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Protocol for the Development of a Core Outcome Set for Inherited Ichthyosis
No full textINTRODUCTION: Inherited ichthyosis comprises a group of rare keratinization disorders caused by abnormal epidermal barrier function. Ichthyosis is yet incurable and current treatments mainly focus on alleviating symptoms such as scaling, erythema and pruritus. Recent developments show promising results for interventions based on the immune-phenotype like biologicals or pathogenesis-based therapies such as gene therapy. However, the lack of uniform reporting and variety of treatment outcomes may complicate performing and comparing efficacy studies. The core outcome set for inherited ichthyosis (COSII) aims to develop a core outcome set (COS), i.e., the minimum of outcomes that should be measured and reported in observational and interventional studies, including a minimum set of baseline characteristics. METHODS: The COSII project will follow the guidelines from the Core Outcome Measures in Effectiveness Trials (COMET) initiative, including the Core Outcome Set-Standards for Development (COS-STAD) recommendations and the Core Outcome Set Standardised Protocol (COS-STAP) checklist. The COS development methodology, including this protocol, follows the guidance of the CHORD COUSIN Collaboration 'C3'. The first stage of this project involves identifying a possible list of outcomes through performing a scoping literature review and conducting interviews with patient(s) (representatives). This list will be presented to five different stakeholder groups: healthcare professionals, researchers, patient(s) (representatives), industry representatives, and regulators. All stakeholders will rate the importance of each outcome in a three-round eDelphi survey. Ultimately, a virtual consensus meeting will be convened to finalize the COS. Ethical approval was obtained prior to the start of this project from the Medical Ethics Committee Board at Maastricht University Medical Centre (METC 2022-3192). Informed consent will be asked prior to enrolment in the eDelphi. This study is registered with the COMET. The results will be distributed via a peer-reviewed journal, communicated to all relevant parties and showcased at national and international conferences. CONCLUSION: This will be the first COS for inherited ichthyosis research in accordance with the Core Outcome Measures in Effectiveness Trials initiative. The development of a COS aims to improve the consistency of reporting and the heterogeneity of outcomes in ichthyosis research
Defining region boundaries to assess the peritoneal cancer index on imaging:a Delphi study
No full textBackground: The gold standard for evaluating the presence and extent of peritoneal lesions is determining the surgical peritoneal cancer index (PCI). However, there is a growing need for non-invasive methods to assess peritoneal lesions. While the standardised and quantified PCI scoring system can be applied to imaging, the region definitions used for surgical PCI assessment are not directly applicable to radiological assessment. Purpose: To define region boundaries applicable to radiological PCI assessment. Materials and methods: A Delphi study was conducted among 88 international experts, including radiologists, surgeons and gynaecologists. Within a questionnaire, the proposed regions for radiological PCI evaluation were shown as overlays on a CT scan and 3D volumes. Participants rated their level of agreement for each structure and region boundary on a 5-point Likert scale in iterative rounds. Consensus was defined as > 75% agreement and < 15% disagreement, and major agreement was defined as 60–75% agreement. Results: In the first Delphi round, 45 experts participated, leading to consensus on 45 of 52 statements. Regions 3, 5, 7, and 9–12 required further refinement. In the second round, 40 experts participated, resulting in consensus on three additional structures/boundaries. For all remaining structures/boundaries, a major agreement was obtained. Final adjustments involved revising terminology for the lower boundaries of regions 5 and 7 and adopting more practical boundaries for regions 9–12 to ensure equal small bowel volumes in these regions. Conclusion: This study defined region boundaries for radiological PCI assessment, facilitating a structured and practical approach for objective evaluation of peritoneal lesions on imaging. Key Points: Question There is a growing need for a non-invasive and standardised imaging method to assess the presence and extent of peritoneal lesions. Findings This Delphi study achieved expert consensus on most region boundaries for evaluating the radiological PCI, providing structured guidelines for imaging-based assessment of peritoneal lesions. Clinical relevance This study establishes imaging guidelines for assessing peritoneal lesions, enabling more consistent and objective patient evaluations in clinical practice, potentially reducing the need for invasive procedures.</p
Population Pharmacokinetics Model of Thioguanine in Patients with Inflammatory Bowel Disease
