Hologic AI mammography technology has demonstrated potential in detecting invasive lobular cancer, according to new research presented at the Society of Breast Imaging Symposium. The findings highlight how artificial intelligence may support radiologists in identifying difficult-to-detect cancer subtypes.
AI supports detection of invasive lobular cancer
The study evaluated Hologic’s AI-driven breast cancer detection system, including the Genius AI Detection solution. Researchers analysed cases of invasive lobular cancer, a subtype known for being more difficult to identify using conventional mammography.
In a retrospective review conducted at Massachusetts General Hospital, the AI system correctly identified and localised close to 90% of confirmed cases. Importantly, it also flagged a proportion of cancers that had previously been missed during routine screening.
Challenges in breast cancer detection
Invasive lobular cancer accounts for around 10–15% of breast cancer cases and often presents with subtle imaging features. As a result, it can be harder to detect compared to more common forms such as ductal carcinoma.
Meanwhile, its growth pattern may lead to later diagnosis and more advanced disease at detection. This reinforces the need for improved diagnostic support tools.
Role of AI in radiology workflows
The study findings suggest that AI could act as a complementary tool for radiologists. By identifying patterns that may not be immediately visible, AI systems can support earlier and more accurate diagnosis.
However, the research was retrospective and conducted at a single centre. It did not assess real-time clinical outcomes such as recall rates, biopsy decisions or patient management.
Understanding AI in mammography
AI in mammography uses machine learning algorithms trained on large imaging datasets to detect abnormalities. These systems analyse subtle variations in breast tissue that may indicate early-stage cancer.
Importantly, AI is not intended to replace radiologists but to assist in decision-making. By improving sensitivity and reducing missed cases, it may enhance screening programmes and patient outcomes.
As healthcare systems continue to adopt digital tools, AI-assisted imaging is expected to play an increasing role in cancer diagnostics.
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