- Cancer has established itself as one of the main causes of mortality in the world and in 2020 alone it caused 10 million deaths.
- One of the biggest drawbacks is that most cases are identified in advanced stages.
- Immunotherapies are indicated to treat various tumors, although not all of them work for all types of patients.
The development of technology offers multiple benefits to the field of health. In the first place is the development of new devices that have the objective of strengthening medical care. But it is also useful for analyzing large amounts of data in seconds and thus being able to identify the possible performance of treatments such as immunotherapies.
The most recent case was developed by researchers from the Houston Methodist Hospital. This is a mathematical model to predict how certain types of cancer will respond to immunotherapy treatments. This improves the chances of successful treatment from a wide variety of drug combinations against tumors.
How does it work?
The mathematical model uses a system of equations based on the laws of physics and chemistry to describe the biological systems involved in immunotherapy treatment and the associated immune response.
This innovation was designed by the Dr. Zhihui Wang and Dr. Vittorio Cristinias well as other health professional colleagues at Houston Methodist Hospital, with the goal of predict the immune response using only data that is already measured in cancer patients. The model establishes a framework for designing individual treatment strategies in a step towards the future of personalized medicine.
“With our model we try to predict the results of immunotherapy for patients. It is a mechanical model, but for which we require images and patient data. We get CT or MRI data from tumors before, during, and after immunotherapy, then we get specific numerical measures of therapeutic response,” explained Dr. Zhihui Wang, research associate professor of mathematics of medicine at Houston Methodist Hospital.
Main findings identified
During the research to implement this mathematical model, two measures were discovered: those that quantify the presence and health of the immune presence within the tumor and those that give the resulting rate of cancer cell death by immune cells activated by immunotherapy, could be combined into a single measure.
That was highly correlated with long-term tumor burden, thus providing a single numerical score of that cancer’s response to the specific drug. These results were further validated with data from an additional 177 patients treated with one of the most common checkpoint inhibitor immunotherapies (anti-CTLA4 or anti-PD1/PDL1 monotherapies).
The mathematical model can be implemented immediately in clinical medicine, without the need for new technology, personnel or extensive training. In addition, methods are currently being investigated to use other clinical measures to improve the accuracy and precision of model-based predictions, such as data from blood samples or tumor biopsies.
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