Promising Blood Test Can Detect 50 Types Of Cancer Using Artificial Intelligence

Early detection of cancer can have a significant impact on chances of recovery. The National

In 12 prespecified cancers, the overall detection rate was 67.3%.

Commenting on the research, Dr David Crosby, head of early detection at Cancer Research UK, who was not involved in the study, said: "Although this test is still at an early stage of development, the initial results are encouraging".

Tumour biology: The amount of ctDNA released by tumours tends to increase as they grow however some cancer types release very small amounts of ctDNA into the bloodstream, for reasons that are not entirely understood.

At its most basic, cancer is uncontrolled cell growth.

For example, the new system correctly identified 63 percent of those with stage 1 pancreatic cancer, rising to 100 percent in stage 4. Overall, for Stage I through III cases of the dozen cancers as a group, the test correctly identified the cancer 44% of the time.

The test was able to predict the tissue in which the cancer originated in 96% of samples, and it was accurate in 93%, the researchers said. They were divided into training and validation sets, and a classifier for the detection of cancer and tissue of origin was developed and validated.

Minetta Liu, research chair and professor, Department of Oncology, Mayo Clinic, and co-lead author and investigator in the CCGA study, said, "The promising results from this independent validation data set demonstrate the robustness of the test performance, including its ability to detect multiple cancer types, and its generalizability to broader populations due to a low false-positive rate". It also seems to have trouble identifying the origin of cancers caused by the human papillomavirus.

Concerns over using very sensitive technologies for early cancer detection include how we know whether the result indicates a genuine cancer or a pre-malignant condition (i.e., one that without treatment might, or might not, progress to cancer), and what the next clinical steps should be.


The team say the results are exciting as they offer the possibility of a new way to screen for cancers that are otherwise hard to detect.

"For a screening test to have clinical utility, it must have an impact on cancer-related survival, be appropriate for the population to which it is applied, have an appropriate natural history for screening, have clinically effective therapies, have tests confirmatory that are safe, have a low burden on the health system and have a clear management plan in the health system, "he explained". Instead, the disease usually isn't detected until doctors begin specifically looking for it, after a patient experiences symptoms.

GRAIL published validation data for its multi-cancer early detection blood test. That's genetic material shed by cells that circulates freely in the blood.

Both tests are designed for the early detection of multiple cancers, but they have clear differences, he noted.

More than 99% of positive results are accurate, the team says, but it will be crucial to check it does not miss cases and provide false assurance. GRAIL's test was created to minimize false positives in order to limit associated harms, including patient anxiety and unnecessary diagnostic workups. The program grouped these into like-patterns and the researchers then taught the AI which pattern reflected which type of cancer, before putting it to the test.

The test looks for tell-tale changes to the DNA of dead cancer cells that leak into the blood as diseased tissues break down. Sequencing results identifying methylated.

These are different for each type of cancer, and the criteria are now only met in the general USA population. Advanced cancers are a lot easier to detect.

In a study involving thousands of participants, a new blood test detected more than 50 types of cancer as well as their location within the body with a high degree of accuracy, according to an worldwide team of researchers led by Dana-Farber Cancer Institute and the Mayo Clinic.

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