Estimating how long a cancer patient may survive is imprecise, but a new statistical method as been developed to accomplish this. Researchers also say this tool should help them evaluate the success of treatments as well.

The new method, known as SURVIV (Survival analysis of mRNA Isoform Variation), outperformed a current method that employs genetic sequencing data to predict patient survival time.

These simulation tests were carried out for six different cancer types involved different types of malignancies that affect the breast, brain, kidneys, lungs and the ovaries.

Researchers from the University of California in Los Angeles spent more than two years developing the SURVIV algorithm, using tissue samples from 2,684 patients.  They then compared the survival time estimated by the algorithm with the amount of time the patients had survived. The group also included some patients who were still living.

The team is now applying the SURVIV algorithm to much larger datasets across many more types of cancers to develop more reliable predictors of patient survival.

The hope is that the algorithm, which should be ready in one-to-three years, can be used to help guide decision-making in evaluating the potential success of different types of cancer treatment, the researchers say of their study, which appears in Nature Communications.