Publications by authors named "F H THIELE"
Objective: The purpose of this study was to evaluate the effectiveness of a deep learning model (DLM) in improving the sensitivity of neurosurgery residents to detect intracranial aneurysms on CT angiography (CTA) in patients with aneurysmal subarachnoid hemorrhage (aSAH).
Methods: In this diagnostic accuracy study, a set of 104 CTA scans of aSAH patients containing a total of 126 aneurysms were presented to three blinded neurosurgery residents (a first-year, third-year, and fifth-year resident), who individually assessed them for aneurysms. After the initial reading, the residents were given the predictions of a dedicated DLM previously established for automated detection and segmentation of intracranial aneurysms.
Lu-based targeted radionuclide therapy (TRT) has become an important cancer treatment option in recent years, in particular in the treatment of advanced prostate cancer and metastasized neuroendocrine tumors. Although it is known from conventional radiotherapy that the temporal dynamics of the dose-rate can be of relevance for tumor cell survival, the analysis of TRT efficacy usually considers only the absorbed dose. Thus, the aim of this theoretical analysis is to shed light on the possible effects of the pattern of dose-rate in TRT on tumor control probability (TCP).
View Article and Find Full Text PDFBackground & Aims: Although most hepatocellular carcinoma (HCC) cases are driven by hepatitis and cirrhosis, a subset of patients with chronic hepatitis B develop HCC in the absence of advanced liver disease, indicating the oncogenic potential of hepatitis B virus (HBV). We investigated the role of HBV transcripts and proteins on HCC development in the absence of inflammation in HBV-transgenic mice.
Methods: HBV-transgenic mice replicating HBV and expressing all HBV proteins from a single integrated 1.
Sentinel lymph node biopsy (SLNB) is increasingly incorporated in European national guidelines for the management of the clinically node-negative neck (cN0) in early-stage oral squamous cell carcinoma (OSCC). In Germany, SLNB in OSCCs is not yet routinely performed. This study aimed to evaluate the clinical outcome of SLNB in a German cohort.
View Article and Find Full Text PDFArticle Synopsis
- * The study proposes using a convolutional neural network (CNN) to automate the detection of random head motion artifacts (RHM) in T1-weighted MRI images.
- * Results showed a 95% accuracy in identifying images with significant motion artifacts and a 76% accuracy with an additional classification, demonstrating CNN's potential to enhance QA efficiency in analyzing extensive MRI datasets.