Breast cancer Cancer
Conditions
Interventions
The intervention is the sponsor's deep learning software, assessed on de-identified randomised retrospective breast screening cases and outcomes. Comparison is made against the control arm of existing
Sponsors
Kheiron Medical Technologies
Eligibility
Sex/Gender
Female
Inclusion criteria
Inclusion criteria: 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)
Exclusion criteria
Exclusion criteria: 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Current primary outcome measure as of 25/05/2021: Standalone sensitivity and specificity performance of the AI system. Previous primary outcome measure: Rate of detection of malignancy of the Sponsor’s deep learning software measured using patient notes. | — |
Secondary
| Measure | Time frame |
|---|---|
| Current secondary outcome measures as of 25/05/2021: 1. Non-inferiority and superiority testing of the AI system as an independent reader in a double reading workflow compared to national guidelines and historical double reading in terms of recall rate, cancer detection rate, sensitivity, specificity, and interval cancer rate. 2. Comparing the AI system’s standalone performance to the historical first reader. 3. The AI system’s standalone performance in terms of recall rate, cancer detection rate, interval cancer rate, positive predictive value, arbitration rate, and AUC. Previous secondary outcome measures: Secondary aims will assess the software's performance (sensitivity, specificity, percent indeterminate) at varying settings as well as measure its recall rate (percentage of screening mammograms recalled for further assessment). Accuracy is measured in terms of sensitivity (true positive rate), specificity (true negative rate) as compared to a defined Reference Standard, plotted onto Receiver Operator Curves, and an Area Under the Curve (AUC) calculated. Recall rate is the rate of positive reported findings in a given sample. | — |
Countries
England, Hungary, United Kingdom
Outcome results
None listed