Breast Cancer
Conditions
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.
Interventions
DEVICEMammography screening and diagnosis
Mammography screening and diagnosis
Sponsors
Siemens Medical Solutions USA - CSG
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
40 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| FFDM Mammography Examination Screening or diagnostic mammography exam. | 442 |
| Total | 442 |
Baseline characteristics
| Characteristic | FFDM Mammography Examination |
|---|---|
| Age, Customized >=40 years old | 442 Participants |
| Region of Enrollment United States | 442 Participants |
| Sex/Gender, Customized Female | 442 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | — / — |
| other Total, other adverse events | 0 / 442 |
| serious Total, serious adverse events | 0 / 442 |
Outcome results
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.
Time frame: ~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.
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| FFDM Mammography Exam - LIP 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 |
| FFDM Mammography Exam - SIP Algorithm | Area Under the Receiver Operating Characteristic (ROC) Curve to Compare Diagnostic Accuracy of 2 Algorithms in Breast Cancer Diagnosis | 0.880 probability | Standard Error 0.008 |