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Our objective in this study is to identify an optimal set of quantitative ultrasound parameters that can be used, non-invasively, to characterize breast masses with high accuracy, as determined histopathologically. Breast cancer is the most frequent form of non-epithelial cancer diagnosed in women, with approximately 1.5 million new cases diagnosed annually worldwide. Accurate diagnosis and characterization of disease play an important role in therapy planning for breast cancer treatment. Currently, the gold standard method of tumour diagnosis is pathological examination of core biopsy specimens. However, the invasive core biopsies can cause post-surgical complications. Besides, some lesions require repeat biopsy due to sampling errors during the initial biopsy. Also X-ray mammography and ultrasound B-mode images, which are used by radiologists for breast examination, lack reliable information about micro-structural properties of tissues. There is an urgent need of a non-invasive imaging modality that can provide rapid and quantitative information for breast tumour characterization, in real time and at the patient bed. The main goal, as described above, is to select the best quantitative ultrasound parameters that can facilitate breast cancer characterization, non-invasively.
This project is an observational/early validation study in human subjects that will use ultrasound imaging and spectroscopy to characterize suspected breast cancers. Patients will be imaged with ultrasound, and the acquired data will be analyzed using quantitative ultrasound techniques, in conjunction with textural analysis on generated parametric images. Results of quantitative ultrasound data analysis for these breast lumps will be compared to and correlated with their histopathological characteristics from pathology reports on core biopsy specimens, surgery reports, or radiology reports, available from patient charts. From this data we will potentially estimate the best hybrid ultrasound-based biomarker that can characterize suspected breast cancers, noninvasively.
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| Measure | Description | Time Frame |
|---|---|---|
| Select the best quantitative ultrasound parameters that can facilitate breast cancer characterization, non-invasively | Correlate quantitative ultrasound parameters obtained using spectroscopic techniques, in conjunction with textural analysis on generated parametric images, to the histopathological characteristics from pathology reports on core biopsy specimens, surgery reports, or radiology reports. | Up to 5 years |
| Measure | Description | Time Frame |
|---|---|---|
| Results of ultrasound-based breast cancer characterization and clinical outcomes. | Correlating the results of ultrasound-based breast cancer characterization. | Up to 5 years |
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Inclusion Criteria:
Exclusion Criteria:
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Both men and women and members of all races and ethnic groups are eligible for this trial.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Gregory J Czarnota, PhD, MD | Contact | (416)480-6128 | gregory.czarnota@sunnybrook.ca | |
| Schontal Halstead | Contact | 416-480-6100 | 89533 |
| Name | Affiliation | Role |
|---|---|---|
| Gregory J Czarnota, PhD, MD | Sunnybrook Health Science Centre | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sunnybrook Health Sciences Centre | Recruiting | Toronto | Ontario | M4N 3M5 | Canada |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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| D017437 |
| Skin and Connective Tissue Diseases |