Development of Clinically Translatable Technologies for Optical Image-Guided Breast Tumor Removal Surgery
The rate of occurrence and number of deaths associated with cancer continues to climb each year despite the continual efforts to battle the disease. When given a cancer diagnosis, it is particularly demoralizing and devastating news to a patient. Generally, cancer is defined as the uncontrolled rapid growth of abnormal cells with metastatic potential. In the cancer types originating from solid tissue or organ sites, a tumor will grow as a result of this rapid proliferation of cells. Surgical resection is a commonly used as part of the treatment regimen prescribed for these types of cancer.
Specifically in breast cancer, which impacts over 200,000 women a year, surgical intervention is used in almost 92% of treated cases. A specific surgical procedure is known as breast conserving surgery (BCS), where the physician removes only the tumor, while retaining as much normal tissue as possible. BCS is used in 59% of cases and is generally more preferable than the more radically mastectomy procedure where the entire breast is removed.
To minimize the chance of local recurrence, it is vital that the tumor is completely removed and residual cancer cells are not still present in the patient. This diagnosis is made by inspecting the edge of the resected tumor mass, typically known as the surgical margin. If tumor cells are still present at the margin, then a positive diagnosis is given and tumor cells likely remain inside the patient. Unfortunately, since margins are typically diagnosed using post-operative pathology a patient with a positive margin must undergo a second re-excision operation to remove additional tissue.
For breast cancer patients undergoing BCS, a staggering 20-70% of patients must undergo additional operations due to incomplete tumor removal during the first procedure.
Currently, there are two intra-operative techniques that are used, frozen section analysis and touch prep cytology. Although both have been proven to be effective in reducing re-excision rates, both techniques require
There remains a clinical unmet need for an intra-operative technology capable of quickly diagnosis tumor margins during the initial surgical operation
Optical technologies provide an attractive method of quickly and non-destructively assessing tissue. These techniques rely the interactions of light with tissue, which include absorption, scattering, and fluorescence. Utilizing proper measurement systems, these interactions can be measured and exploited to yield specific sources of contrast in tissue. In this dissertation, I have focused on developing two specific optical techniques for the purpose of surgical margin assessment.
The first is diffuse reflectance spectroscopy (DRS) which is a specific method to extract quantitative biological composition of tissues has been used to discern tissue types in both pre-clinical and clinical cancer studies. Typically, diffuse reflectance spectroscopy systems are designed for single-point measurements. Clinically, an imaging system would provide valuable spatial information on tissue composition. While it is feasible to build a multiplexed fiber-optic probe based spectral imaging system, these systems suffer from drawbacks with respect to cost and size. To address these I developed a compact and low cost system using a broadband light source with an 8-slot filter wheel for illumination and silicon photodiodes for detection. The spectral imaging system was tested on a set of tissue mimicking liquid phantoms which yielded an optical property extraction accuracy of 6.40 ± 7.78% for the absorption coefficient (µa) and 11.37 ± 19.62% for the wavelength-averaged reduced scattering coefficient (µs').
While DRS provided one potential approach to margin diagnosis, the technique was inherently limited in terms of lateral resolution. The second optical technique I chose to focus on was fluorescence microscopy, which had the ability to achieve lateral resolution on the order of microns. Cancer is associated with specific cellular morphological changes, such as increased nuclear size and crowding from rapidly proliferating cells. In situ tissue imaging using fluorescent stains may be useful for intraoperative detection of residual cancer in surgical tumor margins. I developed a widefield fluorescence structured illumination microscope (SIM) system with a single-shot FOV of 2.1×1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 µm). The objectives of this work were to measure the relationship between illumination pattern frequency and optical sectioning strength and signal-to-noise ratio in turbid (i.e. thick) samples for selection of the optimum frequency, and to determine feasibility for detecting residual cancer on tumor resection margins, using a genetically engineered primary mouse model of sarcoma. The SIM system was tested in tissue mimicking solid phantoms with various scattering levels to determine impact of both turbidity and illumination frequency on two SIM metrics, optical section thickness and modulation depth. To demonstrate preclinical feasibility, ex vivo 50 µm frozen sections and fresh intact thick tissue samples excised from a primary mouse model of sarcoma were stained with acridine orange, which stains cell nuclei, skeletal muscle, and collagenous stroma. The cell nuclei were segmented using a high-pass filter algorithm, which allowed quantification of nuclear density. The results showed that the optimal illumination frequency was 31.7 µm−1 used in conjunction with a 4x 0.1 NA objective. This yielded an optical section thickness of 128 µm and an 8.9x contrast enhancement over uniform illumination. I successfully demonstrated the ability to resolve cell nuclei in situ achieved via SIM, which allowed segmentation of nuclei from heterogeneous tissues in the presence of considerable background fluorescence. Specifically, I demonstrated that optical sectioning of fresh intact thick tissues performed equivalently in regards to nuclear density quantification, to physical frozen sectioning and standard microscopy.
However the development of the SIM system was only the first step in showing potential application to surgical margin assessment. The nest study presented in this dissertation was to demonstrate clinical viability on a sample size of 23 animals. The biological samples used in this study were a genetically engineered mouse model of sarcoma, where a spontaneous solid tumor was grown in the hind leg. After the tumor was surgically removed from the animal and the relevant margin was stained with acridine orange (AO), a simple and widely available contrast agent that brightly stains cell nuclei and fibrous tissues. The margin was imaged with the SIM system with the primary goal of visualizing specific morphological changes in cell nuclei. To automatically segment AO-stained regions, an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images.
As an intermediate step prior to diagnosing whole margins, a tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER nuclei segmentation output. A logistic regression model was used which yielded a final output in terms of probability (0-100%) the tumor within the localized region. The model performance was tested using an ROC curve analysis that revealed a 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that at a tumor probability threshold of 50% only 8% of all regions from a negative margins exceeded this threshold, while over 25% of all regions exceeded the threshold in the positive margins.
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