"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.

The researchers trained a convolutional neural network - a type of deep learning or artificial intelligence model - to analyse the scans, and to identify and quantify the cross-sectional area (CSA) of the temporalis muscle at its thickest part. Then they looked at the association of CSA with overall survival and how long patients lived without their disease progressing (progression-free survival).


They found that CSA was a significant predictor of overall and progression-free survival. Patients with a high CSA, indicating more muscle, had half the risk of dying and a two-thirds reduction in risk of the disease progressing at any given time compared to patients with a low CSA, indicating a reduced amount of muscle. The effect of CSA on overall and progression-free survival was particularly strong in patients under the age of 55 years and men.

Dr Mi said: "The average overall survival for patients in the high CSA group was 21.3 months compared to 14 months for patients in the low CSA group. Recently, we have taken the work forward to take into account important factors such as age, sex, which side of the brain the tumour was on and a genetic characteristic of brain tumours that often predicts response to chemotherapy. We have found that patients with high CSA had around 60% reduction in risk of death and 75% reduction in risk of disease progression compared to patients with low CSA, even when all these other factors are accounted for.

"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."

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