Case Collection Study to Support Digital Mammography Image Software Change (LIP2SIP)
A Multi-center Feature Analysis Study to Compare the Diagnostic Accuracy of Siemens' Image Processing (SIP) Algorithms With Lorad's Image Processing (LIP) Algorithms in Detecting and Characterizing Breast Lesions
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Female
- > 40 years
Exclusion Criteria:
- Pregnant women, or women who may become pregnant
- Mammographic evidence of breast surgery, prior radiation to the breast, needle projection or pre-biopsy markings are evident in the mammogram (but may include breast implants)
- Palpable lesion or one that is visible by another modality
- Inmates
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: 1
|
Mammography screening and diagnosis
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Area Under the Receiver Operating Characteristic (ROC) Curve to Compare Diagnostic Accuracy of 2 Algorithms in Breast Cancer Diagnosis
Time Frame: ~1 year. Women with negative or biopsy benign findings at baseline (study entry) were followed for 1 year to confirm the negative status at 1-year follow-up mammography exam. Women diagnosed with cancer were not followed up.
|
The primary objective of this study was to demonstrate non-inferiority of the Siemens' processing algorithm to Lorad's processing algorithm with regards to readers' diagnostic accuracy in detecting and characterizing breast lesions. The non-inferiority analyses were performed by comparing the area under the ROC curve (AUC) for the two algorithms & to compare false positive marks per subject. The ROC curve incorporates both sensitivity (true positive rate) and specificity (true negative rate) providing a single assessment incorporating both measures. It shows in a graphical way the trade-off between clinical sensitivity and specificity for every possible cut-off for a test, and gives an idea about the benefit of using the test in question. The higher the total area under the curve, the greater the predictive power of the reader assessments. A breast-based analysis was used for the primary AUC comparison in order to obtain additional power by having more normal/benign breasts. |
~1 year. Women with negative or biopsy benign findings at baseline (study entry) were followed for 1 year to confirm the negative status at 1-year follow-up mammography exam. Women diagnosed with cancer were not followed up.
|
Collaborators and Investigators
Investigators
- Study Director: Raymond C Duhamel, Ph.D., Siemens Medical Solutions USA, Inc
Study record dates
Study Major Dates
Study Start
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- SMS-SP04-06
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