The largest clinical trial confirms that it detects more and reduces the radiologist’s burden

With the arrival of artificial intelligence, one of the applications was undoubtedly medicinewhich could mark a authentic revolution. Although definitive proof was missing to tell us that it really had real use. And this one just arrived thanks to an article published in The Lancent which has pointed out how AI can help us detect more breast cancers and even reduces those that are much more dangerous.

The screening. Unfortunately, in Spain we have in mind, because of how recent it was, the problems with screening programs in Andalusia. And despite this great controversy, this type of screening is very useful and significantly reduces the number of women who end up dying from breast cancer that was not detected in time.

But now we want to go a little further with the integration of technology so that fewer tumors escape that to the human eye can escape due to their small size.

Interval cancers. Without a doubt, it is the great enemy in radiodiagnosis when we refer to screening mammograms. This term refers to those tumors that are detected between one check-up and the next, and that have different reasons for their appearance.

The first reason is that it is a tumor that grows very quickly (and that can be much more malignant) or that was missed in the previous control mammogram due to its small size. And this is a serious problem, since the basis of screening is to detect cancers in the earliest stages where they can respond better to more conservative treatments.

The study. The MASAI trial (Mammography Screening with Artificial Intelligence) has shown that the use of AI reduces these cases drastically. And the figures are quite promising, since there was a 12% reduction in cancer rate interval in the two years after the woman was screened. In figures, it went from 1.76 cases per 1,000 women to 1.55 cases.

A difference that may be very small in our eyes, but in public health and oncology it is a real success, since reducing by 12% the tumors that usually “escape” is a major clinical advance.

Less work. Until now the standard method to analyze these tests focused on a double reading. This means that two radiologists reviewed each mammogram independently to ensure nothing was missed. A security method that is ideal, but that consumes an immense amount of human resources in health systems.

That is why with this method a paradigm shift is proposed that is based on intelligent triage and that can be summarized in three different points:

1. The AI ​​initially analyzes the mammogram image and assigns it a risk score from 1 to 10.
2. In the event that it is categorized as low risk, the image is reviewed by a single radiologist to see if it agrees that the image is clean and closes the case.
3. If the risk is high in the mammography, the image does pass the double reading system with AI marking the most suspicious areas where there may be injury.

The result. With this new algorithm, the study has aimed at a 44% reduction in the reading letter for professionals, in order to make doctors now focus on the images that are much more doubtful.

And no, working less did not mean working worse. On the contrary: the AI ​​arm of the study detected 29% more clinically relevant cancers without increasing the rate of false positives (the great fear of over-diagnosing healthy patients).

Complement and not replace. This is something important that the study itself highlights, since they point out that AI has not arrived to fire radiologists. The MASAI method is only a “decision support”, since the AI ​​prioritizes, orders and signals, but the final clinical decision is always that of the doctor and therefore in human hands.

With the publication of these final results in The Lancet, The validation cycle of one of the most important tests is closed of the decade in radiology. The next step is no longer asking whether AI works in breast cancer screening, but how long it will take for public health systems to implement it to give radiologists one more tool that allows them to be more precise and methodical.

Images | National Cancer Institute

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