Advanced viz points way to personalized medicine
Lisa Fratt - FOR LEAD ONLY - 118.11 Kb
The era of personalized medicine has dawned. At its best, personalized medicine promises to improve patient care and outcomes, while better allocating healthcare resources, by individualizing treatment to specific characteristics of a patient’s disease.

Informing such nuanced treatment decisions requires laser-sharp details and data. A pair of recent studies demonstrates that advanced visualization tools can provide this critical information.

Treatment of ductal carcinoma in situ (DCIS) represents the conundrum of overtreatment. Breast imaging specialists acknowledge that some cases of DCIS may be overtreated. However, they do not yet understand which cases of DCIS require treatment to reduce progression to invasive disease and which cases of DCIS can be managed with a less intense approach.

Habib Rahbar, MD, from the department of radiology at University of Washington, Seattle Cancer Care Alliance, and colleagues developed and applied a model to leverage diffusion-weighted and dynamic contrast MRI features to differentiate women with DCIS at high risk of progression from those with less severe disease.

The capability to categorize patients could allow physicians to better tailor treatment to individual patients. Potentially, some women with low-risk DCIS could forego radiation therapy, thus curbing morbidity and cutting costs, according to Rahbar and colleagues.

In another study detailing the potential contribution advanced visualization to personalized medicine, researchers at the Los Angeles Biomedical Research Institute at University of California-Los Angeles, and colleagues used coronary CT angiography to identify young adults with diabetes to detect stenosis and plaque.

The ability to identify these patients provides physicians with the data they need to consider earlier initiation of primary coronary artery disease prevention, such as lipid-lowering treatment.

These examples represent the mere of the tip of the personalized medicine iceberg. It is a model that depends heavily on imaging data, and the contributions of advanced visualization can boost the utility of imaging.

Please let us know how you are using advanced visualization to personalize treatment decisions.

Lisa Fratt