Researchers at the University of Chicago are developing computer-aided diagnosis (CADx) and quantitative image analysis (QIA) methods for mammograms, ultrasounds and magnetic resonance images (MRIs) to identify specific tumour characteristics, including size, shape and sharpness.
Currently, computer-aided detection provides a "second opinion" to a radiologist in locating suspicious regions within mammograms.
Radiologists will ultimately be able to use computer-extracted lesion characteristics when performing a diagnosis to assess whether the tumour is cancerous, said lead researcher Maryellen Giger.
The role of quantitative image analysis is expanding beyond screening and towards application of risk assessment, diagnosis, prognosis, and response to therapy, and in using data to identify how tumour characteristics apply to disease states, Giger said.
This could lead to the comparison of a tumour's characteristics with thousands of similar cases, enabling the exploration of complex relationships among tumour characteristics across large populations, which may ultimately contribute to the design of patient-specific treatments.
It could also be used to study the association between a tumour's observable characteristics and cell-level data for the emerging field of imaging and genomics, which aims to identify genes that influence the risk for disease.
While the results are promising for digital mammograms, researchers are extending their analysis to breast ultrasounds and MRIs due to the need for clinical validation within a larger screening population.
Through studies between image-based characteristics and genomics, investigators will potentially be able to determine which tumour characteristics are related to and which complement genetic findings, with the ultimate goal of merging them to include both genetic and environmental contributions in clinical decisions.
The study was published by the International Society for Optics and Photonics (SPIE).