Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination.

PURPOSE: The aim of this study is to assess the potential of quantitative image analysis and machine learning techniques to differentiate between malignant lymph nodes and benign lymph nodes affected by reactive changes due to COVID-19 vaccination. METHOD: In this institutional review board-approved retrospective study, we improved our previously published artificial intelligence model, by retraining it with newly collected images and testing its performance on images containing benign lymph nodes affected by COVID-19 vaccination. All the images were acquired and selected by specialized breast-imaging radiologists and the nature of each node (benign or malignant) was assessed through a strict clinical protocol using ultrasound-guided biopsies. RESULTS: A total of 180 new images from 154 different patients were recruited: 71 images (10 cases and 61 controls) were used to retrain the old model and 109 images (36 cases and 73 controls) were used to evaluate its performance. The achieved accuracy of the proposed method was 92.7% with 77.8% sensitivity and 100% specificity at the optimal cut-off point. In comparison, the visual node inspection made by radiologists from ultrasound images reached 69.7% accuracy with 41.7% sensitivity and 83.6% specificity. CONCLUSIONS: The results obtained in this study show the potential of the proposed techniques to differentiate between malignant lymph nodes and benign nodes affected by reactive changes due to COVID-19 vaccination. These techniques could be useful to non-invasively diagnose lymph node status in patients with suspicious reactive nodes, although larger multicenter studies are needed to confirm and validate the results.

Towards 3D Multi-Layer Scaffolds for Periodontal Tissue Engineering Applications: Addressing Manufacturing and Architectural Challenges.

Reduced periodontal support, deriving from chronic inflammatory conditions, such as periodontitis, is one of the main causes of tooth loss. The use of dental implants for the replacement of missing teeth has attracted growing interest as a standard procedure in clinical practice. However, adequate bone volume and soft tissue augmentation at the site of the implant are important prerequisites for successful implant positioning as well as proper functional and aesthetic reconstruction of patients. Three-dimensional (3D) scaffolds have greatly contributed to solve most of the challenges that traditional solutions (i.e., autografts, allografts and xenografts) posed. Nevertheless, mimicking the complex architecture and functionality of the periodontal tissue represents still a great challenge. In this study, a porous poly(ε-caprolactone) (PCL) and Sr-doped nano hydroxyapatite (Sr-nHA) with a multi-layer structure was produced via a single-step additive manufacturing (AM) process, as a potential strategy for hard periodontal tissue regeneration. Physicochemical characterization was conducted in order to evaluate the overall scaffold architecture, topography, as well as porosity with respect to the original CAD model. Furthermore, compressive tests were performed to assess the mechanical properties of the resulting multi-layer structure. Finally, in vitro biological performance, in terms of biocompatibility and osteogenic potential, was evaluated by using human osteosarcoma cells. The manufacturing route used in this work revealed a highly versatile method to fabricate 3D multi-layer scaffolds with porosity levels as well as mechanical properties within the range of dentoalveolar bone tissue. Moreover, the single step process allowed the achievement of an excellent integrity among the different layers of the scaffold. In vitro tests suggested the promising role of the ceramic phase within the polymeric matrix towards bone mineralization processes. Overall, the results of this study demonstrate that the approach undertaken may serve as a platform for future advances in 3D multi-layer and patient-specific strategies that may better address complex periodontal tissue defects.