Trial Outcomes & Findings for Case Collection Study to Support Digital Mammography Image Software Change (NCT NCT00756496)

Results Overview

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.

Recruitment status

COMPLETED

Study phase

NA

Target enrollment

442 participants

Primary outcome timeframe

~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.

Results posted on

2020-12-07

Participant Flow

Participant milestones

Participant milestones
Measure	Mammography Exam Full Field Digital Mammography exam
Overall Study STARTED	442
Overall Study COMPLETED	442
Overall Study NOT COMPLETED	0

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

Case Collection Study to Support Digital Mammography Image Software Change

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	FFDM Mammography Examination n=442 Participants Screening or diagnostic mammography exam.
Age, Customized >=40 years old	442 Participants n=5 Participants
Sex/Gender, Customized Female	442 Participants n=5 Participants
Region of Enrollment United States	442 Participants n=5 Participants

PRIMARY outcome

Timeframe: ~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.

Outcome measures

Outcome measures
Measure	FFDM Mammography Exam - LIP Algorithm n=260 breasts Screening or diagnostic Full Field Digital Mammography (FFDM) exam	FFDM Mammography Exam - SIP Algorithm n=260 breasts The same 130 raw data images were externally reprocessed with the Siemens processing algorithm.
Area Under the Receiver Operating Characteristic (ROC) Curve to Compare Diagnostic Accuracy of 2 Algorithms in Breast Cancer Diagnosis	0.884 probability Standard Error 0.008	0.880 probability Standard Error 0.008

Adverse Events

Mammography Exam

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Adverse event data not reported

Additional Information

Milind Dhamankar

Siemens Medical Solutions USA, Inc.

Phone: +1 (610) 448-6467

Email: [email protected]

Results disclosure agreements

Principal investigator is a sponsor employee
Publication restrictions are in place