Cancers, such as malignant mesothelioma, are one of the leading causes of death. Yet mortality could be reduced by detecting malignant tumors earlier so that treatment is started earlier at a less aggressive stage. A team of American data scientists recently “cracked the code” of cancer screenings.
Cancer screenings are expensive and it can be difficult to know who and when to give screening. The tradeoff between the cost and the benefit of cancer screening is an ongoing challenge.
A new study in PLOS Computational Biology describes a new mathematical model to help clinicians decide whom to screen and when. This is a big step forward in catching some mesotheliomas earlier.
Early Diagnosis for Mesothelioma is Key
Exposure to asbestos fibers is a known risk factor for lung cancer and the cause of mesothelioma. Asbestos-related diseases can take 20 to 40 years to emerge after people have been exposed. Former asbestos workers and those exposed to products containing this carcinogen continue to be diagnosed with asbestos-caused cancers.
As researchers search for better treatments and even a cure for these diseases, they are also focusing on new diagnostic methods that might identify the cancers earlier.
Early diagnosis is particularly crucial with mesothelioma, because many patients survive only one year after they first start to show signs, and symptoms are often difficult to distinguish from those of other lung diseases.
Cracking the Code for Cancer Screening
A team of data scientists from the Dana-Farber Cancer Institute in Boston wanted to find a better way to evaluate cancer screening strategies. They wanted to create an optimal cancer screening protocol.
For most cancer types, including mesothelioma, a sensitive cancer screening is not available. Cancer screening takes time and is very expensive. And in some cases, it does not prolong patient survival.
The Boston team was determined to find a way to maximize the benefits of cancer screening. They used mathematical modeling to quantify the benefits of screening based on clinical factors. Patient age at diagnosis, rate of cancer tumor progression, and treatment outcomes were included.
Others have tried to solve this problem. But the models created here were different. These models were based on the theory of queuing networks which tracks disease progression over time.
Useful Toolbox through Simple Examples
Justin Dean, PhD of the Dana-Farber Cancer Institute in Boston states, “Our aim is to illustrate an alternative and potentially useful toolbox through simple examples.”
The Boston team of data scientists created a mathematical model for cancer screening. This model estimates the costs and benefits of cancer screening, including mesothelioma.
This new queuing approach is a potentially useful alternative to the traditional modeling approaches. This new approach provides more detailed results. It can be widely applied to estimate the costs and benefits of cancer screening strategies.
Dean, Justin, Evan Goldberg, and Franziska Michor. “Designing optimal allocations for cancer screening using queuing network models.” PLOS Computational Biology 18, no. 5 (2022): e1010179. https://doi.org/10.1371/journal.pcbi.1010179