(114 days)
QuantX is a quantitative image analysis software device used to assist radiologists in the assessment and characterization of breast abnormalities using MR image data. The software automatically registers images, and segments and analyzes user-selected regions of interest (ROI). QuantX extracts image data from the ROI to provide volumetric and surface area analysis. These imaging features are then displayed to the user in a dedicated analysis panel on the display monitor.
When interpreted by a skilled physician, this device provides information that may be useful for screening and diagnosis. Patient management decisions should not be made based solely on the results of QuantX analysis.
QuantX may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software also includes tools that allow users to measure and document images, and output in a structured report.
Limitations: QuantX is not intended for primary interpretation of digital mammography images.
QuantX is a software program that analyzes patient breast images, and is designed to aid radiologists in the characterization of lesions. After MR images are acquired from a third-party acquisition device, they can be loaded into the QuantX database manually, or automatically if connected to a DICOM-compatible device. Users then select and load the patient case to use the QuantX software tools in the examination of the images. Different types of MR sequences (T1, DCE, T2, DWI, etc.) can be viewed at the same time as mammography or ultrasound images from the same patient.
A variety of viewing tools are available to users. The MR images can be examined under different image planes (axial, sagittal, and coronal) as well as different image time points and slices. Users can use keyboard shortcuts or a scrolling mechanism to navigate through MR image slices. Colored axes serve as slice location markers for ease of pinpointing regions of interest (ROI). Images can be panned, changed in contrast, zoomed in or out. and measured. The Colormap feature visualizes contrast uptake (enhancement) studies, and a time intensity curve can be viewed for any location on the MR image.
QuantX includes image registration, and automated segmentation and analysis functions, based on a seed point indicated by the user. Users can select a ROI manually from the MR image, or use the automatic segmentation tool to obtain and accept a ROI, for input to the QuantX analytics.
The QuantX analytics display the QI Most Enhancing Curve, volume and surface area of the specified region. A user experienced with the significance of such data will be able to view and interpret this additional information during the diagnosis of breast lesions.
The provided text describes the QuantX device and its FDA 510(k) summary. However, it does not contain the specific details required to fully address all sections of your request regarding acceptance criteria and the study that proves the device meets those criteria.
Here's what can be extracted and what information is missing:
1. A table of acceptance criteria and the reported device performance
The document mentions "Verification testing of lesion segmentation on MR image data" and "Verification that all measurements and BIRADS reporting were recorded correctly," which implies that there were acceptance criteria for these functions. However, the specific numerical acceptance criteria (e.g., minimum accuracy, sensitivity, specificity, or error bounds) and the reported device performance against these criteria are not provided.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not available in the provided document. The summary only states that nonclinical tests were performed, including "Verification testing of lesion segmentation on MR image data." It does not specify the number of cases or the nature of the data used for these verification tests.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not available in the provided document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not available in the provided document.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
The document states, "OuantX is a quantitative image analysis software device used to assist radiologists... When interpreted by a skilled physician, this device provides information that may be useful for screening and diagnosis." This implies an assistive role for radiologists. However, there is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study or any data on how much human readers improve with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document does not explicitly describe a standalone performance study. While it mentions "automated segmentation and analysis functions," the overall context emphasizes its role in assisting radiologists. Therefore, standalone performance details are not provided.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document mentions "Verification testing of lesion segmentation on MR image data" and "Verification that all measurements... were recorded correctly." This implies that there were reference standards for these tests, likely based on expert annotations or ground truth derived from clinical knowledge. However, the specific type of ground truth (e.g., expert consensus, pathology, long-term outcomes) is not explicitly stated.
8. The sample size for the training set
This information is not available in the provided document. The document describes the device's functions but does not delve into the details of its development or training.
9. How the ground truth for the training set was established
This information is not available in the provided document.
Summary of available and missing information:
The provided 510(k) summary focuses on establishing substantial equivalence based on technological characteristics and general non-clinical testing. It highlights the device's intended use to assist radiologists and its features like image registration, segmentation, and volumetric/surface area analysis. However, it lacks the detailed performance metrics, study designs (e.g., sample sizes, data provenance, ground truth establishment, expert qualifications, adjudication methods), and comparative effectiveness data that would be found in a comprehensive clinical validation study report. These types of details are often found in the full 510(k) submission, but not typically in the public summary document.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).