Case Collection Study to Support Digital Mammography Image Software Change

Not Applicable

Completed

• Conditions: Breast Cancer
• Interventions: Device: Mammography screening and diagnosis

• Registration Number: NCT00756496
• Lead Sponsor: Siemens Medical Solutions USA - CSG

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2006-11
• Study First Submitted: 2008-09-18
• Study First Submitted QC: 2008-09-19
• Study First Posted: 2008-09-22
• Primary Completion: 2008-12
• Study Completion: 2009-01
• Results First Submitted: 2020-08-17
• Status Verified: 2020-11
• Results First Submitted QC: 2020-11-11
• Last Update Submitted: 2020-11-11
• Results First Posted: 2020-12-07
• Last Update Posted: 2020-12-07

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 442

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
1	Mammography screening and diagnosis	-

Primary Outcome Measures

Name	Time	Method
Area Under the Receiver Operating Characteristic (ROC) Curve to Compare Diagnostic Accuracy of 2 Algorithms in Breast Cancer Diagnosis	~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.