107036 research outputs found
Sort by
100550 Atopic dermatitis severity correlation with Staphylococcus aureus as function of demographic and environmental factors [Abstract]
No full text
Lesion-level dual-tracer PET biomarkers predict prognosis in multiple myeloma treated with CXCR4-directed radiopharmaceutical therapy
No full textBackground
C-X-C motif chemokine receptor 4 (CXCR4)-directed radiopharmaceutical therapy (RPT) represents a promising option for hematologic malignancies. Nevertheless, responses in relapsed and refractory (r/r) multiple myeloma (MM) are heterogenous, emphasizing the need for optimized patient selection before RPT. Current approaches mostly rely on qualitative assessments, confirming relevant CXCR4-expression by CXCR4-directed PET in vital myeloma burden, the latter usually being determined by additional [18F]FDG-PET.
Purpose
To evaluate whether quantitative imaging biomarkers derived from dual-tracer PET/CT can enhance patient stratification and predict therapeutic response and survival following CXCR4-RPT.
Materials and methods
22 patients with r/r MM who underwent CXCR4-directed [⁶⁸Ga]Ga-PentixaFor-PET/CT and [¹⁸F]FDG-PET/CT imaging prior to CXCR4-RPT were retrospectively analyzed. A fully automated pipeline performed deep-learning–based lesion segmentation and dual-tracer fusion; batch quality control and correction ensured segmentation accuracy and concordant-lesion adjudication (> 10% volumetric overlap) prior to lesion-level feature extraction. Features included demographics, laboratory/genetic variables, and imaging metrics from FDG- and CXCR4-PET, stratified by anatomic site (medullary vs. extramedullary) and concordance (concordant vs. discordant as defined by comparing FDG- and CXCR4-positive myeloma lesions). Surface-standardized maximum inter-lesion distances (sDmax) were additionally computed. Endpoints were therapy response (responder vs. non-responder as defined by serological response assessment based on IMWG-criteria or PET/MRI based response assessment) and overall survival (OS). Group comparisons were performed using Welch’s t-test/Chi-square; survival analysis was conducted applying Kaplan–Meier estimates and log-rank tests. Decision-tree models were interpreted with SHapley Additive exPlanations (SHAP).
Results
Responders to RPT showed lower [18F]FDG-SUVmean in medullary and extramedullary concordant lesions (Welch’s p = 0.03). In the response classifier, these metrics ranked among the top predictors by SHAP, alongside selected extramedullary CXCR4-dominant discordant features and high-risk cytogenetics. Shorter OS was associated with higher TLG[FDG medullary concordant], greater sDmax[FDG], and higher CXCR4-positive medullary burden (MTV/TLC) (log-rank p < 0.05). SHAP directionality agreed with univariate trends. BMI showed a modest inverse association with early mortality in the 6-month survival model.
Conclusions
In our exploratory analysis, [¹⁸F]FDG-uptake within medullary concordant lesions was linked to both response and survival, while CXCR4-expressing medullary volume and sDmax added survival information. Concordance-aware, lesion-level quantification from dual-tracer PET/CT might present as a promising approach to aid risk stratification for CXCR4-directed RPT. However, initial findings of our hypothesis generating analysis warrant prospective validation in larger cohorts
Die psychiatrische Versorgungssituation psychisch kranker Straftäter in Haft – Ergebnisse einer Umfrage der DGPPN
No full textPaediatric Therapeutic Development Workshop on rhabdoid tumours
No full textRhabdoid tumours (RT) are malignancies of the central nervous system, kidneys, liver and soft tissues that most commonly affect very young children with survival rates below 30% in high-risk cohorts. Treatment entails surgery, intensive chemotherapy and radiotherapy, associated with substantial short- and long-term toxicities. There is an unmet need to develop targeted therapies for RT to improve patient outcomes and mitigate the toxicities of current therapy. Detailed research followed by a workshop had the objective of enabling the development of targeted therapeutics for RT. Given the inherent commonality of their biology (i.e. biallelic inactivation of SMARCB1 or more rarely SMARCA4) the therapeutic approach should be similar for intra-cranial and extra-cranial tumours. DDB1–CUL4-associated factor 5 is a promising target, and the development of small molecule binders/degraders is a priority. Enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) degraders may have greater therapeutic potential than inhibitors. Fibroblast growth factor receptor and platelet-derived growth factor receptor inhibitors may have value in subgroups. Mouse double minute 2 homologue (MDM2) is a priority target for novel therapeutic development and combination trials. Combinations of EZH2, MDM2 inhibitors and selective inhibitors of nuclear export should be evaluated robustly preclinically and drive early clinical studies
Warpage prediction for fiber reinforced injection molding via geometric feature learning and differentiable FEM
No full textInjection molding is a popular mass production process for short fiber reinforced components. One of the main production defects is warpage, unwanted deformation, resulting from the thermal history of the injected polymer and the geometry of the part. Ideally, this deformation should be predicted and compensated for before mass production to minimize defective parts. Conventional process simulation is able to predict warpage, but is too computationally expensive to be used in iterative optimization procedures. Hence, we propose a fast approximation method based on machine learning and a custom finite element solver to predict warpage in arbitrary 3D geometries with any injection location. It combines nodal geodesic values and spatial moments to capture local geometry on different scales. Neural networks then predict fiber orientation and initial strain fields for a subsequent warpage calculation in a differentiable finite element software. The latter enables training of the neural network through the solver with warpage based losses, enabling the training with observable deformation data. The approximation pipeline exhibits relative warpage errors of less than 1% on typical injection molded geometries while being six times faster than the conventional simulation. Retraining through the solver with a warpage enhanced dataset to emulate real world data leads to significant improvements in the predictive accuracy
