease (VI-RADS=0), 37 (32.2%) a VI-RADS 1-3 score, and 57 (49.5%) had a VI-RADS 4-5 score. 20 pts (17.4%) had a downstage from VI-RADS 4-5 to VIRADS 0-3 post-pembro. Pre-pembro VIRADS 4-5 were associated with higher angiogenesis and epithelial-mesenchymal transition (EMT) activity vs VI-RADS 0-3 (p=0.011 and 0.033). Both pre-pembro and post-pembro VI-RADS 0-3 scores were the only significant covariates against pT≤1 endpoint on multivariable analyses (MVA): the strongest effect was seen with post-pembro VI-RADS 0-3 against pT≤1 response (OR: 23.4, 95%CI: 7-95.3, p&lt;0.0001). The AUC of this model was 0.90. Model-based decisions were supported in the range of threshold probabilities of 10-80%. Post-pembro VI-RADS 4-5 were characterized by higher stromal signature scores vs VI-RADS 0-3. When post-therapy VI-RADS were tested in evaluable pts from the combined NureCombo+SURE-01 cohort (N=17pts, 34 mpMRI): 90% of post-VI-RADS 0-3 revealed a pT≤1 stage at radical cystectomy. Conclusions: VI-RADS scores mirrored distinct biological features in MIBC, recapitulating tissue biomarkers of IO response. VI-RADS scores post-IO revealed a strong association with pathological downstaging and represented a robust feature based on which selecting pts for bladder-sparing strategies. Results seemed to be also applicable to other novel neoadjuvant therapies beyond IO monotherapy. Clinical trial information: NCT02736266 .