Jun 16 2022
The Food and Drug Administration (FDA) is issuing this guidance to provide recommendations for manufacturers about the information that should be included in premarket submissions for radiological devices that include quantitative imaging functions.
This guidance clarifies that, in general, manufacturers preparing premarket submissions for radiological devices that include quantitative imaging functions should provide performance specifications for the quantitative imaging functions, supporting performance data to demonstrate that the quantitative imaging functions meet those performance specifications, and sufficient information for the end user to obtain, understand, and interpret the values provided by the quantitative imaging functions.
FDA recommends that the premarket submission for your device that incorporates quantitative imaging function(s) include the information described below.
Your premarket submission should include a technical description of the quantitative imaging function(s) included in your device at a level of detail sufficient for the Agency to understand the functionality. In some instances, a more general description of the measurement process may be sufficient; however, you should provide a more detailed description of the processes for more complex quantitative imaging functions, to ensure FDA’s understanding of your device. Details on the software implementation for your algorithm should be included as part of the software documentation following the Agency’s recommendations found in “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.”
FDA recommends including the following information when describing your quantitative imaging function(s):
*** A description of the quantitative imaging function, such as:
Description of the measurand (including units);
Name, version, and relevant characteristics of the software platform;
A detailed description of the algorithm employed, including algorithm inputs and outputs;
For algorithms derived from physical processes (e.g., fluence correction, tomographic image reconstruction), the assumed underlying physics and its relationship to the mathematical components of the algorithm;
Level of automation (e.g., manual, automatic, or semi-automatic); and
If applicable, a brief summary of your algorithm training paradigm, including how algorithm parameters and thresholds were established.
- Information about input data (e.g., images):
Target population, including intended patient populations, organs, and diseases/conditions/abnormalities;
Restrictions on input data, such as imaging modalities, as applicable, (e.g., computed tomography, magnetic resonance), make, model, and specific trade name for each modality/system, specific ancillary hardware/software necessary to produce the input data (e.g., magnetic resonance elastography (MRE) acoustic driver), specific image acquisition parameter ranges (e.g., kVp range, slice thickness, voxel size) or specific imaging protocol(s) (e.g., pre-exam diet, breath hold, magnetic resonance angiography (MRA) sequence); or
Specific limitations, including diseases/conditions/abnormalities or imaging conditions, for which your quantitative imaging function has been found ineffective and should not be used, as applicable.
- Image acceptance activities (e.g., how your device ensures that input data (e.g., images)/preprocessing are acceptable for processing with your algorithm) and whether these are performed manually by a trained user or automatically by your algorithm;
- Information presented to the user about the derived values (including units); and
- The level of user interaction needed for your device to be used as intended, and if applicable, instructions for users (preprocessing image steps, selecting seed points, applying algorithm, and verifying resulting measurement for a lesion sizing tool).
Technical Performance Assessment
FDA recommend that the technical performance assessment of a quantitative imaging function of your device include the following steps:
1. Define the quantitative imaging function, its relationship to the measurand, and the use conditions. For example, if the input to your algorithm is required to have a pixel size of < 1 mm, you would not be expected to evaluate the performance of your algorithm for pixels > 1 mm.
2. Determine an appropriate reference standard and the performance metrics applicable to your device. Bias, precision, limits of detection, limits of quantitation, linearity, sensitivity, specificity, and uncertainty should generally be considered as applicable.
3. Characterize the performance of the quantitative imaging function under the conditions defined in the device labeling.
4. Define the experimental unit (e.g., per lesion, per patient).
5. Define the appropriate statistical estimates of performance (e.g., limits of agreement, total deviation index).
6. Define acceptance criteria (performance targets or goals) based on the intended use of the quantitative imaging function and other restrictions/limitations (such as minimum image quality requirements).
7. Specify the elements of the statistical design, the necessary data (e.g., patient population, type of images), and the statistical analysis plan, based on the selected performance metrics. This statistical analysis should be appropriate for the task at hand.
8. Collect the relevant data.
9. Perform the statistical analysis.
10. Compare the analysis results to the pre-defined acceptance criteria
Your premarket submission must include labeling in sufficient detail to satisfy any applicable requirements for your type of premarket submission (e.g., 21 CFR 807.87(e) or 21 CFR 814.20(b)(10)). In addition, device labeling must satisfy all applicable FDA labeling requirements, including 21 CFR part 801.
Your device labeling should include sufficient information for the end user to obtain, understand, and interpret the values provided by the quantitative imaging function.
Generally, this information should include :
a) A description of the measurand and the units in which it is measured.
b) A description of the algorithm inputs, including any restrictions on input data (e.g., images).
c) Performance specifications, including uncertainty information, that cover the entire operating range of the quantitative imaging function. The performance specification or claims in the labeling should correspond to device design requirements or specifications.
Uncertainty information should facilitate interpretation of results and should be provided in units of the measurand whenever possible. On-screen display of uncertainty information is preferred whenever possible.
For quantitative imaging functions for which specific performance metrics for uncertainty cannot be provided, the premarket submission should include information on the primary sources of variability affecting the quantitative imaging output (e.g., pixel size, image signalto-noise-ratio (SNR), patient anatomy).
d) Instructions for image acceptance or quality assurance activities to be performed by the user. If the performance for the quantitative imaging function is dependent on quality assurance by the user (e.g., ensuring that SNR is acceptable, slice thickness is within a given range, that the image is free of artifacts), the device labeling should include quality assurance protocols (e.g., what characteristics to test for, how to execute test methods and calculate metrics), as well as clear instructions on actions to be taken when quality assurance fails. A detailed description of all necessary phantoms and/or instructions on how to obtain phantoms should be included.
e) A description of the qualifications and training needed for a user to be within the device’s intended user population.
f) Quantitative imaging functions that provide a comparison to a reference database should include information about the composition of the reference database (e.g., subject demographics, number of subjects within each stratum or bin of the database). If the database is well known and publicly available, we recommend you include a reference or a hyperlink to the publicly available reference in your labeling. For in-house developed reference databases, information on subject composition (e.g., number of subjects, subject demographics (e.g., sex, race, age), disease conditions) should be provided.
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