Big data identifies triple-negative breast, oropharyngeal, and lung cancers
Big data analytics predicted if a patient is suffering from aggressive triple-negative breast cancer, slower-moving cancers, or noncancerous lesions with 95% accuracy.
If the tiny patterns that the researchers found in magnetic resonance imaging (MRI) scans prove consistent in further studies, the technique may enable doctors to use an MRI scan to diagnose more aggressive cancers earlier and fast track these patients for therapy. The work, led by researchers at Case Western Reserve University in Cleveland, Ohio, was published in Radiology (2014; doi: 10.1148/radiol.14121031).
"Literally, what we're trying to do is squeeze out the information we're not able to see just by looking at an image," said Anant Madabhushi, PhD, of Case School of Engineering.
For this breast cancer study, the research team analyzed MRI images of breast lesions from 65 women. The researchers sifted through hundreds of gigabytes of image data from each patient to try to find differences that distinguish the different subtypes of breast cancers from each other.
Looking at images enhanced with contrasting agents, they discovered that different textures are reflected from triple-negative cancer, benign fibroadenoma that is commonly mistaken for triple-negative, and two other common types of breast cancer—estrogen-receptor positive (ER+) and human epidermal growth factor receptor 2-postive (HER2+).
The scientists mathematically modeled the textures that appear as the tissues absorb contrast-enhancing dye. The model revealed that changes over just milliseconds distinguished triple-negative from benign lesions. The investigators used machine learning and pattern recognition to aid in diagnoses among the three types of cancers. The diagnoses were based on texture changes and other quantitative evidence.
"Today, if a woman or her doctor finds a lump, she gets a mammogram and then a biopsy for molecular analysis, which can take 2 weeks or up to a month," Madabhushi said. "If we can predict the cancer is triple-negative, we can fast track the patient for biopsy and treatment. Especially in cases with triple-negative cancer, 2 to 4 weeks saved can be crucial."
For the three types of cancers, the early diagnosis would enable quick and personalized treatments. ER+ and HER2+ respond to different therapies. An MRI could also become a regular screening device for women who have family histories of these cancers.
Using much the same science, Madabhushi and fellow researchers from Washington University in St. Louis, Missouri, developed a way to distinguish between recurrent and treatable forms of human papillomavirus-related oropharyngeal squamous cell carcinoma. That work was published in the American Journal of Surgical Pathology (2014; doi:10.1097/PAS.0000000000000086).
"Most sufferers tend to have good outcomes, but a small subset—about 10%—doesn't," Madabhushi said. "There's nothing out there to predict which. We developed an algorithm and found patterns that allowed us to distinguish between the two with 80% to 90% accuracy."
"Personalized medicine is possible using this," Madabhushi said. "Using biopsy specimens, pathologists can't tell one from the other, but big data analytics can."