Machine-learning tool accurately predicts cancer patient response to chemo drugs
Researchers with the Georgia Institute of Technology have developed an open source, machine-learning tool that accurately predicted how cancer patients would respond to specific chemotherapy drugs.
The tool was able to predict how individual cancer patients would respond to chemotherapy drugs with 80 percent accuracy, according to research published in Scientific Reports Nov. 6. Researchers used a data set that included the gene-expression profiles, or RNA, of individual tumors from 152 patient records and the outcome of treatments with specific drugs.
Of the 152 patient records, 114 were used to train the system, while the remaining 38 records were used to test how well the system could predict which chemotherapy drugs would perform best based on the RNA data, according to the university.
“By looking at RNA expression in tumors, we believe we can predict with high accuracy which patients are likely to respond to a particular drug,” John McDonald, Georgia Tech professor and director of its Integrated Cancer Research Center, said in a statement. “This information could be used, along with other factors, to support the decisions clinicians must make regarding chemotherapy treatment.”
Additionally, researchers stated they believe the tool’s accuracy should improve as the system analyzes more patient data.
“The accuracy of machine learning will improve not only as the amount of training data increases, but also as the diversity within that data increases,” Evan Clayton, Georgia Tech PhD student, said in a statement. “There's potential for improvement by including DNA data, demographic information and patient histories. The model will incorporate any information if it helps predict the success of specific drugs."
The tool could further help provide precision medicine in treating cancer. The software will be available as an open source for hospitals.
Danielle covers Clinical Innovation & Technology as a senior news writer for TriMed Media. Previously, she worked as a news reporter in northeast Missouri and earned a journalism degree from the University of Illinois at Urbana-Champaign. She's also a huge fan of the Chicago Cubs, Bears and Bulls.