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Thesis (M.S.)--Boston University

Breast cancer is one of the most widespread types of cancer in the world. The key factor in treatment is to reliably diagnose the cancer in the early stages. Moreover, currently used clinical diagnostic methods, such as X-ray, ultra-sound and MRI, are limited by cost and reliability issues. These limitations have motivated researchers to develop a more effective, low-cost diagnostic method and involving lower ionization for cancer detection. In this thesis, radar based microwave imaging is proposed as a method for early breast cancer detection. This imaging system has advantages such as low cost, being non- invasive and easy to use, with high image resolution and its thus good potential for early cancer detection. In the first stage, an ultra-wideband Vivaldi antenna and a slot Vivaldi antenna are proposed, simulated and fabricated for breast cancer detection. The designed antennas exhibit an ultra-wideband working frequency. The radiation patterns also achieve the desired directional radiation patterns. The second stage of this study presents a planar breast phantom and a hemisphere breast phantom. These two breast phantoms are simulated and fabricated using CST microwave studio and tissue-mimicking materials respectively. Mono-static radar systems based on a single antenna configuration and an antenna pair configuration are then proposed. These two systems are used to measure the planar breast phantom and hemi- sphere breast phantom, with the scattering signals measured in the frequency and time domains. Based on the measurement results, it is concluded that the reflected energy increases when the antenna moves close to the tumour; otherwise, the reflected energy is reduced when the antenna moves away from the tumour. The received time domain scattering signals are processed first and then used to create microwave images to indicate tumour position. A clutter removal method is proposed to extract the tumour response from the received signals. The microwave images are then created using the tumour response based on the simulation and experimental results. The imaging results indicate that a 5 mm radius tumour can be detected. The tumour burial depth is also studied. A multi bio- layer phantom which contains deep and shallow buried tumours is simulated and measured using the Vivaldi antenna. A spectrum analysis method is proposed to distinguish between different tumour depths. The results indicate that a difference in depth of 15 mm results in a mean change of 0.3 dB in the magnitude of the spectrum. Discrimination between benign and malignant tumours is also considered in this study. The singularity expansion method (SEM) for breast cancer is proposed to discriminate between benign and malignant tumours based on their morphology. Two cancerous breast phantoms are developed in CST. The benign tumour is a 5mm radius sphere and the malignant tumour is a spiny sphere with an average radius of 5mm. The use of the SEM leads to the successful discrimination of these two tumours. This method provides a solution to discriminate between benign and malignant tumours similar size when the resulting images cannot provide sufficient resolution. A preliminary study of brain cancer detection is also concluded. Research in this area has never been implemented. A cancerous brain model is designed and simulated in CST. The antenna pair configuration is then used to measure the cancerous brain, with the scattering signals measured. Microwave images for brain cancer detection are then created based on the measurement results. The tumour is correctly indicated in the resulting images.

Background: Axillary lymph node dissection as routine part of breast cancer treatment has been questioned in relation to the balance between benefits and morbidity. The purpose of this study is to determine the association of tumor size, age and histological grade with axillary lymph node metastasis, to determine if some patients could be exempted from axillary dissection. Methods: The data are derived from BreastScreen NSW, the government sponsored population-based breast screening program. In New South Wales (NSW) Australia between 1995 and 2002, 7,221 patients with invasive breast carcinoma were diagnosed and 5,290 patients were eligible for this study. The relationship between incidence of positive axillary lymph nodes and three study factors (tumor size, age and histological grade) was investigated by univariate and multivariate analysis. Logistic regression models were used to predict probability of axillary metastases. Results: The incidence of axillary lymph node metastases was 28.6% (95% CI: 27.4%- 29.8%). Univariate analysis showed that age, tumor size and histological grade were significant predictors of axillary lymph node metastases (p<0.0001). Multivariate analysis identified age, tumor size and histological grade remained as independent predictors (p<0.0001). From multivariate analysis, patients with T1a (Less than or equal to 5mm) and grade I tumors regardless of age had 5.2% (95% CI: 1.2%- 9.3%) frequency of node metastases. Patients 70 years or older with grade I, T1a and T1b (6-10mm) tumors had 4.9% (95% CI: 3.2%- 7.5%) and 6.6% (95% CI: 5.3%-8.3%) predicted frequency of node metastases. Conclusions: Tumor size, age and histological grade are predictors of axillary lymph node metastases. Routine axillary lymph node dissection could be avoided in some patient groups with a low frequency of involved lymph nodes if the benefits are considered to exceed the risks.

Background: Axillary lymph node dissection as routine part of breast cancer treatment has been questioned in relation to the balance between benefits and morbidity. The purpose of this study is to determine the association of tumor size, age and histological grade with axillary lymph node metastasis, to determine if some patients could be exempted from axillary dissection. Methods: The data are derived from BreastScreen NSW, the government sponsored population-based breast screening program. In New South Wales (NSW) Australia between 1995 and 2002, 7,221 patients with invasive breast carcinoma were diagnosed and 5,290 patients were eligible for this study. The relationship between incidence of positive axillary lymph nodes and three study factors (tumor size, age and histological grade) was investigated by univariate and multivariate analysis. Logistic regression models were used to predict probability of axillary metastases. Results: The incidence of axillary lymph node metastases was 28.6% (95% CI: 27.4%- 29.8%). Univariate analysis showed that age, tumor size and histological grade were significant predictors of axillary lymph node metastases (p<0.0001). Multivariate analysis identified age, tumor size and histological grade remained as independent predictors (p<0.0001). From multivariate analysis, patients with T1a (Less than or equal to 5mm) and grade I tumors regardless of age had 5.2% (95% CI: 1.2%- 9.3%) frequency of node metastases. Patients 70 years or older with grade I, T1a and T1b (6-10mm) tumors had 4.9% (95% CI: 3.2%- 7.5%) and 6.6% (95% CI: 5.3%-8.3%) predicted frequency of node metastases. Conclusions: Tumor size, age and histological grade are predictors of axillary lymph node metastases. Routine axillary lymph node dissection could be avoided in some patient groups with a low frequency of involved lymph nodes if the benefits are considered to exceed the risks.

No description available.

Master of Science / Department of Statistics / Gary Gadbury / This report illustrates how to use two statistic methods to investigate the performance of a new technique to detect breast cancer and lung cancer at early stages. The two methods include logistic regression and classification and regression tree (CART). It is found that the technique is effective in detecting breast cancer and lung cancer, with both sensitivity and specificity close to 0.9. But the ability of this technique to predict the actual stages of cancer is low. The age variable improves the ability of logistic regression in predicting the existence of breast cancer for the samples used in this report. But since the sample sizes are small, it is impossible to conclude that including the age variable helps the prediction of breast cancer. Including the age variable does not improve the ability to predict the existence of lung cancer. If the age variable is excluded, CART and logistic regression give a very close result.