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Image-based risk assessment to identify women at high-risk for breast cancer

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41CA285182-01
Agency Tracking Number: R41CA285182
Amount: $405,950.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NCI
Solicitation Number: PA22-178
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-01
Award End Date (Contract End Date): 2024-08-31
Small Business Information
20 GODFREY DR Suite 201C
Orono, ME 04473-3610
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 KENDRA BATCHELDER
 (207) 401-8645
 kendra.batchelder@wavedmedical.com
Business Contact
 KENDRA BATCHELDER
Phone: (207) 401-8645
Email: kendra.batchelder@wavedmedical.com
Research Institution
 UNIVERSITY OF MAINE SYSTEM
 
107 MAINE ST
BANGOR, ME 04401
United States

 Nonprofit College or University
Abstract

7. PROJECT SUMMARY
Breast cancer is the most common cancer worldwide and the most common cancer diagnosed in American
women. While there has been good progress regarding detection and treatment methods, breast cancer remains
the primary cause of death from malignant tumors. Hence, there is a critical need for the development of novel
predictive and prognostic factors. Risk assessments are currently performed by medical professionals to identify
women that could benefit from enhanced breast surveillance or risk reduction methods. Unfortunately, most
diagnosed cases do not have an identifiable risk factor, making it a challenge to identify high risk women prior to
onset using classical risk assessments. This medical difficulty has resulted in the development of several artificial
intelligence and machine learning approaches being applied to screening mammograms to identify breast cancer
earlier. However, these approaches search for abnormalities that indicate an existing cancer and have been
found to not be generalizable to the entire screening population. It is becoming more common for younger women
to be diagnosed with breast cancer, and the cancers tend to be more aggressive. This Phase I proposes to
create a risk assessment product for mammography that is not based on machine learning but rather a novel
measurement of risky dense tissue. Alteration in the architecture and composition of microenvironment is a well-
recognized component of breast pathologies and some changes may occur prior to tumor onset. WAVED
Medical’s measurement is sensitive to these alternations in identifying areas of dense tissue that is tumor prone.
This feasibility study seeks to demonstrate that the novel measurement of risky dense breast tissue has the
potential to be implemented into classical risk models. Phase I specific aims are to 1) improve efficiency in
identifying risky dense tissue on mammograms by creating a secure database that contains preprocessed data
for optimized analysis, and 2) establish risky dense tissue as a better predictor of breast cancer than traditional
mammographic percent density (MPD), by showing risky dense tissue is more accurate in predicting breast
cancer than MPD. Follow-on Phase II efforts will include developing a platform and integrating WAVED into
hospital infrastructure for evaluating mammograms. These improvements will create a risk assessment product
that increases the accuracy of medical professionals at identifying high-risk patients and ensures patients are
receiving additional medical care, such as supplemental screening or risk reduction methods, to prevent invasive
cancer. Successful completion of the project has potential to advance state-of-the-art breast cancer assessments
to provide quantification of risky dense tissue to identify high-risk patients needing preventive care.

* Information listed above is at the time of submission. *

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