Case Collection Study to Support Digital Mammography Image Software Change

Study Overview
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Brief Summary
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The primary objective of this study is to compare image processing software to support a new image processing software application for a full-field digital mammography (FFDM) system.

Official Title
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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

Conditions
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Breast Cancer

Intervention / Treatment
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* Device: Mammography screening and diagnosis


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 40 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: 1<br> | Device: Mammography screening and diagnosis<br>* Mammography screening and diagnosis<br>|

What is the study measuring?
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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 | 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. |