STTR Phase II: Automated system for creating custom three-dimensional radiofrequency ablation lesion geometries in post-lumpectomy margin ablation breast cancer treatment

Period of Performance: 09/15/2017 - 08/31/2019

$486K

Phase 2 STTR

Recipient Firm

Innoblative Designs
4660 N Ravenswood Avenue Array
Chicago, IL 60640
Firm POC, Principal Investigator

Research Institution

Northwestern University
1801 Maple Ave.
Evanston, IL 60201
Institution POC

Abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project will focus on the design of the first completely automated radio frequency ablation (RFA) system for breast tissue. Of the approximately 200,000 breast cancer patients in the US who elect lumpectomy annually, over 20% must undergo re-operation due to lack of clear margins. Furthermore, many breast cancer patients who are treated with lumpectomy require expensive adjuvant radiation therapy that can last up to 7 weeks causing time away from work and family. Fortunately, performing intraoperative RFA of the lumpectomy cavity has been shown to decrease the need for re-operations and radiation therapy by sterilizing tumor margins. However, no RFA devices are designed for post-lumpectomy breast cavities or breast tissue. The RFA system being developed in this project will automate post-lumpectomy RFA, accurately and precisely delivering energy based on local tissue properties and desired ablation depth in three dimensions thus customizing treatment for each patient. Eventual commercialization of this system could provide early stage breast cancer patients new treatment options that improve quality of life, reduce burdens of care, and costs while providing breast cancer recurrence control. The proposed project aims to develop a system (control unit and device) for optimal post-lumpectomy RFA. The proposed device is designed to mechanically fit the post-lumpectomy cavity for near-perfect tissue contact. The control unit includes advanced algorithms that utilize machine learning to create a three-dimensional ablation status map of each margin (ablation vs in-ablated) for controlling ablation. The combined system allows the surgeon to customize and monitor the three-dimensional ablation profile and automates therapy delivery to ensure accurate, precise ablation results. The objectives propose gathering device requirements, identifying critical tasks of an automated RFA procedure, and improving the system algorithm by collecting training data in cadaveric and prophylactic mastectomy specimens. The intellectual merits proposed are: (1) an automated ablation system implemented on an embedded microprocessor and co?]processor FPGA capable of ablation shape estimation and control; (2) a system that demonstrates clinical relevance through successful ablation in human tissue and surgeon usability.