Scientists have developed a better way to identify markers for breast cancer tumors, a breakthrough that could lead to better treatment for millions of women. They used machine learning to rapidly sort images of tumors to identify estrogen receptors, a key to determining prognosis and treatment. The technique offers a new pathway for breast cancer treatment that promises faster results for less cost for more people worldwide.

A research team led by USC scientists has developed a new way to identify molecular markers of breast cancer tumors, a potentially life-saving breakthrough that could lead to better treatment for millions of women. The study was published in the Nature Partner Journals Breast Cancer.

Images Of Breast Tumors

The researchers taught a computer to rapidly sort images of breast tumors to identify which ones had estrogen receptors, a key to determining prognosis and treatment options. That's a big step forward from microscopes and cell biopsies in use for more than a century.

The work opens a new pathway for breast cancer treatment that promises faster results for less cost for more people worldwide. It's the beginning of a revolution to use machine learning to get new information about breast cancer to the physician.

They can use it to detect better treatments, get information to patients faster and help more people. We're unleashing this power to give new information to physicians and help treat cancer. Except for skin cancer, breast cancer is the most common cancer in women in the United States.

Identify And Treating Cancer

While deaths have declined, it remains the second-leading cause of cancer death among women and the leading cause of cancer death among Hispanic women. About 237,000 cases of breast cancer are diagnosed in U.S. women and about 41,000 die from the disease each year. The key to identifying and treating cancer is knowing the nature of the tumor.

Cancer cells that contain receptors for estrogen and other hormones respond differently to cancer drugs that target these mechanisms. While doctors use these characteristics to classify tumors and select treatments, testing for markers is a slow and inefficient process.

Detection Of Cancer

While machine learning has been used before for cancer detection, the USC study adapted the technology to more focus on telltale markers of a cell's nucleus. The key was to extract parameters describing the shape of nuclei, and feeding these into a large neural network that could learn the relationships between nucleus shape and molecular markers.

Histopathology Images

The scientists used publicly available hematoxylin-eosin (H&E) stained histopathology images. The cell-stained slides doctors have been using for more than a century.  Next, they ascertained clinical status for 113 cancer patients, then split the patients into two groups, using one group to train a convolutional neural network algorithm, which is used to enhance visual imagery, and another to test the machine.

When they compared the two sets, they found a strong correlation, providing high confidence that an algorithm can predict the estrogen receptor status of the tumor. They can use this technology to identify the molecular markers of the tumor and in the future will identify which therapeutics the tumor will respond to.

Machine Learning

Machine learning helps us get this information to patients sooner and may transform cancer care in the developing world where precise breast cancer marker assessment is in short supply. The research findings demonstrate that the new technology has the potential to improve clinical care. Validation studies are an underway important step before it's ready for use in the doctor's office.