Diagnostic medical imaging technologies such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are noninvasive procedures used to acquire high-resolution images of organs/tissues in the body. These technologies provide valuable information for identifying tissue damage, disease diagnosis, and delivery of radiation therapy. Both of these technologies function by acquiring image slices of the tissue and combining them into high-resolution 3D images for visualization by a radiologist. They are vital for viewing tumors, as well as for treatment planning; however, current methods for determining tumor progression in cancer surveillance are limited to simple image comparisons to previous imaging scans. As a result, it is difficult for the radiologist to determine if a tumor is progressing, which can delay the administration of life-saving treatment.

Investigators have developed a machine learning software platform that enables radiologists to quantify tumor growth for improved and accurate cancer surveillance.  The technology interacts with an electronic medical records system allowing for quantification of the tumor volume/size over time by the use of raw MRI data.

Competitive Advantages

•       More accurate quantification of the volume/size of a cancerous tumor

•       Provides for earlier treatment for a cancer victim

•       Has the potential to improve cancer treatment outcome and chances of survival

Opportunity

The overall diagnostic imaging market in the US exceeded $33 billion dollars in 2017 and is expected to rise to $36 billion at a CARG of 6.6% by 2021.  Within this market, the machine learning software sector is expected to be valued at more than $2 billion dollars by 2023, with approximately 30% of medical providers in the US expected to implement some type of machine learning technology as part of their diagnostic assessment of patients. 

Rowan University is looking for a partner for further development and commercialization of this technology through a license. The inventor is available to collaborate with interested companies.