Case Collection Study to Support Digital Mammography Imag... | NCT00756496

NCT00756496

Protocol Section

Identification Module

Results Section

Participant Flow Module

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Status Module

Oversight Module

Description Module

Design Module

Interventional Study Design

Biospecimen

No data available

No data is available for this block.

Label	Type	Description	Intervention Names
1	Experimental		Device: Mammography screening and diagnosis

Name	Type	Description	Arm Group Labels	Other Names
Mammography screening and diagnosis	Device	Mammography screening and diagnosis	1

Outcomes Module

Primary Outcomes

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.

Eligibility Module

Name	Affiliation	Role
Raymond C Duhamel, Ph.D.	Siemens Medical Solutions USA, Inc	Study Director

ID	Title	Description
FG000	Mammography Exam	Full Field Digital Mammography exam

Baseline Characteristics Module

ID	Title	Description
BG000	FFDM Mammography Examination	Screening or diagnostic mammography exam.

Units	Counts
Participants	BG000

Baseline Measures

Title	Description	Population Description	Parameter Type	Dispersion Type	Unit of Measure	Calculate Percentage	Denominator Units Selected	Denominators	Classes
Age, Customized			Count of Participants

Outcome Measures Module

Outcome Measures

Type	Title	Description	Population Description	Reporting Status	Anticipated Posting Date	Parameter Type	Dispersion Type	Unit of Measure	Calculate Percentage	Time Frame	Units Analyzed	Denominator Units Selected	Arm/Group Information	Denominators	Classes	Analyses
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.		Posted		Mean	Standard Error	probability		~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.	breasts	breasts

Adverse Events Module

Arm/Groups

ID	Title	Description	Deaths (Affected)	Deaths (At Risk)	Serious Events (Affected)	Serious Events (At Risk)	Other Events (Affected)	Other Events (At Risk)
EG000	Mammography Exam	FFDM screening or diagnostic mammography exam

More Info Module

Certain Agreements

Title	Organization	Phone	Extension	Email
Milind Dhamankar	Siemens Medical Solutions USA, Inc.	+1 (610) 448-6467		milind.dhamankar@siemens-healthineers.com

ID	Term
D001943	Breast Neoplasms

ID	Term
D009371	Neoplasms by Site
D009369	Neoplasms
D001941	Breast Diseases
D012871	Skin Diseases

ID	Term
D003933	Diagnosis

ID	Title	Description
OG000	FFDM Mammography Exam - LIP Algorithm	Screening or diagnostic Full Field Digital Mammography (FFDM) exam
OG001	FFDM Mammography Exam - SIP Algorithm	The same 130 raw data images were externally reprocessed with the Siemens processing algorithm.