Radiology

SEZ · Radiological Software Device To Predict Future Breast Cancer Risk

Radiology · 21 CFR 892.8500 · Class 2

Overview

Identification

The Clairity Allix5 is a prescription software device intended to generate a 5-year risk prediction of breast cancer based on a bilateral screening mammogram. It provides a prediction of the percentage probability that an individual will receive a diagnosis of breast cancer or develop breast cancer within 5 years following the screening mammogram, through analysis of mammography features and characteristics. It is intended for patients without known breast cancer at presentation. It is not intended to diagnose or detect breast cancer, provide care recommendations, or serve as the sole determinant for clinical decision-making.

Classification Rationale

Class II (special controls). FDA determined that the device can be classified in class II with the establishment of special controls, which, in combination with the general controls of the FD&C Act, provide reasonable assurance of the safety and effectiveness of the device type.

Special Controls

In combination with the general controls of the FD&C Act, the radiological software device to predict future breast cancer risk is subject to the following special controls: (1) Data obtained from clinical performance validation testing acquired under anticipated conditions of use must demonstrate that the device performs as intended when used in the intended patient population. Documentation must include the following: (i) A description of prespecified performance testing protocols (including the study objectives, study endpoints, statistical hypotheses, performance goals, sample size calculation, and statistical analyses, including adjustment for left- and/or right-censoring of the time of disease onset). Performance goals used to determine success of the clinical validation study must be clinically justified; (ii) A description of the dataset(s) used. Validation must use a clinical test dataset acquired from a representative patient population. The test dataset must be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment. The test dataset must be independent of the data used in the training/development of the device; (iii) Establishment of a reference standard, with clinical justification, to distinguish those who experience the disease by the designated future time from those who do not, or a time interval within which the disease onset is known to have occurred or not occurred. Study protocols must include a description of the methodology for determining the reference standard for training and test datasets; (iv) Results to validate the device output pertaining to future disease risk prediction. The performance assessment must be based on clinically justified measures of discrimination and calibration of risk categorization or score estimates in the intended patient population. Agreement between device predicted risk outputs and observed risk must be calculated across the range of risks expected in the intended patient population; (v) The clinical performance of discrimination and calibration of device outputs must demonstrate the generalizability of device performance across clinically important subgroups. Subgroup analysis of discrimination and calibration by study site, relevant demographic subgroups, image acquisition system, and any other applicable confounders of clinical interest must be provided; and (vi) Data must demonstrate reproducibility of the device output across the range of input image acquisition settings including acquisition equipment and patient positioning. (2) Software verification, validation, and hazard analysis must be provided. Software documentation must include: (i) A technical description of the model/algorithm(s) and algorithm inputs and outputs; and (ii) Verification and validation data that demonstrate software ensures input radiological images are adequate for processing. (3) Labeling must include: (i) Compatible imaging requirements for input; (ii) A warning that the output of this device is not intended to guide interpretation of imaging exams; (iii) A warning that the user of this device should consider other clinical information for patient management; (iv) A warning that the device is not intended to diagnose, detect, or inform the treatment of disease; (v) A summary of the clinical performance testing methods, including results of the performance testing for tested performance measures/metrics, selection criteria, truthing, patient dataset characteristics, and subgroup analyses by relevant confounders; (vi) A description of output reproducibility and results of reproducibility testing; (vii) Device limitations or a description of subpopulations for which the device may not perform as expected or for whom the device has not been validated; and (viii) A summary of the device’s current performance that incorporates clinical performance testing and data collected from post-market performance monitoring. (4) The device manufacturer must develop and implement a post-market performance management plan that ensures regular assessment of the generalizability and device performance in the intended patient population in real-world use. The plan must include: (i) Data collection, analysis methods, and procedures for: (A) Monitoring relevant performance characteristics and detecting changes in performance; (B) Identifying sources of performance changes between validation and real-world environment over time; and (C) Assessing the results from the performance testing on safety and effectiveness; (ii) Procedures for communicating the device’s current performance to the users.

Cleared Devices (1)

Top Applicants

• Clairity, Inc. — 1 clearance