Scientific foundation supporting our biophysics-driven approach to breast cancer risk assessment.
Research Foundation
WAVED’s proposed interpretable risk assessment technology is grounded in over a decade of peer-reviewed research in computational mammography analysis. Unlike black box AI approaches that are based on computational science and lack transparency, our methodology is built on established biophysical principles published in leading scientific journals.
Our research investigates how mammographic tissue organization may be mathematically linked to cancer growth dynamics, providing the scientific foundation for our tissue subtyping approach. This body of work supports our hypothesis that our computational method can distinguish “at-risk” active dense tissue from passive dense tissue – a breakthrough that could enable more precise risk stratification. In contrast to methods that provide unexplainable outputs, our biophysics-driven approach offers transparent, interpretable analysis based on measurable physical properties.
Featured Research
Key publications that establish the scientific basis for WAVED’s technology:
MEDICAL PHYSICS 2017
Mammographic Evidence of Microenvironment Changes in Tumorous Breasts
Significance: Establishes the link between tissue disorganization patterns and tumor presence – foundational to our active/passive dense tissue classification.
FRONTIERS IN PHYSIOLOGY 2021
Loss of Mammographic Tissue Homeostasis in Invasive Lobular and Ductal Breast Carcinomas vs. Benign Lesions
Significance: Further establishes the robustness of our active/passive dense tissue classification by showing a similar assessment across both invasive ductal and invasive lobular tumors.
Patents & Intellectual Property
Patented Technology & Exclusive Licensing
WAVED Medical holds exclusive licensing rights from the University of Maine for patented technology that analyzes mammographic breast tissue organization. This intellectual property covers the biophysical methodology underlying our tissue subtyping approach, representing years of research into the mathematical relationships between tissue structure and cancer risk.
Key Innovation: The licensed patents cover our method for quantifying spatial organization patterns in mammographic images to distinguish between different types of dense breast tissue based on their correlation properties – the foundation of our proposed interpretable risk assessment technology.
Complete Publications
CLINICAL CANCER RESEARCH 2025
Image-based breast cancer risk assessment based on dense mammographic tissue subtypes
Khalil et al
CANCER RESEARCH 2024
Multiscale Computational Analysis of Patient-Matched Multi-Modal Imaging of Breast Tissue Microenvironment
Hamilton et al
FRONTIERS IN PHYSIOLOGY 2016
Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms
Gerasimova-Chechkina et al
COMPUTERS IN BIOLOGY AND MEDICINE 2016
Computational growth model of breast microcalcification clusters
Plourde et al
PLOS ONE 2014
Wavelet-Based 3D Reconstruction of Microcalcification Clusters: Fractal Tumors Are Malignant
Batchelder et al
Academic Recognition
Our research has been presented at leading scientific conferences and symposiums:
- Radiological Society of North American 2025 – A biophysics-based computational approach both outperforms and synergizes with a model developed using inputs from a commercial mammographic density assessment software for breast cancer prediction
- Maine INBRE Data Science Colloquium 2025 – Interpretable breast cancer risk assessment through biophysics-driven subtyping of mammographic dense tissue
- The 11th International Breast Density and Cancer Risk Assessment Workshop 2025 – Robustness of a biophysics-based computational approach that is agnostic to processing vs presentation mammograms for breast cancer risk assessment
- San Antonio Breast Cancer Symposium 2024 – Image-based breast cancer risk assessment based on dense mammographic tissue subtypes
- San Antonio Breast Cancer Symposium 2023 – Multiscale computational analysis of patient-matched imaging
- École Normale Supérieure Lyon Physics Colloquium 2023 – Longitudinal case-control study of mammographic breast tissue subtypes
- University of Hawaii Cancer Center 2023 – International Breast Density and Cancer Risk Assessment Workshop
- Radiological Society of North America 2022 – Computational assessment of healthy vs. risky mammographic breast density
- Why Study Breast Density?, Melbourne Australia 2022 – Quantitative visualization of healthy vs risky mammographic breast density
- National Cancer Institute 2020 – Overview of 2D Wavelet Transform Modulus Maxima method applications
Scientific Rigor Meets Clinical Innovation
Our extensive research foundation provides the scientific credibility that distinguishes WAVED from uninterpretable AI approaches. Every aspect of our technology is grounded in peer-reviewed research and validated through rigorous academic study.