Background: The accurate delineation of ablation zones (AZs) is crucial for assessing radiofrequency ablation (RFA) therapy's efficacy. Manual measurement, the current standard, is subject to variability and potential inaccuracies.
Aim: This study aims to assess the effectiveness of Artificial Intelligence (AI) in automating AZ measurements in ultrasound images and compare its accuracy with manual measurements in ultrasound images.
Over the last century, there has been a steady development of new technologies for intraoperative tissue identification and differentiation. The applications are varied, with the core purpose being to identify target structures while preserving adjacent tissue and thereby follow a general paradigm of minimally invasive medicine. Particularly in oncology, a further asset of these technologies is the identification or classification of neoplastic tissue to support and improve therapy, for example, in breast cancer surgery.
View Article and Find Full Text PDFEndoscopic submucosal dissection (ESD) was developed for the removal of benign and early malignant lesions in the gastrointestinal tract. We aimed to evaluate the performance and safety of a novel high-pressure waterjet-assisted ESD knife in colorectal applications. Six female German Landrace pigs with an average weight of 62 kg (range 60-65 kg) were used in this prospective, randomized, and controlled study.
View Article and Find Full Text PDFImmunological consequences of endoscopic ultrasound (EUS)-local thermal ablation (LTA) for pancreatic ductal adenocarcinoma (PDAC) have not been extensively assessed. We aimed to explore EUS-LTA effects on the systemic immune response in PDAC. Peripheral blood was collected from 10 treatment-naïve patients with borderline resectable and locally advanced PDAC, randomly allocated to Nab-paclitaxel plus Gemcitabine chemotherapy (CT-arm, n = 5) or EUS-LTA with HybridTherm Probe plus CT (HTP + CT-arm, n = 5).
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