No full textBACKGROUND: Thioguanine (TG) has recently been rediscovered as an immunosuppressive agent in the treatment of inflammatory bowel disease (IBD). This prodrug is directly converted into its active metabolites, 6-thioguanine nucleotides (6-TGNs), targeting the inhibition of RAC1 GTPase in inflammatory conditions, disrupting key cellular signaling pathways necessary for T-cell activation and survival, thereby contributing to its immunosuppressive action. In IBD, TG is used fixed dose and may benefit from model-informed precision dosing (MIPD) to optimize treatment efficacy and minimize toxicity. However, a population pharmacokinetic (PopPK) model to do so is lacking. OBJECTIVE: To develop a PopPK model for TG in IBD patients, enhancing the understanding of TG's pharmacokinetics and supporting the implementation of model-informed precision dosing (MIPD). METHODS: We employed a dataset comprising 131 6-TGN trough concentrations from 28 IBD patients treated with TG. The data were analyzed using nonlinear mixed-effects modeling (NONMEM) to estimate pharmacokinetic parameters and explore the influence of covariates such as weight and 5-ASA use on drug disposition. Model fit-for-purpose was evaluated through computation of the model's forecasting performance. RESULTS: The developed PopPK model was a one-compartment model with first-order absorption. A one-compartment TG model was stable, and able to estimate pharmacokinetic parameters with good precision (relative standard error [RSE] 15%) with weight and aminosalicylic acid (5-ASA) use significantly affected TG clearance. Forecasting performance was also adequate with a relative root mean squared error (rRMSE) of 24.1% and practically no systematic bias (mean percentage error [MPE] 0.2%). CONCLUSION: This study presents the first PopPK model of thioguanine for IBD, offering a novel tool for MIPD in clinical settings. Future studies should explore additional covariates such as TPMT genotype and drug interactions to further refine dosing recommendations for diverse patient populations
Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
No full textBackground: Magnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation, a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for automatic segmentation of the aortic lumen and vessel wall in high-resolution electrocardiogram-triggered free-breathing respiratory motion-corrected three-dimensional (3D) bright- and black-blood MRA images. Methods: Manual segmentation, serving as the ground truth, was performed on 25 bright-blood and 15 black-blood 3D MRA image sets acquired with the iT2PrepIR-BOOST sequence (1.5T) in thoracic aortopathy patients. The training was performed with no new U-Net (nnUNet) for bright-blood (lumen) and black-blood image sets (lumen and vessel wall). Training consisted of a 70:20:10% (17/25:5/25:3/25 datasets) training:validation:testing split. Inference was run on datasets (single vendor) from different centers (UK, Spain, and Australia), sequences (iT2PrepIR-BOOST, T2 prepared coronary magnetic resonance angiography [CMRA], and time-resolved angiography with interleaved stochastic trajectories [TWIST] MRA), acquired resolutions (from 0.9–3 mm 3), and field strengths (0.55T, 1.5T, and 3T). Predictive measurements comprised Dice similarity coefficient (DSC) and Intersection over Union (IoU). Postprocessing (3D slicer) included centreline extraction, diameter measurement, and curved planar reformatting (CPR). Results: The optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm 3) and 3D CMRA datasets (0.9 mm 3) resulted in DSC ≥ 0.96 and IoU ≥ 0.92. For bright-blood segmentation on 3D CMRA at 0.55T, the nnUNet achieved DSC and IoU scores of 0.93 and 0.88 at 1.5 mm³, and 0.68 and 0.52 at 3.0 mm³, respectively. DSC and IoU scores of 0.89 and 0.82 were obtained for CMRA image sets (1 mm 3) at 1.5T (Barcelona dataset). DSC and IoU scores of the BRnnUNet model were 0.90 and 0.82, respectively, for the contrast-enhanced dataset (TWIST MRA). Lumen segmentation on black-blood 1.5T iT2PrepIR-BOOST image sets achieved DSC ≥ 0.95 and IoU ≥ 0.90, and vessel wall segmentation resulted in DSC ≥ 0.80 and IoU ≥ 0.67. Automated centreline tracking, diameter measurement, and CPR were successfully implemented in all subjects. Conclusion: Automated aortic lumen and wall segmentation on 3D bright- and black-blood image sets demonstrated excellent agreement with ground truth. This technique demonstrates a fast and comprehensive assessment of aortic morphology with great potential for future clinical application in various cardiovascular diseases.</p
Cumulative Sum Analysis-Integrated E-Learning for Differentiation Between Basal Cell Carcinoma and Non-Basal Cell Carcinoma on Optical Coherence Tomography:An Observational Cohort Study
