A new imaging method being developed at Ohio State University may one day help doctors diagnose breast cancer with greater accuracy.
The method, which involves computerised analysis of magnetic resonance images (MRIs) of breasts, could greatly reduce the number of women who have to undergo painful biopsies.
With current screening techniques, seven out of every 10 women suspected of having breast cancer and sent for biopsies turn out to have no malignancies.
To lower that high ratio of false positives, a team of Ohio State researchers, led by Bradley Clymer, an associate professor of electrical engineering, is developing a diagnostic step between x-ray mammography – the technique most commonly used for breast cancer screening – and biopsy.
In this step, doctors would analyse MRIs of breasts to detect blurred regions that might correspond to microcalcifications that are often indicators of cancer.
In a recent study, their method – which is still several years away from clinical use – was nearly 100 percent accurate in detecting blurred regions in breast MRIs.
The method is based on the knowledge that calcified regions in the breast show up as blurred areas in MRIs – a result of the magnetic properties of calcifications being slightly different from those of normal soft tissue. Some of these calcifications, which occur when breast tissue hardens due to calcium accumulation, are a sign of malignancy.
Clymer and research colleagues Petra Schmalbrock and David James showed that it is possible to detect blurred patches in MRIs using a computer program that recognises minor differences between pixels.
Clymer is now studying how specific kinds of calcification in the breast – like a cluster of tiny particles or branching lines of hardened tissue – would distinctively blur a breast image.
MRI data could then be used to confirm or reject suspicious regions found on x-ray mammograms.
Although the MRI test could end up being slightly more expensive than a biopsy, Clymer thinks it would still be attractive because of its non-invasive nature.
To test the feasibility of detecting blurs on MRIs, Clymer and his colleagues took MRI images of healthy breasts and introduced blurred patches in them.
They did this by blurring an image completely on a computer and pasting small parts from this blurred image on specific locations in a normal image. The blurs were superimposed in a way that simulated a certain type of calcification called ‘focal clusters’.
For the detection, the researchers used statistical texture analysis – a method to observe how pixels are related to their neighbours within an image.
The software that the researchers used produced 14 measures of difference between pixels – called texture features, quantifying the extent of blurring in different locations of the image.
The researchers found that the software was able to detect more than 90 percent of the blurred regions and that the accuracy of detection was close to 100 percent.