‘Electronic skin’ could improve early breast cancer detection

Using a silicone model of a breast and embedding objects representing lumps, scientists have successfully tested an electronic skin that can accurately "feel" and image lumps much smaller than those detectable by manual exams. Credit: American Chemical Society

Using a silicone model of a breast and embedding objects representing lumps, scientists have successfully tested an electronic skin that can accurately “feel” and image lumps much smaller than those detectable by manual exams.
Credit: American Chemical Society

For detecting cancer, manual breast exams seem low-tech compared to other methods such as MRI. But scientists are now developing an “electronic skin” that “feels” and images small lumps that fingers can miss.

Knowing the size and shape of a lump could allow for earlier identification of breast cancer, which could save lives. They describe their device, which they’ve tested on a breast model made of silicone, in the journal ACS Applied Materials & Interfaces.

Ravi F. Saraf and Chieu Van Nguyen point out that early diagnosis of breast cancer, the most common type of cancer among women, can help save lives. But small masses of cancer cells are not always easy to catch. Current testing methods, including MRI and ultrasounds, are sensitive but expensive. Mammography is imperfect, especially when it comes to testing young women or women with dense breast tissue. Clinical breast exams performed by medical professionals as an initial screening step are inexpensive, but typically don’t find lumps until they’re 21 millimeters in length, which is about four-fifths of an inch. Detecting lumps and determining their shape when they’re less than half that size improves a patient’s survival rate by more than 94 percent. Some devices already mimic a manual exam, but their image quality is poor, and they cannot determine a lump’s shape, which helps doctors figure out whether a tumor is cancerous. Saraf and Nguyen wanted to fill this gap.

Toward that end, they made a kind of electronic skin out of nanoparticles and polymers that can detect, “feel” and image small objects. To test how it might work on a human patient, they embedded lump-like objects in a piece of silicone mimicking a breast and pressed the device against this model with the same pressure a clinician would use in a manual exam. They were able to image the lump stand-ins, which were as little as 5 mm and as deep as 20 mm. Saraf says the device could also be used to screen patients for early signs of melanoma and other cancers.

via American Chemical Society

 

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USC Stem Cell researcher targets the “seeds” of breast cancer metastasis

Circulating tumor cells from the blood of a breast cancer patient (Image by Maria C. Donaldson and Min Yu)

For breast cancer patients, the era of personalized medicine may be just around the corner, thanks to recent advances by USC Stem Cell researcher Min Yu and scientists at Massachusetts General Hospital and Harvard Medical School.

In a July 11 study in Science, Yu and her colleagues report how they isolated breast cancer cells circulating through the blood streams of six patients. Some of these deadly cancer cells are the “seeds” of metastasis, which travel to and establish secondary tumors in vital organs such as the bone, lungs, liver and brain.

Yu and her colleagues managed to expand this small number of cancer cells in the laboratory over a period of more than six months, enabling the identification of new mutations and the evaluation of drug susceptibility.

If perfected, this technique could eventually allow doctors to do the same: use cancer cells isolated from patients’ blood to monitor the progression of their diseases, pre-test drugs and personalize treatment plans accordingly.

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First Warning Systems uses thermodynamic metabolic data to help combat deadly affliction.

First Warning Systems uses thermodynamic metabolic data to help combat deadly affliction.

First Warning Systems has created the FWS Circadian Biometric Recorder (CBRTM), a cancer-detecting bra that enables women to identify malignant tumors in their earliest stages.

It uses sensors to detect unusual heat patterns in breast tissue in order to identify abnormalities before they become a big problem.

The CBRTM is applied via a bra insert and records thermodynamic metabolic data, gathering changes in cell activity over a 2 to 12 hour period, after which the results are wirelessly transmitted to a computer for analysis. Early research has shown that there’s a 74% correlation to the actual state of cancer in all types of breast tissue, which aids the accurate recording of abnormal dynamic tissue processes which are indicative of the presence of cancer.

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Can Vitamin A Turn Back the Clock on Breast Cancer?

The arrow on this mammogram points to a small cancerous lesion. A lesion is an area of abnormal tissue change (Photo credit: Wikipedia)

Pre-cancerous cells treated with a vitamin A derivative revert into non-cancerous cells.

A derivative of vitamin A, known as retinoic acid, found abundantly in sweet potato and carrots, helps turn pre-cancer cells back to normal healthy breast cells, according to research published this month in the International Journal of Oncology. The research could help explain why some clinical studies have been unable to see a benefit of vitamin A on cancer: the vitamin doesn’t appear to change the course of full-blown cancer, only pre-cancerous cells, and only works at a very narrow dose.

Because cells undergo many changes before they become fully aggressive and metastatic, Sandra V. Fernandez, Ph.D., Assistant Research Professor of Medical Oncology at Thomas Jefferson University, and colleagues, used a model of breast cancer progression composed of four types of cells each one representing a different stage of breast cancer: normal, pre-cancerous, cancerous and a fully aggressive model.

When the researchers exposed the four breast cell types to different concentrations of retinoic acid – one of the chemicals that the body converts vitamin A into – they noticed a strong change in the pre-cancerous cells. Not only did the pre-cancerous cells begin to look more like normal cells in terms of their shape, they also changed their genetic signature back to normal. Dr. Fernandez’s pre-cancerous cells had 443 genes that were either up or downregulated on their way to becoming cancerous. All of these genes returned to normal levels after treatment with retinoic acid. “It looks like retinoic acid exerts effects on cancer cells in part via the modulation of the epigenome,” says Fernandez.

“We were able to see this effect of retinoic acid because we were looking at four distinct stages of breast cancer,” says Dr. Fernandez. “It will be interesting to see if these results can be applied to patients.”

Interestingly, the cells that were considered fully cancerous did not respond at all to retinoic acid, suggesting that there may be a small window of opportunity for retinoic acid to be helpful in preventing cancer progression. In addition, the researchers showed that only one concentration of retinoic acid (about one micro Molar) produced the anti-cancer effects. Lower concentrations gave no change, and higher concentrations produced a smaller effect.

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Machines learn to detect breast cancer

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The Human Body — Cancer (Photo credit: n0cturbulous)

Software that can recognize patterns in data is commonly used by scientists and economics.

Now, researchers in the US have applied similar algorithms to help them more accurately diagnose breast cancer. The researchers outline details in the International Journal of Medical Engineering and Informatics.

Duo Zhou a biostatistician at pharmaceutical company Pfizer in New York and colleagues Dinesh Mital and Shankar Srinivasan of the University of Medicine and Dentistry of New Jersey, point out that data pattern recognition is widely used in machine-learning applications in science. Computer algorithms trained on historical data can be used to analyze current information and detect patterns and then predict possible future patterns. However, this powerful knowledge discovery technology is little used in medicine.

The team suggested that just such an automated statistical analysis methodology might readily be adapted to a clinical setting. They have done just that in using an algorithmic approach to analyzing data from breast cancer screening to more precisely recognize the presence of malignant tumors in breast tissue as opposed to benign growths or calcium deposits. This could help improve outcomes for patients with malignancy but also reduce the number of false positives that otherwise lead patients to unnecessary therapeutic, chemotherapy or radiotherapy, and surgical interventions.

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