(193 days)
Not Found
Yes
The device description explicitly states that QP-Prostate® CAD is "an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software" and "uses Al-based algorithms trained with pathology data". It also mentions that it is based on "Neural Networks and Machine Learning".
No.
This device is a diagnostic aid, providing assistance in interpreting medical images for disease detection, rather than directly treating or preventing a medical condition.
Yes
The device is explicitly described as "Computed Aided Detection and Diagnosis (CADe/CADx) image processing software" and states its intention to "detect and identify suspected lesions" and serve "as an aid for interpreting prostate MRI studies." These functions are diagnostic in nature.
Yes
The device is described as "image processing software" and "artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software." It takes existing MRI images as input and provides output in standard DICOM formats for display on third-party workstations and PACS. There is no mention of any accompanying hardware component that is part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices intended for use in vitro for the examination of specimens, including blood and tissue donations, derived from the human body, solely or principally for the purpose of providing information concerning a physiological or pathological state, or concerning a congenital abnormality, or to determine the safety and compatibility with potential recipients, or to monitor therapeutic measures.
- Device Function: QP-Prostate® CAD is an image processing software that analyzes medical images (MRI) of the prostate gland. It does not examine specimens derived from the human body in vitro.
- Intended Use: The intended use is to aid physicians in interpreting prostate MRI studies by detecting and identifying suspected lesions. This is an in vivo diagnostic aid, not an in vitro one.
The device falls under the category of medical image analysis software or Computer-Aided Detection/Diagnosis (CADe/CADx), which are regulated as medical devices but are distinct from IVDs.
No
The provided text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
QP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.
Product codes (comma separated list FDA assigned to the subject device)
QDQ
Device Description
QP-Prostate® CAD is an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software. QP-Prostate® CAD uses Al-based algorithms trained with pathology data to detect suspicious lesions for clinically significant prostate cancer. The device automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI and provides marks over regions of the images that may contain suspected lesions. There are two possible markers that are provided in different colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high suspicion level).
The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Based on biparametric input consisting of T2W and DWI series, the analysis is run automatically, and the output in standard DICOM formats is returned to PACS.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
bi-parametric prostate MRI (T2W and DWI series)
Anatomical Site
prostate gland
Indicated Patient Age Range
Patients above 40 years
Intended User / Care Setting
Intended users: QP-Prostate® CAD is intended to be used by physicians with proper training, including radiologists, urologists and any physician qualified to read and interpret prostate MRI consistent with ACR recommendations in the context of PI-RADS.
Care setting: clinical setting
Description of the training set, sample size, data source, and annotation protocol
The training dataset included cases acquired in the US from multiple centers, with multiple magnetic fields (1.5T, 3T) and vendors (Siemens, GE Medical Systems, Philips Medical Systems). T2w and DWI sequences of these cases were used for training. Annotation protocol: QP-Prostate® CAD uses Al-based algorithms trained with biopsy outcomes as ground truth to detect suspicious lesions for csPCa (Gleason score ≥7). The dataset contained representation of different Gleason score subgroups, was ethnically diverse, and the age ranged from 43 to 86 years. The fine-tuning of the model aimed to optimize the sensitivity and specificity at a case level for the high and moderate markers.
Description of the test set, sample size, data source, and annotation protocol
Sample size: 228 cases
Data source: collected retrospectively from multiple centers across the US. This dataset was completely independent from the training dataset that was acquired from different institutions.
Annotation protocol: The pivotal dataset included both positive and negative cases in the same proportion. Positive cases were biopsy confirmed (Gleason score ≥ 7); negative cases were either biopsy-confirmed (Gleason score
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
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March 18, 2025
Quibim S.L. % John J. Smith Partner Hogan Lovells US LLP 555 13th St NW Washington, District of Columbia 20004
Re: K242683
Trade/Device Name: QP-Prostate® CAD Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: ODO Dated: February 14, 2025 Received: February 14, 2025
Dear John J. Smith:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
1
(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rue"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
2
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
D.R.K.
Daniel M. Krainak, Ph.D. Assistant Director DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K242683
Device Name QP-Prostate® CAD
Indications for Use (Describe)
OP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.
Intended patient population: Patients above 40 years with prostate MR imaging.
Intended users:
QP-Prostate® CAD is intended to be used by physicians with proper training, including radiologists, urologists and any physician qualified to read and interpret prostate MRI consistent with ACR recommendations in the context of PI-RADS.
