A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan.
Using a deep learning algorithm, researchers have developed a way to accurately predict which skin cancers are highly metastatic.
Scientists at have shown that diagnostic nanoparticles could be used to monitor tumor recurrence after treatment or to perform routine cancer screenings.
A tool, based on machine learning methods, that evaluates the potential contribution of all possible mutations in a gene in a given type of tumour to the development and progression of cancer.
AI tools models are a powerful tool in cancer treatment. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.
Clinicians are using patient-specific tumor 'organoid' models as a preclinical companion platform to better evaluate immunotherapy treatment for appendiceal cancer.
Researchers have found a way to enhance radiation therapy using novel iodine nanoparticles.
The MasSpec Pen has shown to accurately identify tissues and surgical margins directly in patients and differentiate healthy and cancerous tissue from banked pancreas samples.
New research could help surgeons perform liver resections with greater accuracy and deliver improved patient outcomes.
Scientists have developed a new generation of microneedles technology which allows the intradermal delivery of living cells in a minimally invasive manner.
Researchers have developed a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence.
Researchers have successfully designed and tested a system for rapid testing of large numbers of potential immunotherapy drugs.
Researchers have achieved promising results with a new cancer therapy using focused ultrasound (FUS) and ionizing radiation.
Research has shown how microbubbles carrying powerful cancer drugs can be guided to the site of a tumour using antibodies.
In the next-generation operating room interconnected sensors will collect data, analyse it in real-time and make it available to digital assistance functions.
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 shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
Researchers have developed an AI algorithm that uses computer vision to analyze tissue samples from cancer patients.
Researchers have developed a new, faster method to identify cancer stem-like cells (CSCs), which could help improve the effectiveness of cancer treatments.
A new AI approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report.
Computer scientists working with pathologists have trained an AI tool to determine which patients with lung cancer have a higher risk of their disease coming back after treatment.
Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.
Brain cancer patients in the coming years may not need to go under the knife to help doctors determine the best treatment for their tumors.
Researchers have developed a new approach to early diagnosis of lung cancer: a urine test that can detect the presence of proteins linked to the disease.
Researchers demonstrated a methodology that combines the bioprinting and imaging of glioblastoma cells in a way that more closely models what happens inside the human body.
Using a robot to treat brain aneurysms is feasible and could allow for improved precision when placing stents, coils and other devices.
Researchers have developed a tumor biosensing chip that can help determine the optimum dosage of chemotherapy required for a cancer patient.
Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,
