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New Hope in the Fight Against Breast Cancer: Cancer Detection Goes Digital

May/June 1998

Almost 179,000 American women will be diagnosed with invasive breast cancer in 1998, according to American Cancer Society statistics. During that same period, more than 43,000 women will die from the disease.

Since their introduction in the 1960s, mammograms have become a powerful weapon in the fight against breast cancer. When used by experienced radiologists, the special x-ray technique can catch cancer during its early stages, when it is most treatable. Yet, despite the advances in cancer detection made possible by mammography, current methods have their limitations.

With funding from NSF and other agencies, hope is on the horizon. Digital mammography, a new technology now being evaluated by the Food and Drug Administration (FDA), promises to locate very small tumors using lower doses of radiation.

In the June 1992 issue of Radiology, Dr. Faina Shtern of the National Cancer Institute wrote that digital mammography is "the most fertile territory for major advances in the detection and diagnosis of minimal breast cancers."


Robert Gray, professor of electrical engineering at Stanford University, and Richard Olshen, professor of health research and policy, are two of the people helping to create these advances. Gray, Olshen and their colleagues have developed a method for comparing the quality and precision of traditional mammograms to their new digital counterparts. A primary goal of this portion of Gray and Olshen's NSF-funded research has been to develop standards for evaluating the reliability of the digital images.

In digital mammography, the analog film normally used for recording the image is replaced by an electronic x-ray detector. The signal from the detector is digitized and stored in the computer's memory, and the resulting image can be displayed and adjusted by the radiologist. This facilitates detection of small breast cancers and microcalcifications, or benign calcium deposits in breast tissue.

Gray and Olshen began working on a way to reliably compare the accuracy of traditional and digital mammograms in the early 1990s. They became interested in signal processing and data compression for medical imaging, in part because of concerns expressed by some physicians that the processes for acquiring, storing and transmitting digital images would result in a loss of reliability.

Before the new digital techniques can be used in clinical settings, physicians and the FDA must be convinced that digital mammography is as accurate as its conventional counterpart. Toward that end, the Stanford team, led by Gray and Olshen and with additional support from the U.S. Army, has used its own algorithms to compare more than 500 digital and conventional mammograms.

Some of their results have already been incorporated into the FDA's new protocol for evaluating digital mammography systems. Several equipment manufacturers are testing these systems; one company submitted data from clinical trials to the FDA in late 1997. The data from the Stanford research will help the FDA evaluate the systems and determine whether or not they should be marketed.

"The newer (digital) machines filter the signal to eliminate the noise, clean up the image and deliver better resolution," explains John Cozzens, program manager in NSF's Directorate for Computer and Information Science and Engineering. "Radiology is not an exact science," he continues, "which to-date has made it incredibly difficult to confirm the accuracy of digital mammography. When Bob first began his work, he was a voice in the wilderness."

According to Cozzens, the thrust of the work is to demonstrate that there is no difference between conventional and digital mammography and, in fact, that the digital technology will prove to be better. "Doctors need to be confident that the digital images are as good as the films they are used to reading," says Gray. "There is mounting evidence that digital mammography will be superior to analog film methods."


Many researchers believe this new imaging technique may provide significant benefits over traditional methods.

Unlike film, which must be developed and yields one copy, digital mammograms can be duplicated and transmitted over the Internet to multiple locations. Once approved by the FDA for commercial use, the technology will enable physicians or technicians in remote areas to employ telemammography--sending mammograms over the Internet--to confer with specialists at large medical centers for initial readings or second opinions.

Digital mammograms can easily be compiled into a database and be used as a powerful teaching tool. The image files will enable medical students, interns and radiologists to study, classify and compare hundreds of cases. In addition, the digital approach delivers a lower dose of radiation and may improve imaging of particularly dense breast tissue.

As computers enter the realm of mammography, they also have the potential to assist radiologists in evaluating what they see. With a grant from NSF, Professor Ed Delp and his colleagues at Purdue University in Indiana began work on a computer-aided diagnosis (CAD) system in 1992. His research involved the development of computer algorithms that recognize and highlight irregularities in mammograms.

In this foundational research, Delp set out to look for stellate lesions. Once he had identified the characteristics of these star-shaped tumors, the research team had planned to design an algorithm that would flag possible lesions, to assist in the reading of the mammogram.

In the process, however, Delp discovered that many physicians were resistant to the idea of a CAD system for identifying tumors. Based on that reaction, Delp decided it made sense, instead, to look for "normalities" in breast tissue. "When you've defined what is normal, you know that everything else is abnormal," explains Delp.

On a related project, funded by the National Institutes of Health (NIH), Delp's group pioneered other techniques such as multi-resolution processing and tree classification. By examining the digital images at many different resolutions, researchers are better able to classify the information contained in the mammograms. Tree classification is a method for extracting the unique features of each image and then classifying it as either normal or abnormal.

"The preliminary findings in our search for 'normals' are promising," Delp notes. "This new kind of CAD has enormous potential. It will save time by allowing physicians to concentrate only on the mammograms that show abnormalities." Delp is planning a rigorous three-year study to evaluate the effectiveness of his CAD system.

Dragana Brzakovic, an NSF program director and expert on digital mammography, sums up the anticipated advantages of the research that she, Delp and others are conducting: "With digital mammography, you can control quality in real time. Radiologists will be able to evaluate mammograms on the spot--while the patient is still there--and correct positioning and other problems immediately." She adds, "Women will no longer have to live in fear, waiting days for results."


Concern about early cancer detection is widespread. The work of Gray, Olshen, Delp and other NSF-funded scientists has paved the way for other research in the field of digital mammography.

  • Researchers at the National Aeronautics and Space Administration's Langley Research Center have teamed with colleagues at the University of Virginia and University of Massachusetts Schools of Medicine to test concepts that make all-digital full-breast imaging possible. The first working system was introduced in November 1995 and is now being tested at four locations in the United States and Canada.
  • At the Naval Surface Warfare Center, researchers are beginning to transfer imaging techniques developed for the Department of Defense to the development of their version of a CAD system.
  • With a grant from the U.S. Army, scientists at the University of South Florida have established the Digital Database for Screening Mammography to facilitate sound research into the development of computer algorithms to aid in screening.
  • The effort to improve breast cancer detection crosses many borders. Among those working to improve the technology and techniques are researchers at Brandeis University and Lawrence Livermore National Laboratory, as well as at England's Oxford University and Norway's Signal Processing Group.

The work in this field is bringing computer scientists, engineers and statisticians together with physicians and patients from around the world. They are working together to find better ways to identify and treat breast cancer, bringing hope to tens of thousands of women and their families.


Under an NSF grant awarded in April 1997, Robert Gray, Stanford University professor of electrical engineering, has been collaborating with mathematicians at the French National Center for Scientific Research (Le Centre National de la Recherche Scientifique, CNRS). CNRS is the largest research organization in France for fundamental research in science and engineering. This international team is designing algorithms for image compression and classification. Their work is expected to contribute significantly to further advances in digital mammography.

"This collaboration brings the value of France's sophisticated theoretical mathematicians to bear on Gray's work," says Rose Gombay, program manager in NSF's Division of International Programs. "Grants from NSF's Division of International Programs are designed to stimulate international collaborations and advance U.S. science. Our goal is to provide catalytic funding so that U.S. researchers will incorporate such collaborations in their plans," she concludes.

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