Type of Use (Select one or both, as applicable)
| Prescription Use (Part 21 CFR 801 Subpart D) |
|---|
| Over-The-Counter Use (21 CFR 801 Subpart C) |
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510(k) SUMMARY Quibim's QP-Prostate® CAD (K242683)
Submitter Quibim S.L. Avenida Aragon 30, 13th floor, Office I-J, 46021 Valencia (Spain)
Phone:
-
. +34 961 243 255
Contact Person: -
Ángel Alberich Bayarri, CEO and Founder of Quibim .
-
Josep Hortigüela Zamora, VP of Quality Assurance and Regulatory Affairs .
Date Prepared: March 12, 2025
Name of Device: QP-Prostate® CAD
Common or Usual Name: CAD software for lesions suspicious for clinically significant prostate cancer
Classification Name: 892.2090 radiological computer assisted detection/diagnosis software for lesions suspicious for cancer Requlatory Class: Class II
Product Code: QDQ
Predicate Device
- . Applicant: ScanMed, LLC
- Device's trade name: ProstatIDTM ●
- . 510(k) number: K212783
- Product code: QDQ .
Device Description
QP-Prostate® CAD is an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software. QP-Prostate® CAD uses Al-based algorithms trained with pathology data to detect suspicious lesions for clinically significant prostate cancer. The device automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI and provides marks over regions of the images that may contain suspected lesions. There are two possible markers that are provided in different colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high suspicion level).
The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Based on biparametric input consisting of T2W and DWI series, the analysis is run automatically, and the output in standard DICOM formats is returned to PACS.
5
Intended Use / Indications for Use
QP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.
Intended patient population:
Patients above 40 years with prostate MR imaging.
Intended users:
QP-Prostate® CAD is intended to be used by physicians with proper training, including radiologists, urologists and any physician qualified to read and interpret prostate MRI consistent with ACR recommendations in the context of PI-RADS.
Comparison to predicate device
QP-Prostate® CAD and the predicate device ProstatID™ have very similar indications statements. Both are medical image processing applications intended for concurrent reading for physicians interpreting prostate MRI identifying suspected lesions and assessing likelihood for prostate cancer. Both devices generate a 3D rendition. ProstatID™ provides the regional overlay scores on a continuous scale ranging from 0-1, presented as a colorized translucent overlay, and a suggested level of suspicion or overall PI-RADS score, whereas QP-Prostate® CAD provides overlay markings around the detected suspicious lesions, in two possible colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high).
Summary of Technological Characteristics
At a high level, the subject and predicate devices are based on the following same technological elements:
- Both devices are compatible with DICOM standard.
- Both devices use the same input sequences to run the analysis: T2-weighted and DWI acquired with specified protocols.
The following technological differences exist between the subject and predicate devices:
- · Both devices are artificial intelligence-based, but they differ in algorithm methodology (e.g. ProstatID™ is based on random forest and QP-Prostate® CAD is based on Neural Networks and Machine Learning). This difference could affect its safety or effectiveness but does not raise any different questions of safety or effectiveness, because a set of verification and
6
validation tests (performance testing) demonstrate the safety and effectiveness of QP-Prostate® CAD.
A table comparing the key features of the subject and predicate devices is provided below:
| Feature | Proposed device:
QP-Prostate® CAD | Predicate device:
ProstatID™ (K212783) | Comments |
|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| REGULATORY DATA | | | |
| Class | II | II | N/A |
| Regulation name | Radiological Computer
Assisted
Detection/Diagnosis
Software | Radiological Computer
Assisted
Detection/Diagnosis
Software | N/A |
| Regulation number | 21 CFR 892.2090 | 21 CFR 892.2090 | N/A |
| Classification Panel | Radiology | Radiology | N/A |
| Product Code | QDQ | QDQ | N/A |
| Applicant | Quibim S.L. | ScanMed, LLC. | N/A |
| | | Indications for Use | |
| Medical device
description | QP-Prostate® CAD is a
Computed Aided
Detection and
Diagnosis
(CADe/CADx) image
processing software
that automatically
detects and identifies
suspected lesions in the
prostate gland based
on bi-parametric
prostate MRI. The
software is intended to
be used as a
concurrent read by
clinicians with proper
training in a clinical
setting as an aid for
interpreting prostate
MRI studies. The
results can be
displayed in a variety of
DICOM outputs,
including identified
suspected regions of
abnormalities marked
as an overlay onto
source MR images. The
output can be displayed
on third-party DICOM
workstations and
Picture Archive and
Communication
Systems (PACS).
