Research has shown how microbubbles carrying powerful cancer drugs can be guided to the site of a tumour using antibodies.
Researchers have developed an robotic system to enhance the safety and efficacy of endoscopic submucosal dissection (ESD) for the treatment of gastrointestinal cancer.
The new 3D hydrogels provide high rates of cell proliferation, as they mimic lymph nodes, where T-cells reproduce in vivo.
Scientists have paired 3D-printed, living human brain vasculature with advanced computational flow simulations to better understand tumor cell attachment to blood vessels.
Robotic surgery for patients with early stage, oropharyngeal squamous cell cancer is associated with improved health outcomes, including better long-term survival.
Researchers have developed a machine learning model that can predict chemotherapy-associated nephrotoxicity.
Nanoengineers plan to develop an immunotherapy for ovarian cancer using 3D-bioprinted plant virus nanoparticles.
Improving the prediction of survival indicators in patients with breast cancer using tools from artificial intelligence and probabilistic modelling is the aim of ModGraProDep.
Commercially available app-based technology now makes early detection of lymphedema easier, allowing for proactive treatment.
Using a robot to treat brain aneurysms is feasible and could allow for improved precision when placing stents, coils and other devices.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.