Ученые разработали ПО для определения выживаемости пациентов с раком легких

Experts in mathematical modeling at Sechenov University have applied Oncomonitor software to forecast disease progression in patients with non-small cell lung cancer (NSCLC) depending on the stage of the disease and the types of therapy regimens used.

The new method is based on meta-analysis combined with survival models, which the program can use to make predictions, the university said.

The model takes into account the stage of the disease, the presence or absence of certain mutations or biomarkers, as well as the possibility of applying different types of therapy - from traditional surgery to combination immunotherapy. Current treatment regimens from global and Russian clinical guidelines were also integrated into the model.

This allows to make forecasts in different scenarios of the course of the disease, differing in treatment protocols, the characteristics of the studied cohort of patients and other factors, the developers said. The advantage of the methodology is that it allows quantifying the effectiveness of different drugs and treatment regimens not only in individual patient cohorts, but also in the general population.

"For example, in a study of the drug Osimertinib, its efficacy in prolonging overall survival compared to standard therapy was proven. However, in the European population, only about 15% of NSCLC patients have the mutation targeted by this drug. Accordingly, the advantage of this drug, which it showed in the clinical trial, will not be so significant in the framework of the analysis of efficacy for the total cohort of patients", - said Kirill Zhudenkov, Associate Professor at the Department of Mechanics and Mathematical Modeling of Sechenov University.

The introduction of elements of pharmacoeconomic analysis into the methodology made it possible to estimate the number of added life years for different cohorts of patients depending on various conditions.

Read more in Izvestia's exclusive story:

Looking at monitoring: software will predict survival rate of lung cancer patients