Mission Statement

The goal of this research is to enhance intraoperative breast cancer margin assessment through high-frequency quantitative ultrasound (QUS). By developing a more accurate, cost-effective, and real-time method for distinguishing malignant from non-malignant tissues, this study aims to reduce the need for repeat surgeries, improve patient outcomes, and contribute to the advancement of ultrasound-based tissue characterization techniques.

Background

Breast cancer remains one of the most common cancers globally, with millions of new cases annually. Advancements in early detection and treatment have improved survival rates, yet challenges persist in breast-conserving surgery (BCS). Surgeons must ensure negative surgical margins—complete tumor removal with minimal excision of healthy tissue—to reduce recurrence. Current intraoperative margin assessment techniques, such as frozen section analysis and touch preparation cytology, have limitations in accuracy, efficiency, and accessibility.

High-frequency ultrasound (HFU), operating at frequencies above 10 MHz, offers a promising alternative by providing high-resolution tissue characterization. Unlike traditional B-mode ultrasound, QUS enables quantitative differentiation of tissues by analyzing attenuation, scatter, and speed of sound, all of which correlate with tissue microstructure. Initial studies, including those by Doyle and colleagues, demonstrate that QUS parameters such as attenuation coefficient slope (ACS) and peak density can effectively distinguish cancerous from normal tissues. However, significant gaps remain in optimizing these techniques for clinical application.

Main Goals

This research seeks to:

1. Refine high-frequency QUS techniques for intraoperative breast tissue characterization, focusing on attenuation, scatter, and spectral analysis.

2. Improve scanning methodologies by developing automated multi-point scanning patterns to minimize false negatives and increase sampling accuracy.

3. Enhance tissue property analysis by integrating spectral peak analysis to infer tissue elasticity, which is associated with cancerous microenvironments.

4. Validate findings with a larger dataset by conducting studies on diverse tissue samples to improve clinical reliability and reproducibility.

5. Develop a practical intraoperative device that integrates force and displacement sensors to control for tissue compression and variability in QUS measurements.

Opportunities for Advancement

• Multivariate Analysis for Higher Accuracy: Combining QUS parameters such as ACS, peak density, and elasticity may improve diagnostic precision beyond single-factor differentiation.

• Automation for Clinical Feasibility: Implementing structured scanning patterns (e.g., spiral or raster) can enhance sampling consistency and reduce operator dependence.

• Integration with Existing Surgical Workflows: By optimizing for real-time margin assessment, QUS technology has the potential to replace or complement current histopathological methods, improving surgical decision-making.

• Expansion to Other Tissues: While this research focuses on breast tissue, the methodologies could be adapted for characterizing other heterogeneous tissues, such as liver fibrosis or thyroid tumors.

By addressing these gaps, this research aims to establish high-frequency QUS as a reliable, real-time diagnostic tool, ultimately improving breast cancer surgical outcomes while reducing healthcare costs and patient burden.