The objectives of this project are: (1) to produce a prototype which will demonstrate how a combination of analytically-determined MRI features, expert findings and patient data can aid the diagnostician in improving the management of patients with known focal breast masses, and (2) design a clinical evaluation of a commercial system. The prototype will generate likelihood estimates of malignancy using expert-observer readings, pre-test risk factors and computer-generated fractal dimension (fd) features as input to a non-linear discriminator. The fd feature is computed as statistics from a space of fractal interpolation function models (FIFM) of boundary segments of the mass. Boundary segments from multiple threshold levels are used. The statistical approach provides a robustness which is not found in other fd estimators. In the Phase I feasibility study, the combination of FIFM and expert-observer features generated improved discrimination over expert-observer features alone.
A system which is an aid to the diagnostician in improving the management of patients with known breast masses will have a significant market value to MRI centers and developers who have an interest in developing and promoting MRI for breast cancer diagnosis.