Assessment of the performance of a deep learning system at identifying breast cancer on screening mammograms using de-identified historic data.
- Conditions
- CancerBreast cancer
- Registration Number
- ISRCTN18056078
- Lead Sponsor
- Kheiron Medical Technologies
- Brief Summary
2023 Results article in https://pubmed.ncbi.nlm.nih.gov/37208717/ (added 22/05/2023)
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Completed
- Sex
- Female
- Target Recruitment
- 1000000
Current participant inclusion criteria as of 25/05/2021:
1. Female participants
2. 4-view mammography cases (with exactly one of each: MLO-R, MLO-L, CC-R, CC-L of the four standard views) produced by certified digital mammography hardware and taken for screening purposes
Previous participant inclusion criteria:
1. Female patients
2. Mammography cases for screening purposes, i.e. cases from:
a) patients involved in the national breast screening program (depending on the jurisdiction includes women of age
45-73 who are called for examination via a letter by the national health authorities based on the population database),
and
b) women outside the national breast screening program who decided on their own to participate as per standard of
care
3. Cases with images in DICOM format
4. Cases with images produced by certified digital mammography hardware
5. Cases with one set of all of the 4 standard mammography images (i.e. exactly one of each: MLO-R, MLO-L, CC-R,
CC-L) present (no images missing and no extra images)
6. Cases with available historical outcome information as specified below*:
(Outcome information:
Confirmed positive case: malignancy is confirmed by a decisive biopsy, cytology or histology of the surgical specimen
within 250 days after the time of the image acquisition date.
Confirmed negative case: a negative follow-up result is available at least 34 months after the image acquisition date
(with no malignant operation and no malignancy indication in that period.)
*This inclusion criteria only applies to sensitivity/specificity analysis (not recall rate analysis)
Current participant exclusion criteria as of 25/05/2021:
1. Male participants
2. Participants from whom any image data is used during training, calibration, or testing for the technology development of the deep learning model
3. Non-original, magnified, or spot-compressed images
Previous participant exclusion criteria:
1. Male patients
2. Images that are non-original images (e.g. post-processed images)
3. Magnified images (in the DICOM file the View Modifier Code Sequence (0054, 0222) has either of the values: R-
102D6, Magnification” or R-102D7, Spot compression”)
4. Cases with indication of a breast operation due to malignancy in the past medical history
5. Cases dated after a breast cancer confirmed by biopsy, cytology or histology
6. All patients of whom any image data was used during training, calibration or testing during the technology
development of the deep learning model.
Note: hormone replacement therapy in the past medical history is not an exclusion criterion.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method <br> Current primary outcome measure as of 25/05/2021:<br> Standalone sensitivity and specificity performance of the AI system.<br><br><br> Previous primary outcome measure:<br> Rate of detection of malignancy of the Sponsor’s deep learning software measured using patient notes.<br>
- Secondary Outcome Measures
Name Time Method