This universal cancer blood test can detect and locate 50 types of tumors

Cancer is one of humanity’s leading killers, and the main reason for that is it’s often hard to detect until it’s too late. But that might be about to change. Researchers have developed a new type of AI-powered blood test that can accurately detect over 50 different types of cancer and even identify where it is in the body.

There are just so many types of cancer that it’s virtually impossible to keep an eye out for all of them through routine tests. Instead, the disease usually isn’t detected until doctors begin specifically looking for it, after a patient experiences symptoms. And in many cases, by then it can be too late.

Ideally, there would be routine test patients can undergo that would flag any type of cancer that may be budding in the body, giving treatment the best shot of being successful. And that’s just what the new study is working towards. The test uses a machine-learning algorithm to search for specific chemical changes to DNA, called methylation patterns, that are associated with cancer.

This is found in the form of cell-free DNA (cfDNA), which is shed into the bloodstream from many cells, including tumors. The researchers started by training a machine learning algorithm on over 3,000 blood samples in the Circulating Cell-free Genome Atlas (CCGA). Half of these had cancer – one of 50 different types – while the other half didn’t. Once the algorithm had learned what methylation patterns to look for, it was put to work on classifying a further 1,200 samples, of which half had cancer. And sure enough, the new test was largely successful, becoming more accurate for later stages of cancer.

It was able to detect 18 percent of stage I tumors, 43 percent of stage II, 81 percent of stage III and 93 percent of stage IV. It was also able to pinpoint which tissue the cancer had originated in with an accuracy of 93 percent, and importantly the false positive rate was just 0.7 percent. While the team says that the results should be generalizable to a larger population, more tests will need to be done in larger groups to further develop the test.

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This universal cancer blood test can detect and locate 50 types of tumors

Cancer is one of humanity’s leading killers, and the main reason for that is it’s often hard to detect until it’s too late. But that might be about to change. Researchers have developed a new type of AI-powered blood test that can accurately detect over 50 different types of cancer and even identify where it is in the body.

There are just so many types of cancer that it’s virtually impossible to keep an eye out for all of them through routine tests. Instead, the disease usually isn’t detected until doctors begin specifically looking for it, after a patient experiences symptoms. And in many cases, by then it can be too late.

Ideally, there would be routine test patients can undergo that would flag any type of cancer that may be budding in the body, giving treatment the best shot of being successful. And that’s just what the new study is working towards. The test uses a machine-learning algorithm to search for specific chemical changes to DNA, called methylation patterns, that are associated with cancer.

This is found in the form of cell-free DNA (cfDNA), which is shed into the bloodstream from many cells, including tumors. The researchers started by training a machine learning algorithm on over 3,000 blood samples in the Circulating Cell-free Genome Atlas (CCGA). Half of these had cancer – one of 50 different types – while the other half didn’t. Once the algorithm had learned what methylation patterns to look for, it was put to work on classifying a further 1,200 samples, of which half had cancer. And sure enough, the new test was largely successful, becoming more accurate for later stages of cancer.

It was able to detect 18 percent of stage I tumors, 43 percent of stage II, 81 percent of stage III and 93 percent of stage IV. It was also able to pinpoint which tissue the cancer had originated in with an accuracy of 93 percent, and importantly the false positive rate was just 0.7 percent. While the team says that the results should be generalizable to a larger population, more tests will need to be done in larger groups to further develop the test.

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