Survivors of childhood cancer need lifelong monitoring for side effects from radiotherapy. However, longitudinal data from routine monitoring is often infrequently and irregularly sampled, and subject to inaccuracies. Due to this, measurements are often studied in isolation, or simple relationships (e.g., linear) are used to impute missing timepoints. In this study, we investigated the potential role of Gaussian Processes (GP) modelling to make population-based and individual predictions, using insulin-like growth factor 1 (IGF-1) measurements as a test case. With training data of 23 patients with a median (range) of 4 (1-16) timepoints we identified a trend within the range of literature reported values. In addition, with 8 test cases, individual predictions were made with an average root mean squared error of 31.9 (10.1 - 62.3) ng/ml and 27.4 (0.02 - 66.1) ng/ml for two approaches. GP modelling may overcome limitations of routine longitudinal data and facilitate analysis of late effects of radiotherapy.

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification.

Dr Pallavi Tiwari and her team at the Brain Image Computing Laboratory are using AI models to help move away from a one-size-fits-all approach to treating brain tumours. As technology continues to advance, its use within the healthcare sector is becoming more and more prominent. From remote healthcare to 3D printing, there are so many applications of technology that can pave the way for personalised medicine and better healthcare. One such application is the use of AI and machine learning in the treatment of brain tumours, something I spoke to Dr Pallavi Tiwari about. Tiwari is an assistant professor of biomedical engineering and the director of the Brain Image Computing Laboratory at Case Western Reserve University in Ohio.

Washington [US], August 14 (ANI): Researchers have developed a deep learning model that is capable of classifying a brain tumour as one of six common types, using a single 3D MRI scan, during a new study. The study by researchers from the Washington University School of Medicine has been published in Radiology: Artificial Intelligence. "This is the first study to address the most common intracranial tumours and to directly determine the tumour class or the absence of tumour from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, PhD, and Daniel Marcus, PhD, in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri. The six most common intracranial tumour types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of suspected cancer and examining it under a microscope.

In the first study of its kind in cancer, researchers have applied artificial intelligence to measure the amount of muscle in patients with brain tumours to help improve prognosis and treatment. Dr. Ella Mi, a clinical research fellow at Imperial College London (UK) will tell the NCRI Virtual Showcase, that using deep learning to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker. Furthermore, her research showed that the amount of muscle measured in this way could be used to predict how long a patient might survive their disease as it was an indicator of a patient's overall condition. Glioblastoma is an aggressive brain tumour that is very difficult to treat successfully. Average survival after diagnosis is 12-18 months and fewer than 5% of patients are still alive after five years.

In the first study of its kind in cancer, researchers have applied artificial intelligence to measure the amount of muscle in patients with brain tumours to help improve prognosis and treatment. Dr Ella Mi, a clinical research fellow at Imperial College London (UK) told the NCRI Virtual Showcase, that using deep learning to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker. Furthermore, her research showed that the amount of muscle measured in this way could be used to predict how long a patient might survive their disease as it was an indicator of a patient's overall condition. Glioblastoma is an aggressive brain tumour that is very difficult to treat successfully. Average survival after diagnosis is 12-18 months and fewer than 5% of patients are still alive after five years.

Scientists at New York University have developed an artificial intelligence (AI) that can diagnose brain tumours faster and more accurately than human doctors. First, the researchers used an advanced form of imaging called stimulated Raman histology (SRH) that uses lasers to highlight areas of the brain that would not usually be visible in a scan. These images were then processed and analysed by an artificially intelligent system, which generated an accurate brain tumour diagnosis in less than 150 seconds. According to the research, which was published in the journal Nature Medicine, the AI-based diagnosis was 94.6 per cent accurate, compared to 93.9 per cent for human doctors. "As surgeons, we're limited to acting on what we can see; this technology allows us to see what would otherwise be invisible, to improve speed and accuracy in the OR [operating room], and reduce the risk of misdiagnosis," said senior author Dr Daniel A Orringer, associate professor of neurosurgery at NYU Grossman School of Medicine, who co-led the study.

Artificial Intelligence (AI) based on a combination of deep-learning algorithms and laser-imaging technology can be utilised to examine brain tissue and detect a brain tumour in near real-time according to a study published in Nature Medicine Journal on Monday. This recent AI technique can be a game-changer in intra-operative brain tumour diagnostics according to reports. The method is a combination of "Raman histology (SRH), a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion," the study said. The AI method is also much faster. The neural networks have been trained using over 2.5 million SRH images to identify brain tumours using brain tissue in under 150 seconds, according to the report.

The AI analyses high-resolution images of tumours produced using a method called stimulated Raman histology (SRH). Todd Hollon at the University of Michigan and his colleagues generated more than 2 million SRH images of brain tumours from 415 people with known diagnoses. Each image showed a small region of an excised tumour and was labelled with which type of brain tumour it was out of the 10 most common types. The team fed them all to the AI so it could learn from the images to identify tissue features linked to these specific types of cancer. The images had either come from biopsies that remove a small sample of a suspected tumour for analysis or from surgeries to remove tumours.

Artificial Intelligence is now being used to detect cancer in a pioneering procedure. A new blood test uses AI to quickly scan for brain tumours with 90 per cent accuracy. Scientist hope that this new diagnostic tool could be used by the NHS and hospitals worldwide. Brain tumours are hard to detect and cause symptoms that can be confused with other maladies. These ambiguous symptoms include headaches, memory loss and vision problems, with a scan being the only way to detect the cancerous cells.