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Breast Cancer  Support –

Digital Imaging and AI Mean Fewer Surgeries for Breast Cancer Patients

A project led by Pennsylvania’s Lehigh University might help cancer patients avoid undergoing multiple surgeries. The results can be crucial since according to recent studies, 25 percent of women who undergo breast-saving lumpectomy surgery will require a second operation, which incurs a median cost of $16,000 and causes further health complications.

After removing the tumor from the breast, doctors have to check the operated area for residual cancerous cells. Currently, the process of examination involves taking tissue samples from the margin of the operated area, freezing them with liquid nitrogen, sectioning them to thin slices, and sending them to a lab for examination. The process, known as histopathology, can take as long as a week. If the histopathological analysis finds cancerous cells in the tissues, the patient must undergo another surgery.

But the diagnostic method devised by the researchers at Lehigh University uses advanced imaging techniques and artificial intelligence algorithms to speed up the process and enable real-time scanning and evaluation of the operated margin without the need for extracting tissues.

“If used during surgery, this technique has the potential to significantly reduce the need for a second breast cancer surgery,” said Chao Zhou, assistant professor of electrical engineering at Lehigh who is leading the effort, in an email to Motherboard.

“If used during surgery, this technique has the potential to significantly reduce the need for a second breast cancer surgery.”

The computer-aided diagnostic uses Optical Coherence Microscopy (OCM), an advanced imaging technique that can create high-resolution visualizations of biological tissues at the cellular level, without the need for invasive techniques such as biopsies. The obtained images are then fed to AI algorithms that scan them for presence of cancerous cells.

“OCM images reveal distinctive texture features of benign and malignant tissues, which can be subtle to distinguish,” Zhou said. “Computers are more efficient in spotting these differences than humans.”

AI algorithms have already proven their efficiency at reducing costs and error margins in diagnosing and treating various forms of cancer.