Principal investigators: Dr. Gregory Czarnota and Dr. Michael Kolios
Quantitative ultrasound methods are based on frequency-dependent spectral analyses, which can be used to generate parameters that may be related to acoustic properties of tissues. These include parameters such as acoustic scatterer size, the concentration of scatterers, and higher-order parameters. We have demonstrated the applicability of these methods to the detection of structural changes in tissue related to cell death, and, more recently, to the novel use of optical and ultrasound methods for the early detection of tumour responses in breast cancer patients. Our most recent work demonstrates, for the first time, that quantitative ultrasound spectroscopic methods can be used to predict chemotherapy outcomes and patient survival before initiation of chemotherapy. We have extended aspects of this research to noninvasively diagnose malignant breast cancer with over 90% accuracy, in addition to predicting therapy response.
We will conduct investigations of these new quantitative ultrasound texture-based techniques to establish the sensitivity, specificity, and detection limits for the clinical range of ultrasound frequencies and analytical techniques. This technology’s broad scope spans from diagnosis, to prediction of therapy response, to monitoring therapy response for cure-oriented cancer care personalization.
We will collect quantitative ultrasound data from 200 breast cancer patients at the time of their diagnosis, prior to chemotherapy, and at set times during their chemotherapy treatment. Data will be analyzed using quantitative ultrasound methods, including new textural-based techniques. Data will be tested independently in an additional cohort of 200 patients. The sensitivity and specificity of the methods and the best combination of specific quantitative parameters will be determined for the prediction of therapy response, and for monitoring therapy responses.
The ultimate goal is to use quantitative ultrasound imaging to improve several aspects of breast cancer patient care. We will work towards establishing its use as a clinical tool for characterizing, diagnosing, and monitoring the treatment of malignant breast tumours. The method will also permit novel predictions of response to chemotherapy in addition to treatment monitoring, and will permit non-invasive diagnostic, objective assessments of response, and empowering oncologists to customize therapies upfront by switching from ineffective to effective therapies early on in treatment regimens. Our technology therefore has the potential improve outcomes for breast cancer patients.