A new analysis method for magnetic resonance imaging (MRI) data appears to reliably determine whether patients would need hormonal treatment only or also need chemotherapy, according to a study published in Science Reports. The analysis was found to have significant association with OncotypeDX risk scores for women with estrogen receptor-positive (ER-positive) breast cancer.1
The analysis may provide faster answers on treatment plans for women with ER-positive breast cancer, the most common type. In addition, its expected low cost may open the door to this kind of testing worldwide.
“In the United States, nearly 70% of all breast cancer patients are diagnosed with ER-positive, but the majority don’t need chemotherapy,” said Anant Madabhushi, PhD, biomedical engineering professor at Case Western Reserve University in Cleveland, Ohio, and research lead for the study.
“Until about 15 years ago, doctors had no way of telling aggressive cancer from nonaggressive, so the majority of women got chemotherapy, which can produce very harsh side effects.”
Since then, the OncotypeDX test was developed. It is a genomic test that can differentiate between aggressive and nonaggressive cancer. For the test, doctors send a biopsy sample to the company for analysis and then a risk score is assigned to guide treatment.
“The test is used frequently in the United States, but it destroys tissue, requires shipping, and costs about $4000,” explained Madabhushi. “The cost puts the test out of reach for people in middle- and low-income countries.”
Madabhushi’s team mined radiologic data from MRIs in their attempt to discern aggressive ER-positive from indolent. Their specialty is the use of big data to study disease.
They analyzed images from 96 patients with ER-positive breast cancer from a hospital in Cleveland or Boston. Each woman underwent dynamic contrast-enhanced MRI, which produces images of tissues as it takes up a contrast agent. Each woman also had the OncotypeDX genomic test.
The researchers found intensity values varied by scanner, so they turned to analyzing textural patterns. The textural patterns in the images changed based on gene expression. They quantitated the dynamic texture changes and determined which patients did or did not need chemotherapy. Their results matched OncotypeDX analyses in 85% of the cases.
“We think the dynamic texture data is robust and reliable,” Madabhushi said. “It allows us to compare apples to apples.”
Madabhushi stated that the test would costs pennies on the dollar compared to the $4000 test, though further trials are needed to validate it. Although an MRI scan is required, many doctors already prescribe that for patients with cancer, so it is not an added cost. The test would require a computer and program; no tissue would be shipped and the wait would be minutes instead of 1 to 2 weeks. This would reduce patient stress and allow treatment to begin quickly.
“With cloud computing and data warehousing, we can analyze images coming in from anywhere in the world, “Madabhushi said. “It breaks down geographic boundaries because everything is electronically transmitted.”
Madabhushi said even if the MRI test proves to make accurate predictions only for patients at extremely low or extremely high need for chemotherapy, the analysis would still serve 30% to 40% of patients.
1. Wan T, Bloch BN, Plecha D, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores [published online ahead of print February 18, 2016]. Sci Rep. doi:10.1038/srep21394.