Cancer
Analysing multiple mammograms improves breast cancer risk prediction

A new study describes an innovative method of analysing mammograms that significantly improves the accuracy of predicting the risk of breast cancer development over the following five years.
Using up to three years of previous mammograms, the new method identified individuals at high risk of developing breast cancer 2.3 times more accurately than the standard method, which is based on questionnaires assessing clinical risk factors alone, such as age, race and family history of breast cancer.
“We are seeking ways to improve early detection, since that increases the chances of successful treatment,” said senior author Graham Colditz, associate director of Siteman Cancer Center.
“This improved prediction of risk also may help research surrounding prevention, so that we can find better ways for women who fall into the high-risk category to lower their five-year risk of developing breast cancer.”
This risk-prediction method builds on past research led by Colditz and lead author Shu Jiang, a statistician, data scientist and associate professor of surgery in the Division of Public Health Sciences at WashU Medicine.
The researchers showed that prior mammograms hold a wealth of information on early signs of breast cancer development that can’t be perceived even by a well-trained human eye. This information includes subtle changes over time in breast density, which is a measure of the relative amounts of fibrous versus fatty tissue in the breasts.
For the new study, the team built an algorithm based on artificial intelligence that can discern subtle differences in mammograms and help identify those women at highest risk of developing a new breast tumour over a specific timeframe.
In addition to breast density, their machine-learning tool considers changes in other patterns in the images, including in texture, calcification and asymmetry within the breasts.
“Our new method is able to detect subtle changes over time in repeated mammogram images that are not visible to the eye,” said Jiang, yet these changes hold rich information that can help identify high-risk individuals.
At the moment, risk-reduction options are limited and can include drugs such as tamoxifen that lower risk but may have unwanted side effects. Most of the time, women at high risk are offered more frequent screening or the option of adding another imaging method, such as an MRI, to try to identify cancer as early as possible.
“Today, we don’t have a way to know who is likely to develop breast cancer in the future based on their mammogram images,” said co-author Debbie Bennett, associate professor of radiology and chief of breast imaging for the Mallinckrodt Institute of Radiology at WashU Medicine.
“What’s so exciting about this research is that it indicates that it is possible to glean this information from current and prior mammograms using this algorithm. The prediction is never going to be perfect, but this study suggests the new algorithm is much better than our current methods.”
AI improves prediction of breast cancer development
The researchers trained their machine-learning algorithm on the mammograms of more than 10,000 women who received breast cancer screenings through Siteman Cancer Center from 2008 – 2012. These individuals were followed through 2020, and in that time 478 were diagnosed with breast cancer.
The researchers then applied their method to predict breast cancer risk in a separate set of patients — more than 18,000 women who received mammograms through Emory University in the Atlanta area from 2013 – 2020. Subsequently, 332 women were diagnosed with breast cancer during the follow-up period, which ended in 2020.
According to the new prediction model, women in the high-risk group were 21 times more likely to be diagnosed with breast cancer over the following five years than were those in the lowest-risk group.
In the high-risk group, 53 out of every 1,000 women screened developed breast cancer over the next five years. In contrast, in the low-risk group, 2.6 women per 1,000 screened developed breast cancer over the following five years.
Under the old questionnaire-based methods, only 23 women per 1,000 screened were correctly classified in the high-risk group, providing evidence that the old method, in this case, missed 30 breast cancer cases that the new method found.
The mammograms were conducted at academic medical centres and community clinics, demonstrating that the accuracy of the method holds up in diverse settings.
Importantly, the algorithm was built with robust representation of Black women, who are usually underrepresented in development of breast cancer risk models. The accuracy for predicting risk held up across racial groups. Of the women screened through Siteman, most were white, and 27 per cent were Black. Of those screened through Emory, 42 per cent were Black.
In ongoing work, the researchers are testing the algorithm in women of diverse racial and ethnic backgrounds, including those of Asian, southeast Asian and Native American descent, to help ensure that the method is equally accurate for everyone.
The researchers are working with WashU’s Office of Technology Management toward patents and licensing on the new method with the goal of making it broadly available anywhere screening mammograms are provided. Colditz and Jiang also are working toward founding a start-up company around this technology.
Diagnosis
Vaccine could prevent some people from developing ovarian cancer