No full textBACKGROUND: The clinical implementation of optical coherence tomography (OCT) for diagnosing clinically equivocal lesions suspicious for basal cell carcinoma (BCC) is limited by an OCT assessor shortage. Cumulative sum (CUSUM) analysis enables monitoring of diagnostic performance during training. The objective was to evaluate whether CUSUM-integrated e-learning is suitable for training healthcare professionals in achieving and maintaining an acceptable error rate for differentiating BCC from non-BCC lesions on OCT. Furthermore, we explored the diagnostic accuracy of high-confidence BCC diagnoses by the newly trained assessors. METHODS: A CUSUM-integrated e-learning was developed. Trainee performance was monitored by CUSUM analysis. The number of OCT scans required to achieve and maintain a predefined acceptable error rate (percentage of correct diagnoses) was evaluated. Successfully trained assessors were asked to discriminate BCC from non-BCC on 100 OCT scans for a subsequent diagnostic accuracy study. Histopathology served as the reference standard. RESULTS: Seventeen trainees successfully completed the training. Adequate performance was achieved and maintained after assessing a median of 385 scans (interquartile range [IQR]: 314-429). The pooled area under the curve (AUC) as measure for the ability to differentiate BCC from non-BCC lesions was 0.852 (95% confidence interval [CI]: 0.833-0.870). Pooled specificity and sensitivity for a high-confidence diagnosis were 95.4% (95% CI: 93.2-96.9) and 31.1% (95% CI: 24.2-39.0), respectively. CONCLUSIONS: CUSUM-integrated e-learning was successfully applied to train healthcare professionals to differentiate BCC from non-BCC on OCT. Trainees achieved a diagnostic accuracy suitable to start using OCT in clinical practice. This method can be applied to overcome the OCT assessor shortage and for training other medical skills. TRIAL REGISTRATION: Clinicaltrials.gov: NCT05634421
Updated treatment recommendation for systemic treatment:from the ESMO oncogene-addicted metastatic NSCLC Living Guideline†
No full textLongitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning
No full textBACKGROUND: Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence patterns and severity are heterogeneous and challenging to predict with established clinical and genomic markers. As a result, almost all children undergo frequent, long-term, magnetic resonance imaging (MRI) brain surveillance regardless of individual recurrence risk. Longitudinal deep-learning analysis of serial MRI scans may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers, but, thus far, progress has been limited by data availability and current machine-learning approaches. METHODS: We developed a self-supervised temporal deep-learning approach tailored for longitudinal medical imaging analysis, wherein a multistep model encodes patients' serial MRI scans and is trained to classify the correct chronological order as a pretext task. The pretrained model is then fine-tuned to predict the primary end point of interest - in this case, 1-year recurrence prediction for pediatric gliomas from the point of last scan - by leveraging a patient's historical postoperative surveillance scans. We apply the model across 3994 scans from 715 patients followed at three separate institutions in the setting of pediatric low- and high-grade gliomas. RESULTS: Longitudinal imaging analysis with temporal learning improved recurrence prediction performance (F1 score) by up to 58.5% (range, 6.6 to 58.5%) compared with traditional approaches across datasets, with performance improvements in both low- and high-grade gliomas and area under the receiver operating characteristic curve of (range, 75 to 89%) across all datasets. Recurrence prediction performance increased incrementally with the number of historical scans available per patient, reaching plateaus between three and six scans, depending on the dataset. CONCLUSIONS: Temporal deep learning enables high-performing longitudinal medical imaging analysis and point-of-care decision support for pediatric brain tumors. Temporal learning may be broadly adaptable to track and predict risk in patients with other cancers and chronic diseases undergoing surveillance imaging. (Funded in part by the National Institutes of Health/National Cancer Institute (U54 CA274516 and P50 CA165962), and Botha-Chan Low Grade Glioma Consortium.)
Zicht op jeugdcriminaliteit:Een verdiepende probleemanalyse ter ondersteuning van PmG Maastricht
No full textEssays on economic complexity, international trade and minerals economics
No full textThis PhD thesis offers a critical and innovative contribution to the field of economic complexity by addressing its conceptual ambiguities, methodological limitations, and empirical challenges. Economic complexity, though widely cited, has lacked a clear definition, robust theoretical foundations, and appropriate data for measuring its core idea—local capabilities. The work engages with various strands of the literature, from complexity economics and trade theory to network analysis and global value chains, in order to redefine and operationalize economic complexity in a more coherent and meaningful way. In a nutshell, Chapter 2 identifies four main gaps of economic complexity literature and proposes a novel framework to rethink economic complexity. Chapter 3 digs into the gap related to the mismatch between the intuition of economic complexity as a proxy of local capabilities and the data employed for its estimation (gross export data). Chapter 4 incorporates a sectoral economic complexity index into a trade-in-task model, testing whether complexity affects vertical specialization across global value chains. Chapter 5 uses economic complexity techniques to estimate the criticality level of minerals and the competitiveness level of countries producing them
