Quantitative Ultrasound Characterization and Monitoring of Locally Advanced Breast Cancer
type: CreativeWork
description: Traditional assessment of tumour response to cancer therapy is based on tumour size reduction, which takes several weeks to become clinically significant. In this thesis, novel ultrasound backscatter signal analysis and machine learning techniques were developed to characterize breast tumours and detect early changes correlated to response. In the first study, tumour cell death was induced in human breast cancer tumour-bearing mice, using human mimicking chemotherapy drugs. Treatment-related changes in quantitative ultrasound (QUS) parameters, including change in average acoustic concentration (AAC) and heterogeneity index, revealed a strong correlation to histologically determined cell death extent (r2=0.64). In the second study, radiofrequency (RF) ultrasound data were acquired from locally advanced breast cancer (LABC) patients prior to treatment. Results suggested that a multiparameter QUS model can sensitively (88%) and specifically (91%) differentiate breast tumours from surrounding normal tissue. Furthermore, a local texture - based QUS model was demonstrated as a promising tumour grade predictor (86% accuracy). In the final study, ultrasound RF data were acquired from LABC patients prior to treatment, at 3 times during the treatment (weeks 1, 4, 8), and prior to surgery. Tumour response classification analysis using a multiparameter QUS model of midband fit (MBF), spectral slope (SS), and spacing among scatterers (SAS) demonstrated desirable classification performance at 4 weeks into treatment (80 ± 5%). Secondly, the QUS classification model demonstrated a significant difference in survival rates of responding and nonresponding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). In summary, the incorporation of QUS assessment of the breast during or after an ultrasound-guided breast biopsy session may potentially permit cross-verification of the histopathological findings. Furthermore, patients undergoing neoadjuvant chemotherapy can potentially b…
abstract: Traditional assessment of tumour response to cancer therapy is based on tumour size reduction, which takes several weeks to become clinically significant. In this thesis, novel ultrasound backscatter signal analysis and machine learning techniques were developed to characterize breast tumours and detect early changes correlated to response. In the first study, tumour cell death was induced in human breast cancer tumour-bearing mice, using human mimicking chemotherapy drugs. Treatment-related changes in quantitative ultrasound (QUS) parameters, including change in average acoustic concentration (AAC) and heterogeneity index, revealed a strong correlation to histologically determined cell death extent (r2=0.64). In the second study, radiofrequency (RF) ultrasound data were acquired from locally advanced breast cancer (LABC) patients prior to treatment. Results suggested that a multiparameter QUS model can sensitively (88%) and specifically (91%) differentiate breast tumours from surrounding normal tissue. Furthermore, a local texture - based QUS model was demonstrated as a promising tumour grade predictor (86% accuracy). In the final study, ultrasound RF data were acquired from LABC patients prior to treatment, at 3 times during the treatment (weeks 1, 4, 8), and prior to surgery. Tumour response classification analysis using a multiparameter QUS model of midband fit (MBF), spectral slope (SS), and spacing among scatterers (SAS) demonstrated desirable classification performance at 4 weeks into treatment (80 ± 5%). Secondly, the QUS classification model demonstrated a significant difference in survival rates of responding and nonresponding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). In summary, the incorporation of QUS assessment of the breast during or after an ultrasound-guided breast biopsy session may potentially permit cross-verification of the histopathological findings. Furthermore, patients undergoing neoadjuvant chemotherapy can potentially b…
author: Tadayyon, Hadi
date published: 2015-11
date modified: 2026-07-27T21:28:20Z
provider: University of Toronto
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Toronto

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1807/71348