A vaccine trial will test whether an mRNA jab can help stop precancerous cells developing into bowel and ovarian cancer in people with Lynch syndrome.
The first stage is due to launch this summer and will assess whether the jab can train the immune system to recognise and eliminate precancerous cells before cancer develops.
Around 175,000 people in England have Lynch syndrome, but only five per cent, or around 10,000 people, know they have it.
The inherited condition increases the risk of developing bowel cancer by 80 per cent and is linked to around 1,100 bowel cancer cases each year.
Lynch syndrome is also linked to a far higher risk of bowel, womb and ovarian cancer, alongside other types including stomach, pancreatic, kidney and skin cancer.
While the syndrome does not directly cause cancer, the genetic changes can lead to more abnormal cells developing, which then multiply and increase the risk of cancers such as bowel, prostate and endometrial cancer.
It is caused by an alteration in a mismatch repair gene. Carriers do not have any symptoms.
The new Intercept-Lynch trial is part of a scientific collaboration between the University of Oxford and Moderna, while Cancer Research UK has backed the vaccine’s development.
Once patients receive the new mRNA-4194 jab, experts will analyse their immune responses, assess the best dose and check whether the jab is safe.
The second phase of the study will include multiple centres across the UK, including Oxford, and is expected to begin in 2027.
The aim of the trial is to train the immune system with a vaccine to recognise abnormalities and stop them developing into cancer.
Professor David Church, Cancer Research UK senior cancer research fellow in the University of Oxford’s centre for human genetics and lead investigator of the trial, said: “People with Lynch syndrome are at risk of cancers over their entire lives.
“So, it’s very common, for instance, a woman to have a first cancer of her womb, and then some years later have a bowel cancer, or vice versa.
“The targets we’ve chosen for the vaccine were chosen based on their sharedness across multiple cancer types in Lynch syndrome, so we think they should provide broad protection, if the vaccine works.”
In people with Lynch syndrome, mutations can build up, making the cells containing them more likely to turn into cancerous cells.
However, those mutations can be made visible to the immune system and, with enough stimulation, the immune system can attack the abnormal cells and stop cancer from forming.
Professor Church said the mRNA jab acts as “an instruction manual” for the body to attack precancerous cells.
He added that, as with many vaccines, patients may need a booster jab at some stage.
On whether similar approaches could help prevent cancers not caused by Lynch syndrome, Professor Church said: “In terms of proof of principle that we can train the immune system to recognise these cancer-associated alterations and enhance the immune response against them to prevent these pre-cancers or prevent the progression of pre-cancer to cancer, that proof of principle should give us insights that are generalisable.”
David Berman, chief development officer at Moderna, said: “By applying mRNA technology earlier in the patient journey, we aim to harness the immune system when it can have the greatest impact.
“We are proud to bring this innovation to the UK, building on our long-standing collaboration with leading UK institutions to advance mRNA research and development.”
Diagnosis
Lymph nodes could reveal who’s most at risk of breast cancer spreading

Changes in lymph nodes may help show which breast cancer patients face higher or lower risk of the disease spreading, researchers have found.
The findings could support more tailored care, new treatments and help more people avoid unnecessary treatment.
Dr Simon Vincent is chief scientific officer at Breast Cancer Now, which funded the research:
He said: “These findings suggest that changes to the structure of the lymph nodes are more than just a consequence of the cancer. They can also play an active role in helping breast cancer progress.
“With one person tragically dying from breast cancer every 45 minutes in the UK, we urgently need research like this so that we can better understand who is most at risk of their cancer progressing and becoming incurable. Only then we can find ways to stop it.
“With a better understanding of how lymph nodes change as breast cancer spreads, we could find new targets for future treatments for types of breast cancer that are harder to treat.”
Lymph nodes, a key part of the immune system, help the body fight infections and cancer. In breast cancer, the lymph nodes in the armpit are often the first place the disease spreads to.
At the moment, everyone with invasive breast cancer has to undergo surgery to remove lymph nodes so doctors can check for cancer cells.
Invasive breast cancer means cancer that has spread beyond where it first developed in the breast into nearby tissue.
While this is effective, it can lead to long-term side effects such as swelling of the arm, known as lymphoedema, and may be unnecessary for some patients, particularly those with early-stage disease or those whose cancer responds well to treatment.
The study analysed 331 lymph node samples from people with different types of breast cancer and compared them with healthy lymph nodes from people free from the disease.
It found that breast cancer could change the structure of a network that supports the lymph nodes.
Crucially, some of these changes could occur before doctors were able to spot any cancer cells in the network.
Some changes were linked to a better chance of survival, while others were associated with a poorer prognosis.
Dr Amy Llewellyn and Dr Kalnisha Naidoo from King’s College London, together with professor Sophie Acton at University College London, compared the 331 samples with healthy lymph nodes in people free from the disease.
They looked at fibroblastic reticular cells, known as FRCs, a group of cells in lymph nodes that provide their structure, control fluid flow and activate different immune cells.
The study showed that the structure of this FRC network could change before the cancer had spread and differed depending on the type of breast cancer, any spread and whether someone had received chemotherapy.
Chemotherapy uses medicines to kill cancer cells or slow their growth.
The researchers said the findings could help doctors better understand who is most at risk of breast cancer spreading.
Dr Llewellyn said the first large-scale analysis of FRC in human lymph node tissue from breast cancer patients was addressing the “urgent need” for a better understanding of the area’s biology.
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