Research
Machine learning is getting better at predicting cancer cure rates

Researchers from The University of Texas claim to have developed a machine learning model that is 30 per cent more accurate than previous methods.
Machine learning (ML) techniques are becoming increasingly prevalent in medical settings as a way to predict survival rates and life expectancies among patients diagnosed with diseases such as cancer, heart disease, stroke and, more recently, Covid-19.
A professor and his doctoral student at The University of Texas at Arlington have published a new model of predicting survival from cancer that they say is 30 per cent more effective than previous models in predicting who will be cured of disease.
The model can help patients avoid treatments they don’t need while allowing treatment teams to focus instead on others who need additional interventions, the authors state.
“Previous studies modelling the probability of a cure, also called the cure rate, used a generalised linear model with a known parametric link function such as the logistic link function,” said principal investigator Suvra Pal, associate professor of statistics in the Department of Mathematics.
“However, this type of research doesn’t capture non-linear or complex relationships between the cure probability and important covariates, such as the age of the patient or the age of a bone marrow donor.
“Our research takes the previously tested promotion time cure model (PCM) and combines it with a supervised type of ML algorithm called a support vector machine (SVM) that is used to capture non-linear relationships between covariates and cure probability.”
Supported by a grant from the National Institute of General Medical Sciences, the new SVM-integrated PCM model (PCM-SVM) is developed in a way that builds upon a simple interpretation of covariables to predict which patients will be uncured at the end of their initial treatment and need additional medical interventions.
To test the technique, Pal and his student Wisdom Aselisewine took real survival data for patients with leukaemia, a type of blood cancer that is often treated with a bone marrow transplant.
The researchers chose the condition because it is caused by the rapid production of abnormal cancerous, white blood cells. Since this does not happen in healthy people, they were able to clearly see which patients in the historic data set were cured by treatments and which were not.
Both statistical models were tested and the newer PCM-SVM technique was found to be 30 per cent more effective at predicting who would be cured by the treatments compared to the previous technique.
“These findings clearly demonstrate the superiority of the proposed model,” Pal said.
“With our improved predictive accuracy of cure, patients with significantly high cure rates can be protected from the additional risks of high-intensity treatments. Similarly, patients with low cure rates can be recommended timely treatment so that the disease does not progress to an advanced stage for which therapeutic options are limited.
“The proposed model will play an important role in defining the optimal treatment strategy.”
News
Low doses of weight loss drugs may slow ageing

Microdoses of weight loss drugs like Ozempic could slow ageing and increase longevity, according to new research in mice.
The study found that exenatide, a drug with similar chemical make-up to Ozempic, produced molecular changes in mice that opposed typical patterns seen with ageing across multiple organs.
Scientists treated mice starting at 11 months of age with small doses of the drug for about 30 weeks, then compared tissue samples from brain, liver, kidney, muscle and fat.
Researchers from the Chinese University of Hong Kong measured levels of RNA and DNA modifications, proteins and metabolism-related molecules to assess how age-related molecular signatures had changed in each tissue.
The treated mice showed metabolic health consistent with younger animals, with their molecular “age-signature” significantly shifted to a younger-looking profile compared with untreated older mice.
Many of the drug’s positive effects appeared to involve brain activity, suggesting the brain acted as a hub influencing the ageing profiles of multiple organs throughout the body.
Exenatide and semaglutide (sold as Ozempic and Wegovy) are GLP-1 receptor agonists. These medicines mimic a naturally occurring hormone in the gut and brain that regulates appetite, helping people feel fuller for longer.
Originally developed for diabetes treatment, these drugs have surged in popularity for weight loss. A new trend has emerged online with some people reportedly taking very small doses for longevity, though health experts warn the anti-ageing effect has not been proven in humans.
“Our work has provided multifaceted evidence for a comprehensive body-wide anti-ageing strategy,” the researchers wrote. “Future longitudinal studies are necessary to explore whether GLP-1R agonism may complement other anti-ageing methods.”
The study examined multiple biological markers of ageing, including epigenetic modifications (changes to DNA that affect gene activity without altering the genetic code), protein levels and metabolic indicators across different tissues.
The findings showed consistent changes across many tissues that opposed typical ageing patterns. However, researchers emphasised several important limitations to their work.
The results were observed only in mice, not humans, meaning whether the drug has any real effect on human ageing remains unknown. The study was conducted on middle-aged mice, so the effects might not be the same in very old animals.
Additionally, while the drug appeared to induce many molecular signs of younger age across tissues, the study did not prove that actual biological ageing was reversed or that the mice lived longer.
GLP-1 receptor agonists work by binding to receptors that respond to the GLP-1 hormone. This binding triggers metabolic processes, including insulin release and appetite suppression, and potentially, as this study suggests, molecular changes linked to younger biological age.
The researchers hope their findings will lead to larger clinical trials and help in developing anti-ageing drugs. However, they stress that longitudinal studies tracking subjects over extended periods are necessary to determine whether these drugs could form part of a comprehensive anti-ageing strategy.
The growing interest in using diabetes and weight-loss drugs for longevity reflects wider trends in anti-ageing research, where scientists increasingly examine how existing medicines might have benefits for healthspan and lifespan.
Experts caution that people should not start taking these medicines for anti-ageing purposes based on animal studies alone, as human trials are needed to establish safety and efficacy for this use.
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