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Background Sensitive and reproducible detection of residual disease after treatment is a major challenge for patients with locally advanced head and neck cancer. Indeed, the current imaging techniques are not always reliable enough to determine the presence of residual disease. The aim of the NeckTAR trial is to assess the ability of circulating DNA (cDNA), both tumoral and viral, at three months post-treatment, to predict residual disease, at the time of the neck dissection, among patients with partial cervical lymph node response on PET-CT, after potentiated radiotherapy. Methods This will be an interventional, multicentre, single-arm, open-label, prospective study. A blood sample will be screened for cDNA before potentiated radiotherapy and after 3 months if adenomegaly persists on the CT scan 3 months after the end of treatment. Patients will be enrolled in 4 sites in France. Evaluable patients, i.e. those with presence of cDNA at inclusion, an indication for neck dissection, and a blood sample at M3, will be followed for 30 months. Thirty-two evaluable patients are expected to be recruited in the study. Discussion The decision to perform neck dissection in case of persistent cervical adenopathy after radio-chemotherapy for locally advanced head and neck cancer is not always straightforward. Although studies have shown that circulating tumour DNA is detectable in a large proportion of patients with head and neck cancer, enabling the monitoring of response, the current data is insufficient to allow routine use of this marker. Our study could lead to better identification of patients who do not have residual lymph node disease in order to avoid neck dissection and preserve their quality-of-life while maintaining their prospects of survival. Trial registration Clinicaltrials.gov: NCT05710679, registered on 02/02/2023, https://clinicaltrials.gov/ct2/show/ . Identifier with the French National Agency for the Safety of Medicines and Health Products (ANSM): N°ID RCB 2022-A01668-35, registered on July 15 th , 2022.

Purpose Extent of lung contusion on initial computed tomography (CT) scan predicts the occurrence of acute respiratory distress syndrome (ARDS) in blunt chest trauma patients. We hypothesized that lung ultrasonography (LUS) on admission could also predict subsequent ARDS. Methods Forty-five blunt trauma patients were prospectively studied. Clinical examination, chest radiography, and LUS were performed on arrival at the emergency room. Lung contusion extent was quantified using a LUS score and compared to CT scan measurements. The ability of the LUS score to predict ARDS was tested using the area under the receiver operating characteristic curve (AUC-ROC). The diagnostic accuracy of LUS was compared to that of combined clinical examination and chest radiography for pneumothorax, lung contusion, and hemothorax, with thoracic CT scan as reference. Results Lung contusion extent assessed by LUS on admission was predictive of the occurrence of ARDS within 72 h (AUC-ROC = 0.78 [95 % CI 0.64–0.92]). The extent of lung contusion on LUS correlated well with CT scan measurements (Spearman’s coefficient = 0.82). A LUS score of 6 out of 16 was the best threshold to predict ARDS, with a 58 % [95 % CI 36–77] sensitivity and a 96 % [95 % CI 76–100] specificity. The diagnostic accuracy of LUS was higher than that of combined clinical examination and chest radiography: (AUC-ROC) 0.81 [95 % CI 0.50–1.00] vs. 0.74 [0.48–1.00] ( p  = 0.24) for pneumothorax, 0.88 [0.76–1.00] vs. 0.69 [0.47–0.92] ( p  < 0.05) for lung contusion, and 0.84 [0.59–1.00] vs. 0.73 [0.51–0.94] ( p  < 0.05) for hemothorax. Conclusions LUS on admission identifies patients at risk of developing ARDS after blunt trauma. In addition, LUS allows rapid and accurate diagnosis of common traumatic thoracic injuries.

PurposeTo compare, vs CMR, four softwares: quantitative gated SPECT (QGS), myometrix (MX), corridor 4DM (4DM), and Emory toolbox (ECTb) to evaluate left ventricular ejection fraction (LVEF), end-systolic (ESV), and end-diastolic volumes (EDVs) by gated MPI CZT-SPECT.Methods48 patients underwent MPI CZT-SPECT and CMR 6 weeks after STEMI, LV parameters were measured with four softwares at MPI CZT-SPECT vs CMR. We evaluated (i) concordance and correlation between MPI CZT-SPECT and CMR, (ii) concordance MPI CZT-SPECT/CMR for the categorical evaluation of the left ventricular dysfunction, and (iii) impacts of perfusion defects > 3 segments on concordance.ResultsLVEF: LCC QGS/CMR = 0.81 [+ 2.2% (± 18%)], LCC MX/CMR = 0.83 [+ 1% (± 17.5%)], LCC 4DM/CMR = 0.73 [+ 3.9% (± 21%)], LCC ECTb/CMR = 0.69 [+ 6.6% (± 21.1%)]. ESV: LCC QGS/CMR = 0.90 [− 8 mL (± 40 mL)], LCC MX/CMR = 0.90 [− 9 mL (± 36 mL)], LCC 4DM/CMR = 0.89 [+ 4 mL (± 45 mL)], LCC ECTb/CMR = 0.87 [− 3 mL (± 45 mL)]. EDV: LCC QGS/CMR = 0.70 [− 16 mL (± 67 mL)], LCC MX/CMR = 0.68 [− 21 mL (± 63 mL], LCC 4DM/CMR = 0.72 [+ 9 mL (± 73 mL)], LCC ECTb/CMR = 0.69 [+ 10 mL (± 70 mL)].ConclusionQGS and MX were the two best-performing softwares to evaluate LVEF after recent STEMI.

Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms. Methods: This retrospective study included 140 patients who underwent [ 18 F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning. Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort. Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

Purpose: The objective of this study was to evaluate periarticular FDG uptake scores from 18F-FDG-PET/CT to identify polymyalgia rheumatica (PMR) within a population presenting rheumatic diseases.Methods: A French retrospective study from 2011 to 2015 was conducted. Patients who underwent 18F-FDG-PET/CT for diagnosis or follow-up of a rheumatism or an unexplained biological inflammatory syndrome were included. Clinical data and final diagnosis were reviewed. Seventeen periarticular sites were sorted by a visual reading enabling us to calculate two scores: mean FDG visual uptake score, number of sites with significant uptake same as that or higher than liver uptake intensity and by a semi-quantitative analysis using mean maximum standardized uptake value (SUVmax). Optimal cutoffs of visual score and SUVmax to diagnose PMR were determined using receiver operating characteristics curves.Results: Among 222 18F-FDG PET/CT selected for 215 patients, 161 18F-FDG PET/CT were performed in patients who presented inflammatory rheumatism as a final diagnosis (of whom 57 PMR). The presence of at least three sites with significant uptake identified PMR with a sensitivity of 86% and a specificity of 85.5% (AUC 0.872, 95% CI [0.81–0.93]). The mean FDG visual score cutoff to diagnose a PMR was 0.765 with a sensitivity of 82.5% and a specificity of 75.8% (AUC 0.854; 95% CI [0.80–0.91]). The mean SUVmax cutoff to diagnose PMR was 2.168 with a sensitivity of 77.2% and a specificity of 77.6% (AUC 0.842; 95% CI [0.79–0.89]).Conclusions: This study suggests that 18F-FDG PET/CT had good performances to identify PMR within a population presenting rheumatic diseases.

Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms. Methods: This retrospective study included 140 patients who underwent [ 18 F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning. Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort. Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms. Methods: This retrospective study included 140 patients who underwent [18F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning. Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort. Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms. Methods: This retrospective study included 140 patients who underwent [18F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning. Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort. Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

Slow acquisition time and instrument stability are the two major limitations for the application of confocal Brillouin microscopy to biological materials. Although overlooked, coupling the microscope to the spectrometer with a multimode fiber (MMF) is a simple yet viable solution to increase both the detection efficiency and the stability of the classical single-mode fiber-coupled virtually imaged phase array (VIPA) instruments. Here we implement the first successful MMF-coupled VIPA spectrometer for confocal Brillouin applications and present a dimensioning strategy to optimize its collected power. The use of an MMF brings a tremendous improvement on the stability of the spectrometer that allows performing experiments over several weeks without realignment of the device. For instance, we map the Brillouin shift and linewidth in growing ductal and acinar organoids with a spatial resolution of 1 × 1 × 6 μ m 3 and 50 ms dwell time. Our results clearly reveal the formation of a lumen in these organoids. Careful examination of the data also suggests an increase in the viscosity of the cells of the assembly.

Background In this prospective phase 2 trial, we assessed the efficacy of trastuzumab-emtansine (T-DM1) in HER2-negative metastatic breast cancer (MBC) patients with HER2-positive CTC. Methods Main inclusion criteria for screening were as follows: women with HER2-negative MBC treated with ≥ 2 prior lines of chemotherapy and measurable disease. CTC with a HER2 /CEP17 ratio of ≥ 2.2 by fluorescent in situ hybridization (CellSearch) were considered to be HER2 -amplified ( HER2 amp ). Patients with ≥ 1 HER2 amp CTC were eligible for the treatment phase (T-DM1 monotherapy). The primary endpoint was the overall response rate. Results In 154 screened patients, ≥ 1 and ≥ 5 CTC/7.5 ml of blood were detected in N  = 118 (78.7%) and N  = 86 (57.3%) patients, respectively. ≥1 HER2 amp CTC was found in 14 patients (9.1% of patients with ≥ 1 CTC/7.5 ml). Among 11 patients treated with T-DM1, one achieved a confirmed partial response. Four patients had a stable disease as best response. Median PFS was 4.8 months while median OS was 9.5 months. Conclusions CTC with HER2 amplification can be detected in a limited subset of HER2-negative MBC patients. Treatment with T-DM1 achieved a partial response in only one patient. Trial registration NCT01975142 , Registered 03 November 2013

The present study investigates, experimentally and numerically, the tensile behavior of copper-clad aluminum composite wires. Two fiber-matrix configurations, the conventional Al-core/Cu-case and a so-called architectured wire with a continuous copper network across the cross-section, were considered. Two different fiber arrangements with 61 or 22 aluminum fibers were employed for the architectured samples. Experimentally, tensile tests on the two types of composites show that the flow stress of architectured configurations is markedly higher than that of the linear rule of mixtures’ prediction. Transverse stress components and processing-induced residual stresses are then studied via numerical simulations to assess their potential effect on this enhanced strength. A set of elastic-domain and elastoplastic simulations were performed to account for the influence of Young’s modulus and volume fraction of each phase on the magnitude of transverse stresses and how theses stresses contribute to the axial stress-strain behavior. Besides, residual stress fields of different magnitude with literature-based distributions expected for cold-drawn wires were defined. The findings suggest that the improved yield strength of architectured Cu-Al wires cannot be attributed to the weak transverse stresses developed during tensile testing, while there are compelling implications regarding the strengthening effect originating from the residual stress profile. Finally, the results are discussed and concluded with a focus on the role of architecture and residual stresses.