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.
Advanced Manufacturing of Polymer Nanofiber Sutures
Inventor: Dominque Hassinger, Sean McMillan, Vince Beachley
Brief Description
Advanced polymer nanofiber manufacturing technology enables fabrication of nanoyarns suitable for suture applications. Nanofiber architecture promotes favorable cell-biomaterial interactions that promote healing, such as an M2 regenerative response, organized extracellular matrix deposition, and cell differentiation toward preferred phenotypes. Electrospun nanofibers can be easily modified to include bioactive peptides to further enhance healing/regeneration.
Problem
The regenerative potential of nanofiber architecture in sutures is well known, however manufacturing polymer nanofiber yarns faces technical challenges. High velocity nanofiber fabrication results in poor fiber organization, yarn uniformity, and scalable lengths.
High temperatures associated with conventional melt spun sutures can degenerate bioactive factors and peptides.
Solution
A multi-stage nanofiber yarn manufacturing process fixes aligned nanofibers to facilitate tight control over yarn organization. This process yields nanofiber yarns with suitable mechanical performance and diameter uniformity for sutures. Bioactive peptides can easily be encapsulated within nanofibers without loss of activity. This versatile manufacturing approach can combine many different polymer materials into a single hybrid yarn to target specific purposes (drug release, strength, degradation rate, etc.)
Technology
This technology uses opposing automated tracks to immobilize and post-draw electrospun nanofibers to strength them. They are transferred to a continuous roll to enable twisting/winding into a yarn. Custom peptides are dissolved directly in the electrospinning solution to encapsulate in fibers.
Advantages
Intellectual Property
- System and method for electrospun fiber straining and collecting. U.S. Patent No. 11,015,267. Issued May 2021
- Assembly of polymer staple nanofiber yarn. U.S. Patent Application No. 18/832,559. Filed March 2025
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