Chinese Develop Platform Utilizing AI And Machine Learning Algorithm With Cancer Methylation Signatures To Diagnose Colorectal Cancer

Source: Thailand Medical News Jan 05, 2020 9 months ago

Thailand Medical

: Huiyan Luo et al, Circulating tumor DNA methylation pro files enable early diagnosis, prognosis prediction, and screening for colorectal cancer, Science Translational Medicine (2020).

ReferenceHuiyan Luo et al, Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer, Science Translational Medicine (2020). DOI: 10.1126/scitranslmed.aax7533

Researchers affiliated with a large number of institutions in China have developed anandplatformwith cto diagnoseIn their paper published in the journal Science Translational Medicine, the group describes their new approach to diagnosis and prognostication ofand how well it worked.At the moment, the gold standard for detectingis through the use of colonoscopies but such procedures are, unfortunately, invasive, uncomfortable and embarrassing for patients. Because of that, many people forgo testing, and sadly, some develop colorectal cancer that goes undetected for long periods of time. Refusing testing is problematic becauseis the third-most deadly kind ofworldwide.Scientists recognize the problem and continue to look for other ways to detect the disease. One promising area of research involves looking for markers of the disease in blood, which would be far less invasive. Unfortunately, such tests are still not as effective as colonoscopies. In this new effort, the researchers looked at the possibility of combining two diagnostic tools to improve accuracy.The initial diagnostic tool involved identifying-specific. The researchers did this by comparingtissue from people withand from those without, more specifically, they looked for circulating tumor DNA (ctDNA) differences. Once they had their markers, the team used ato spotin people who were at risk for developing. The algorithm was trained on cell data from 801 people who hadand from 1,021 people who did not, and learned to distinguish between cell types. Once the system had learned what to look for, the researchers tested it on patients known to haveThe researchers toldNews that the system was 87.5 percent and 89.9 percent accurate when testing for sensitivity and specificity.The oncology and genomic researchers also report that a modified prognostic model that they developed in conjunction with the diagnostic tool was found to be useful for predicting the risk of death for patients for up to 26.6 months. They also discovered that one of thethey found was particularly useful during the screening process.