If they’re taking your assigned sex at birth into account, then they can still use the data because they can filter for people who were not assigned female at birth. It does require everyone to be honest, but so do most medical trials.
I hope the eventual overall results will only include people assigned female at birth rather than everyone who potentially participated in the study, and they did ask, so they can filter the data in that way.
Including a few extra people who were assigned male at birth but now identify as female is not that big a deal if they’re filtered out of the overall results - they will still be followed up, and their data could potentially be used for other studies (if they agree to their anonymised data being used for other studies, which I think is standard?)
It still misses out anyone was assigned female at birth but doesn’t identify as female. They could be a missed group for breast cancer, same as with lesbians and cervical cancer.
Did they ask anything more about medications, btw?
I wish people addressing the gender issue would remember that there are more than just transmen and transwomen. Nonbinary is a thing. And a lot of non-cisgendered people who were assigned female at birth don’t take hormones and don’t have surgery. But they also don’t identify as female.
It just seems to me that the “identifies as female” criterion might have been created with an incomplete view of who would be included or excluded.
I assume they won’t be “filtered out” but rather, “analyzed separately”.
They asked a lot of questions about hormone use, but that’s all I remember. They again asked about some health conditions i don’t have. Perhaps if I’d said i had those, they would have asked about other medications.
I think the a priori chance for a male to get breast cancer, since it’s much lower than in women, means that with the test being used right now the chance of a false positive outcome exceeds by far the chance of catching a tumor. Mammograms are in the 70% range of sensitivity (iirc, med school has been a while ), and that’s only in women older than 30. Below that age the tissue is too dense to get an accurate image by mammography. I’m not an oncologist, but I seem to recall this from my clinical decision making classes. At least in the Netherlands, ultrasound is recommended in women younger than 30 or who are pregnant, but that’s in symptomatic cases, not for screening purposes.
The radiation from repeated mammograms can also cause cancer. It’s a relatively low risk, and it is outweighed for women by the benefits of catching cancer early. For men, that calculus would be different, though.
Interesting development. Researchers (one of whom has had breast cancer) have developed AI that can read mammograms in greater detail than the human eye. This would not do away with mammograms, but could lead to more wise and strategic use.
Here are a few snippets to give you the idea, but they are by no means, the whole picture. This is a 2,800-word story. I used the share link, so it should not be paywalled.
As she and her team laid out in an article in the Journal of Clinical Oncology last month and explore further in an upcoming piece set to be published in Nature Medicine, by analyzing a mammogram’s set of byzantine pixels and then cross-referencing them with thousands of older mammograms, the AI — known as Mirai — can predict nearly half of all incidences of breast cancer up to five years before they happen.
The mammogram is a little bit like Winston Churchill’s democracy: It’s the worst screening method, except for all the others. The approach — which uses low-grade radiation to examine breast tissue from multiple viewing angles — has become the gold standard over the past several decades, and many medical professionals swear by it as an uncomfortable but important safeguard. It also has drawn its share of critics in the oncology and women’s health communities who say it has led to unnecessary radiation exposure, overtesting, false positives and all the stress that comes with them.
Many radiologists in the field are enthusiastic too. Katerina Dodelzon, Katzen’s colleague at Weill Cornell, noted the technology’s ability to take radiology “from diagnostic to prognostic” functions.
There is, of course, resistance in the medical community…
The same optimism may not yet have taken hold with breast cancer surgeons or oncologists, who most directly advise patients on breast cancer risk. Requests for comment to such doctors at four high-level hospitals were declined, and one hospital staffer described an ambivalence among that group. Mathematical models are common in cancer treatments such as chemotherapy dosages, but that is more familiar to physicians than outsourcing a prognosis to a computer.
Even some radiologists are conflicted, fearing automation could take their jobs.
While emphasizing that these technologies are meant merely as a tool for the human reader, Tobias Rijken, chief technological officer and co-founder of Kheiron Medical, also pointed to a machine’s comparative advantage in the life-or-death effort of breast cancer imaging. “An AI works 24/7, it doesn’t get tired, and it doesn’t have personal problems at home,” he said.