Tissue Oxygen Imaging for Non-invasive Metastasis Detection

Abstract

As metastasis is responsible for a majority of cancer deaths, early and accurate metastasis detection is vital for developing better treatment plans and improving survival rates. Sentinel lymph node biopsy is the gold standard for metastasis detection for many cancers. It involves injecting a blue dye called methylene blue and/or a radiocolloid at the primary tumor site and identifying the lymph nodes to which it drains. These nodes are then excised and sent for histopathological analysis to determine the presence of metastasis. While this method has high sensitivity rates (~90%), it is associated with complications and reduces the patient’s quality of life. Non-invasive metastasis detection is also used clinically but these detection methods lack high sensitivity for small metastases. Higher sensitivity detection techniques enable earlier treatment which may improve survival rates. With recent evidence demonstrating significantly reduced oxygen in metastatic nodes, non-invasive oxygen imaging may improve metastasis detection while avoiding surgical morbidities.This thesis explores the application of a novel oxygen imaging method called Photoacoustic Lifetime Imaging (PALI) for early metastasis detection. To establish that the system works, PALI’s accuracy is first evaluated by measuring its correlation to measurements from a commercial dissolved oxygen probe. PALI was shown to be an accurate oxygen sensor as a strong correlation was observed (R2 = 0.998). Simulations and bench top experiments in a tissue mimicking phantom were then used to determine PALI’s imaging depth. It was found that PALI has an imaging depth between 13 and 16 mm, which is deep enough to image most oral cavity metastases. To improve the imaging depth to image all oral cavity metastases, a simulation was used to vary the lymph node concentration, detector bandwidth, and laser energy. The simulations show that, for small metastases, PALI achieves a sensitivity and specificity of 92 and 95%, respectively. Additionally, it can detect micro-metastases, a functionality that is currently beyond clinically available imaging technologies. While more work must be done to evaluate PALI in-vivo, with a clinically relevant imaging depth and sensitive metastasis detection, this technology may enable earlier treatments and greater survival rates for those with metastases and a reduction in unnecessary surgical procedures for those without.