Saturday , February 4 2023

Breast cancer: tumors can now be detected faster and more efficiently


The combination of the latest advances in technology has returned to the service of medicine and the patient. For a more efficient and more precise way identify carcinogenic tumors in the chest which analyzes tissue images using a computer, a much faster process than the traditional one, was presented by the University of Southern California (USC).

"It's the beginning of a revolution to use"machine learning"And you get new information about breast cancer for a doctor," said David Agus, professor at the Keck Medical School and Viterbi School of Engineering, USC and one of the researchers.

"This system can be used to establish better treatments, Give patients quicker information and help more people. We announce this finding in order to offer new information to doctors and help in the treatment of cancer, "Agus said.

The researcher pointed out that the key to identifying and treating cancer was knowledge of the nature of the tumor. "Cancer cells that contain estrogen receptors and other hormones Answers other than drugs, "he added.

What is new development based on: machine learning

The system "get in"Images of breast cancer tumors are quickly analyzed on the computer to identify those showing estrogen receptors, a key determinant of treatment options." According to a method described in the scientific journal Nature Partner Journals Breast Cancer, it is a "a big step outside the microscope and cell biopsies that have been used for more than a century"

"If you are diagnosed with cancer It will be several weeks before calling on a doctor telling him to find the identifier, "said Dan Ruderman, Assistant Professor of Medical Research at Keck School and co-author of the study." With the technology of "machine learning" we can apply the same day, with which it has fewer delays, less stress, and potentially better results. This will allow us to identify the exact amount of medicine and dose faster. That's a big step towards that personalized medicine"Ruderman added.

The study focused on establishing parameters for identifying "main identifiers" in the core of cells and connecting them to a large network, so that machine technology can quickly identify them. "Machine learning It helps us deliver information faster to patients and can transform cancer treatment in a developed world, where a precise assessment of breast cancer rates is scarce, "concluded Rishi Ravat, a graduate student of the Keck School and lead author of the study.

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