Dreierestrada1649

This is a prospective and comparative study including 76 consecutive patients performing EUS-FNB for pancreatic and extrapancreatic solid lesions, randomized by alternate allocation to macroscopic on-site evaluation (MOSE) (40 patients) or to a conventional technique (40 patients), with three passes each. MOSE samples were differentiated into score 0 no visible material, score 1 only necrotic or haematic material, score 2 white core tissue ≤ 2 mm, or score 3 white core tissue > 2 mm. The conventional technique consisted in pushing all the needle content into a test tube for evaluation by the pathologist. In both groups, a 22-25 Gauge Franseen-tip needle (Acquire, Boston Scientific Co., Natick, MA, USA) was used. The study evaluated the diagnostic accuracy and adequacy of MOSE compared to the conventional technique and whether MOSE could optimize the number of passes during EUS-FNB. Results The analysis was performed on 76 patients (38 MOSE, 38 conventional). The overall diagnostic adequacy was 94.7% (72/76) aotentially reduce the number of passes.Ultrasound (US)-based measurements of the inferior vena cava (IVC) diameter are widely used to estimate right atrial pressure (RAP) in a variety of clinical settings. However, the correlation with invasively measured RAP along with the reproducibility of US-based IVC measurements is modest at best. In the present manuscript, we discuss the limitations of the current technique to estimate RAP through IVC US assessment and present a new promising tool developed by our research group, the automated IVC edge-to-edge tracking system, which has the potential to improve RAP assessment by transforming the current categorical classification (low, normal, high RAP) in a continuous and precise RAP estimation technique. Finally, we critically evaluate all the clinical settings in which this new tool could improve current practice.Recent studies have focused on the development of total-body PET scanning in a variety of fields such as clinical oncology, cardiology, personalized medicine, drug development and toxicology, and inflammatory/infectious disease. Given its ultrahigh detection sensitivity, enhanced temporal resolution, and long scan range (1940 mm), total-body PET scanning can not only image faster than traditional techniques with less administered radioactivity but also perform total-body dynamic acquisition at a longer delayed time point. These unique characteristics create several opportunities to improve image quality and can provide a deeper understanding regarding disease detection, diagnosis, staging/restaging, response to treatment, and prognostication. By reviewing the advantages of total-body PET scanning and discussing the potential clinical applications for this innovative technology, we can address specific issues encountered in routine clinical practice and ultimately improve patient care.Purpose Tc-99m dimercaptosuccinic acid (99mTc-DMSA) renal scan is an important tool for the assessment of childhood urinary tract infection (UTI), vesicoureteral reflux (VUR), and renal scarring. We evaluated whether a deep learning (DL) analysis of 99mTc-DMSA renal scans could predict the recurrence of UTI better than conventional clinical factors. Methods the subjects were 180 paediatric patients diagnosed with UTI, who underwent immediate post-therapeutic 99mTc-DMSA renal scans. The primary outcome was the recurrence of UTI during the follow-up period. For the DL analysis, a convolutional neural network (CNN) model was used. Age, sex, the presence of VUR, the presence of cortical defects on the 99mTc-DMSA renal scan, split renal function (SRF), and DL prediction results were used as independent factors for predicting recurrent UTI. The diagnostic accuracy for predicting recurrent UTI was statistically compared between independent factors. Results The sensitivity, specificity and accuracy for predicting recurrent UTI were 44.4%, 88.9%, and 82.2% by the presence of VUR; 44.4%, 76.5%, and 71.7% by the presence of cortical defect; 74.1%, 80.4%, and 79.4% by SRF (optimal cut-off = 45.93%); and 70.4%, 94.8%, and 91.1% by the DL prediction results. There were no significant differences in sensitivity between all independent factors (p > 0.05, for all). The specificity and accuracy of the DL prediction results were significantly higher than those of the other factors. Conclusion DL analysis of 99mTc-DMSA renal scans may be useful for predicting recurrent UTI in paediatric patients. It is an efficient supportive tool to predict poor prognosis without visually demonstrable cortical defects in 99mTc-DMSA renal scans.The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman's correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R2 metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R2 of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, Gmean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.Combination therapy with immune checkpoint inhibitors and cytotoxic chemotherapies (chemoimmunotherapy) is associated with significantly better survival outcomes than cytotoxic chemotherapies alone in patients with advanced non-small cell lung cancer (NSCLC). However, there are no prognostic markers for chemoimmunotherapy. The prognostic nutritional index (PNI) and lung immune prognostic index (LIPI) are prognostic biomarkers for immune checkpoint inhibitor (ICI) monotherapy or cytotoxic chemotherapies. click here Thus, we aimed to examine whether these factors could also be prognostic markers for chemoimmunotherapy. We retrospectively examined 237 patients with advanced NSCLC treated with chemoimmunotherapy. In the total group, the median overall survival (OS) was not reached, and the median progression-free survival (PFS) was 8.6 months. Multivariate analysis of OS and PFS revealed significant differences based on PNI and LIPI. Programmed cell death ligand 1 (PD-L1) was also significantly associated with OS and PFS. PNI and a PD-L1 tumor proportion score (TPS) of less then 50% and poor LIPI (regardless of PD-L1 TPS) were associated with poor prognosis. PNI and LIPI predicted survival outcomes in patients with advanced NSCLC treated with chemoimmunotherapy, especially in patients with PD-L1 TPS less then 50%. For patients in this poor category, chemoimmunotherapy may result in a worse prognosis than expected.

To qualitatively and quantitatively review the reliability of palatal rugae as a tool for personal identification following orthodontic treatment.

Cross-sectional retrospective studies assessing the accuracy of matching palatal rugae pattern pre- and post-orthodontic treatment were identified from PubMed and SCOPUS databases. The title and abstract of the articles identified in the search were screened for potential duplicates and relevancy to the topic of interest. The full text of the articles selected in the screening was analyzed using the inclusion and exclusion criteria. Quantitative analysis of the studies representing coherent data in terms of age and treatment choice was performed using RevMan software.

Out of 64 screened articles, only 18 articles fulfilled the eligibility criteria and were included in the systematic review. Out of these 18 articles, only 3 studies had data compatible with the quantitative analysis. Significant changes were noted in lateral first rugae in transverse bilateral direction (

= 0.02) and between second and third lateral rugae of the left side in the anteroposterior direction (

= 0.04). Despite the dimensional changes, observers in most studies were able to accurately (>90%) match the palatal rugae pre- and post-orthodontic treatment through visual observation.

The accuracy of the visual matching, despite the significant dimensional changes, indicates that morphology could have potentially been the major matching factor. Thus, a combination of dimensional and morphological evaluation of the palatal rugae could potentially increase the accuracy of personal identification.

The accuracy of the visual matching, despite the significant dimensional changes, indicates that morphology could have potentially been the major matching factor. Thus, a combination of dimensional and morphological evaluation of the palatal rugae could potentially increase the accuracy of personal identification.Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, p = 0.