Breast Mapping with Feminai for Cancer Detection
This study is evaluating Feminai 1.0, an at-home adhesive patch that measures skin temperature and electrical impedance to create a "map" of each breast. Researchers want to see if Feminai can help identify suspicious areas that might need further medical checks. The study aims to enroll up to 300 women, aged 25-75, who are already getting breast cancer screenings. Feminai results will be compared to mammograms and, if applicable, biopsy results to see how well it detects suspicious lesions and how accurately it rules them out. Feminai is meant to support doctors' decisions and does not replace standard diagnostic tests. The study's success will be measured by how accurately Feminai identifies (sensitivity) and rules out (negative predictive value) suspicious lesions.
- Study design
- This is an observational study involving up to 300 women. It is not specified if it is randomized or blinded.
- What's involved
- You would have one study visit where trained staff apply the Feminai patch to your breasts and you complete a short questionnaire.
- Compensation
- Not stated in the trial record.
- Follow-up
- You would be followed for up to 6 months after enrollment to compare Feminai results with mammography and biopsy results.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Breast Mapping and Detection of Suspicious Breast Lesions Using Feminai
At a glance
Conditions
Where it's being run
1 sites across 1 statesWho to contact
Opens a ready-to-send draft in your own email app — review before sending.
What this trial measures
- Sensitivity and NPVFrom enrollment to the time the the ground truth is available followed for up to 6 months
- Sensitivity and NPVFrom enrollment to the time that the mammography and biopsy results (if applicable) are available followed for up to 6 months.
The co-primary endpoints will be defined on a per patient basis as sensitivity and NPV. The endpoints will be based on the binary outcomes produced for each breast side (right and left). For side-level analysis, the definitions of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) are as follows: TP side: Side is Ground Truth (GT) positive and Flagged by device FP side: Side is GT negative and Flagged by device FN side: Side is GT positive and Not Flagged by device TN side: Side is GT negative and Not Flagged by device For subject-level analysis, the definitions of TP, FP, TN, and FN are based on side-level classification, as follows: TP subject: A subject has only one GT positive side and this side is Flagged by the device. Or, a subject has 2 GT positive sides and at least one side is Flagged by device. FN subject: A subject has one or two GT positive sides and none of these sides is Flagged by the device FP subject: A subject has both sides GT