Early Detection of Treatment Response in Lung Cancer Using Delta-Radiomics Features
The study conducted by Heidelberg University Hospital investigates the use of diffusion-weighted MRI (DWI) to predict early treatment outcomes in patients with advanced lung adenocarcinoma. The…
Heidelberg University Hospital: Delta-Radiomics Features from ADC Maps as Early Predictors of Treatment Success in Lung Cancer Therapy
In this prospective study conducted by Heidelberg University Hospital, researchers investigated whether changes in radiomic features from diffusion-weighted MRIs (DWI) could provide early…
Healthcare on FHIR: Igniting the Potential of Interoperability
Interoperability plays a crucial role in healthcare: it enables seamless communication of patient information across different systems, leads to significant benefits throughout the healthcare system…
Optimizing Glioblastoma Imaging: Enhancing MRI Efficiency and Quality with Deep Learning
This study investigates the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients, aiming to reduce scan time and improve image quality. Glioblastomas are aggressive brain…
University Hospital Tübingen: Advancing MRI Efficiency in Glioblastoma Care with Deep Learning
This study explores the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients. Glioblastomas, known for being the most prevalent and most aggressive malignant brain tumors in…
Insights into the BORN Project: Development and Successes with Dr. Maurice Heimer
The BORN-project of the Bavarian Center for Cancer Research (BZKF) is making swift progress in its second funding phase. A key aspect of the project is the development of structured reporting…