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

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.

Study Overview

• Status: Completed
• Conditions: Breast Cancer
• Intervention / Treatment: Device: Mammography screening and diagnosis

• Study Type: Interventional
• Enrollment (Actual): 442
• Phase: Not Applicable

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: Female

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)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: 1	Device: Mammography screening and diagnosis; 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	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

• Sponsor: Siemens Medical Solutions USA - CSG
• Investigators: Study Director: Raymond C Duhamel, Ph.D., Siemens Medical Solutions USA, Inc

Study record dates

Study Major Dates

• Study Start: November 1, 2006
• Primary Completion (Actual): December 1, 2008
• Study Completion (Actual): January 1, 2009

Study Registration Dates

• First Submitted: September 18, 2008
• First Submitted That Met QC Criteria: September 19, 2008
• First Posted (Estimate): September 22, 2008

Study Record Updates

• Last Update Posted (Actual): December 7, 2020
• Last Update Submitted That Met QC Criteria: November 11, 2020
• Last Verified: November 1, 2020

More Information

Terms related to this study

• Other Study ID Numbers: SMS-SP04-06