Case Collection Study to Support Digital Mammography Image Software Change

Breast Cancer Clinical Trial

— LIP2SIP  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT00756496
• Other study ID #: SMS-SP04-06
• Status: Completed
• Phase: N/A
• Start date: November 2006
• Est. completion date: January 2009

Study information

• Verified date: November 2020
• Source: Siemens Medical Solutions USA - CSG
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 442
• Est. completion date: January 2009
• Est. primary completion date: December 2008
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 40 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer

Intervention

Device:
• Mammography screening and diagnosis
Mammography screening and diagnosis

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Siemens Medical Solutions USA - CSG

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.