In patients with glioblastoma (brain tumor) artificial intelligence measures the amount of muscle to help improve prognosis and treatment, said researchers.
Dr Ella Mi, a clinical research fellow at Imperial College London (UK) will tell the NCRI Virtual Showcase, that using deep learning (artificial intelligence) to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker.
Dr Mi said: "Finding a better way to assess patients' physical condition, general well-being and ability to carry out everyday activities is important in glioblastoma, and indeed in many cancers, because, at present, it's often evaluated subjectively, resulting in inaccuracy and a high degree of variability depending on who is looking at it. So indicators that can be assessed objectively, such as measures of sarcopenia, are needed."
The brain scans taken from brain tumor patients, looked at the head's cross-sections. Researchers focused on the temporalis muscle. Temporalis, the broad, fan-shaped muscles on either side of the head that are used for chewing food - which has previously been identified as a good way to estimate skeletal muscle mass in the body.
"We realised that sarcopenia could be identified by quantifying muscle in cross-sectional imaging that cancer patients routinely undergo. This would allow for opportunistic screening of sarcopenia as part of cancer care without additional scanning time, radiation dose or cost," said Dr Mi.
Cross-sectional area (CSA) was a significant predictor of overall and progression-free survival. Patients with high CSA had around 60% reduction in death risk and 75% reduction in risk of disease progression compared to patients with low CSA.
Dr. Mi said: "To our knowledge, this is the first study, in any cancer, to apply deep learning to muscle segmentation and quantification for sarcopenia assessment, and to demonstrate significant associations with clinical outcomes. We are the first to show that this measurement of sarcopenia, generated automatically from routine imaging is sufficiently accurate and reliable to be a useful prognostic marker in cancer, while taking substantially less time than trained humans."
"We show that higher temporalis muscle area before surgery, chemotherapy or radiotherapy is predictive of significantly longer overall and progression-free survival. This has the potential to improve prognostic estimates and could be used to plan treatments. For instance, previous evidence has shown that frail patients might benefit from shorter courses of radiotherapy, or chemotherapy with temozolomide alone. It could also guide therapeutic interventions for muscle preservation, including nutritional support, exercise therapy and drugs."
The research by Ella Mi and her colleagues applied artificial intelligence/deep learning in cancer which is particularly aggressive and difficult to treat successfully in order to select the best treatment approach.
"At this stage their findings show that there is an association between the temporalis muscle size and a patient's frailty and how their disease progresses. The results do not show that the muscle area actually causes the change in patients' outcomes. The findings deserve study in a larger group of patients to take account of factors such as changes to the muscle caused by other factors, including surgery or radiotherapy," said Professor David Harrison, who is Professor of Pathology at the University of St Andrews and chair of the NCRI Cellular Molecular Pathology Initiative.
Glioblastoma Facts and Figures
- It is an aggressive brain tumor that is very difficult to treat.
- Average survival after diagnosis is 12-18 months.
- Accounts for 52% of all primary brain tumors.
- Occurs in adults between the ages 45 and 70.