Optics and acoustics for margin assessment during breast-conserving surgeries

The primary objective of breast-conserving surgery (BCS) is to completely excise the tumor while preserving a small margin of healthy breast tissue, striking a balance between ensuring total tumor removal and achieving an optimal cosmetic outcome. In cases where the surgical margin is positive, indicating incomplete tumor removal, patients often require additional treatment, such as radiotherapy or re-excision, which may involve a repeat BCS or even mastectomy. These additional procedures carry potential risks, including increased morbidity, compromised cosmetic results, reduced quality of life, and higher healthcare costs. Thus, achieving complete tumor removal during the initial surgery is critical.

However, excessively large resections can negatively impact cosmetic outcomes. Therefore, it is crucial for breast cancer surgeons to minimize the excision of healthy breast tissue while ensuring complete tumor removal. Currently, surgeons primarily rely on visual and tactile feedback to differentiate between healthy and cancerous tissue, which presents significant challenges in accurately identifying tumor boundaries. In standard clinical practice, margin assessment is performed postoperatively through histopathological analysis, often leading to re-excision in a subset of patients. This highlights the clinical need for an accurate, real-time intraoperative method to assess breast cancer margins, ensuring both complete tumor resection and optimal cosmetic results.

This thesis seeks to address the existing gap in intraoperative margin assessment by exploring two different yet complementary modalities: ultrasound (US) imaging and diffuse reflectance spectroscopy (DRS) combined with artificial intelligence.