Hidden breast cancer hiding from scanners aimed at innovative technology
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Artificial intelligence is making its mark on the future of cancer care.
One of the most recent applications of this technology is the identification of difficult-to-detect breast cancer.
Researchers at The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and the Richard J. Solove Research Institute are using AI in a preliminary setting to predict which patients may develop lobular breast cancer.
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What is lobular breast cancer?
Breast cancer is the most common cancer in women and the second leading cause of cancer-related death in the country.
Data show that lobular breast cancer, which is aggressive and difficult to detect, accounts for 10% to 15% of breast cancer diagnoses in the US.
This is how lobular breast cancer can appear on a mammogram. Arya Roy, MD, noticed cloudiness in the images, which would lead her to recommend additional scans. (The Ohio State University)
Instead of a group of cells that form a tumor, lobular cancer grows as a long chain of cells, which is why it appears as a “subtle thickness” on mammograms. This means it can be difficult to detect until it has spread to other parts of the body, according to OSU.
This form of the disease is also at risk of recurring even after 10 years of the patient being cancer-free.
“We urgently need better tools… that can predict which patients are truly at high risk.”
Additionally, about 40% of women over age 40 have dense breast tissue, according to the Society of Breast Imaging, which can pose a greater screening challenge and a higher risk of developing breast cancer.
Although invasive lobular cancer grows, spreads and responds to treatment differently than the more common invasive ductal carcinoma, oncologists follow the same guidelines for both diseases, according to lead researcher Dr. Arya Roy, a breast cancer specialist at OSUCCC – James.
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“The genomic tests we currently use often give unclear or conflicting results for lobular cancer, making it difficult for oncologists to decide the best treatment,” he said in the news release. “We urgently need better tools, specific to lobular cancer, that can predict which patients are truly at high risk.”
Technology to fight cancer
Roy reiterated how difficult it is to identify lobular breast cancer through imaging.
“At the same time, it is very difficult to identify patients who are at higher risk of recurrence after treatments,” he told News Digital. “So this is where we are using artificial intelligence techniques to identify patients who are at risk of this cancer coming back.”
Arya Roy, MD, pictured examining a breast scan, is investigating a form of cancer that often goes undetected in regular screening exams. It is using data from real lobular breast cancer cases to train artificial intelligence to improve early detection. (The Ohio State University)
By combining artificial intelligence models with digital pathology images, doctors can detect biomarkers and other indicators in high-risk cancer patients. Together with patients’ clinical data, these findings are used to create a scoring system that predicts the likelihood of cancer recurrence over the next decade, the researchers said.
The AI tool is currently in development, with clinical trials and a funded study on the horizon.
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“We hope that once we have fully developed this artificial intelligence tool, which will help us identify patients at risk of recurrence, we can use it for all patients with lobular breast cancer,” Roy continued.
“If we know that a patient has a 10% greater chance of this cancer coming back in five years, then we can keep that patient under close surveillance.”
The study investigator encourages women to discuss with their doctors whether additional imaging is appropriate for them. (Istock)
Oncologists can also use other imaging techniques to ensure that no cancer recurrence is missed in these higher-risk patients, Roy added, noting that this new AI-powered method could “give hope to many patients.”
The oncologist encourages women to talk to their doctors about whether additional imaging is appropriate for them.
Potential limitations
Dr. Harvey Castro, an emergency physician and artificial intelligence expert in Texas, was not involved in the OSU study but discussed the findings with News Digital.
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“The Ohio State study marks important progress in using AI to detect lobular breast cancer, a notoriously difficult subtype, but also highlights the obstacles that still prevent AI from fully matching the complexity of the real world,” he said.
One of the biggest problems is training AI with old data, the doctor noted. “Medicine is evolving rapidly and algorithms created from yesterday’s images can miss today’s patterns, which is what I call temporal drift.”
“Before these tools enter routine care, we need to ensure they are tested in diverse, real-world populations.”
Castro cautioned that many systems “work wonderfully” in a lab, but can fail when tested in new hospitals or patient populations.
“Dense breast tissue remains the Achilles’ heel of AI,” he noted. “The same density that hides tumors from radiologists can also confuse algorithms, especially among racial and age groups.”
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According to Castro, AI will not replace radiologists; On the contrary, it will redefine the way you work.
“But before these tools enter routine care, we need to ensure they are tested in diverse real-world populations, not just perfect laboratory data.”
Angelica Stabile is a lifestyle reporter for News Digital.