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| ID | Type | Description | Link |
|---|---|---|---|
| Bracco | Other Grant/Funding Number | Bracco |
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| Name | Class |
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| Bracco Corporate | INDUSTRY |
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The purpose of this study is to evaluate the ability of AI to correctly aid in characterization of benign and malignant lesions even when a low dose of gadolinium is administered. This study is relevant for several reasons, most notably being the reduction of MRI dose and decreased gadolinium deposition in the brain. In addition, use of AI may provide increased sensitivity and specificity for the radiologist evaluating a breast MRI exam. Half of the population will have benign pathologies and the other half will have malignant pathologies.
The study involves each patient presenting for an initial MRI with a regular dose of gadolinium and then presenting at least 48 hours after (no less than 14 days later) for a ¼ dose (see below regarding dosing) gadolinium MRI exam. Both exams will be performed on a 1.5 Tesla magnet. Both exams will include a full protocol. The full dose contrast exam will be read as standard protocol. All images will be anonymized. Images from the reduced dose study will be collected and an AI algorithm applied. All three anonymized data sets (regular dose, low dose, and AI algorithm applied to low dose) will be provided to the readers.
Readers will be three attending radiologists specializing in breast imaging. Exams will be scored on quality, background parenchymal enhancement (BPE), and lesion conspicuity. Enhancing lesions will be identified and characterized by the radiologists in a document provided.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Subjects with enhancing breast lesions | Experimental |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Standard of Care (SOC) gadolinium Breast MRI | Drug | Standard of Care (SOC) gadolinium Breast MRI |
|
| Measure | Description | Time Frame |
|---|---|---|
| Compare lesion conspicuity on a scale of 1-4 from low-dose and SOC-dose imaging. | From 1st SOC breast MRI to 2nd low-dose Breast MRI (48 hours to 14 days later). | |
| Compare lesion size in millimeters from low-dose to SOC-dose imaging. | From 1st SOC breast MRI to 2nd low-dose Breast MRI (48 hours to 14 days later). | |
| Compare lesion margins on a scale from 1-4 from low-dose and SOC-dose imaging. | From 1st SOC breast MRI to 2nd low-dose Breast MRI (48 hours to 14 days later). | |
| Compare lesion internal enhancement pattern on a scale from 1-4 from low-dose and SOC- dose imaging. | From 1st SOC breast MRI to 2nd low-dose Breast MRI (48 hours to 14 days later). |
| Measure | Description | Time Frame |
|---|---|---|
| Compare the overall background enhancement (scale per standard imaging guidelines. Minimal, mild, moderate, marked). | From 1st SOC breast MRI to 2nd low-dose Breast MRI (48 hours to 14 days later). |
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Inclusion Criteria for Breast Cancer Patients:
Inclusion criteria for Non-malignant indications:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Stefanie Woodard, MD | University of Alabama at Birmingham | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The Kirklin Clinic | Birmingham | Alabama | 35249 | United States |
To be determined.
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| reduced 1/4 dose gadolinium Breast MRI with Artificial Intelligence (AI) to aid in evaluation. | Drug | reduced 1/4 dose gadolinium Breast MRI with Artificial Intelligence (AI) to aid in evaluation. |
|
| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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| ID | Term |
|---|---|
| D059039 | Standard of Care |
| D001185 | Artificial Intelligence |
| ID | Term |
|---|---|
| D019984 | Quality Indicators, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
| D017530 | Health Care Quality, Access, and Evaluation |
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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