Patient management | ProstatIDTM is a
radiological computer
assisted detection
(CADe) and diagnostic
(CADx) software device
for use in a healthcare
facility or hospital to
assist trained
radiologists in the
detection, assessment,
and characterization of
prostate abnormalities,
including cancer lesions
using MR image data
with the following
indications for use.
ProstatID analyzes T2W,
DWI and ADC MRI data.
ProstatID does not
include DCE images in
its analysis.
ProstatID software is
intended for use as a
concurrent reading aid
for physicians
interpreting
prostate MRI exams of
patients presented for
high-risk screening or
diagnostic imaging, from
compatible MRI
systems, to identify
regions suspicious for
prostate cancer and
assess their likelihood of
malignancy.
Outputs of the device
include the volume of the
prostate and locations,
as well as the extent of
suspect lesions, with | Substantially
equivalent. Minor
differences in outputs,
exist, but both devices
are CADe/x devices
that characterize
prostate abnormalities
on MR image data.
The differences do not
raise different
questions of safety
and effectiveness. |
| Feature | Proposed device: | Predicate device: | Comments |
| | QP-Prostate® CAD | ProstatID™ (K212783) | |
| | decisions should not be
based solely on the
results of QP-Prostate®
CAD. | index scores indicating
the likelihood that cancer
is present, as well as an
exam score by way of
PI-RADS interpretation
suggestion. "Extent of
suspect lesions" refers to
both the assessment of
the boundary of a
particular abnormality,
as well as identification
of multiple abnormalities.
In cases where multiple
abnormalities are
present, ProstatID can
be used to assess each
abnormality
independently.
Outputs of this device
should be interpreted
with all available MR
data consistent with
ACR clinical
recommendations
(e.g., dynamic contract
enhanced images if
available) in context of
PI-RADS v2, and in
conjunction with bi-
parametric
MRI acquired with either
surface or endorectal
MRI accessory coils
from compatible MRI
systems. Analysis by
ProstatID
is not intended as a
replacement for
interpreting prostate
abnormalities using MR
image data consistent
with clinical
recommendations
(including DCE); nor
should patient
management decisions
be made solely on the
basis of ProstatID. | |
| Intended user
population | QP-Prostate® CAD is
intended to be used by
physicians with proper
training, including
radiologists, urologists
and any physician
qualified to read and
interpret prostate MRI
consistent with ACR
recommendations in the
context of PI-RADS. | Intended users of
ProstatID are physicians
qualified to read and
interpret prostate MRI
exams consistent with
ACR recommendations
in the context of PI-
RADS v2. | Substantially
equivalent. |
| Feature | Proposed device:
QP-Prostate® CAD | Predicate device:
ProstatID™ (K212783) | Comments |
| Intended patient
population | Patients above 40
years with prostate MR
imaging. | The device is intended to
be used in the
population of biological
adult males with a
prostate gland
undergoing screening or
clinical MRI exams. This
includes biological males
with clinical indicators
suggestive of possible
prostate cancer or
with family history of
prostate cancer. | Substantially
equivalent. |
| CHARACTERISTICS | | | |
| Clinical finding | Identification of location
of suspicious lesions,
with two suspicion
levels of clinically
significant prostate
cancer, moderate or
high | Extent and location of
identified suspected
lesions, assigned to a
level of suspicion (0-1).
A suggested level of
suspicion or overall PI-
RADS exam score. | Substantially
equivalent. While
minor differences exist
in that QP-Prostate
provides two suspicion
levels, while ProstatID
assigns a level of
suspicion on a scale of
0-1, the intended use
of the devices remain
the same.
Furthermore, the
safety and
effectiveness of QP-
Prostate are supported
through performance
assessments and do
not raise different
questions of safety
and effectiveness. |
| DICOM compatibility
Input for analysis | DICOM-in DICOM-out
T2W and DWI series,
DCE is not included for
analysis | DICOM-in DICOM-out
T2W, DWI and ADC MRI
data, DCE is not
included for analysis | Same
Substantially similar.
While QP-Prostate
takes a subset of the
input data used in the
predicate device. This
difference does not
raise different
questions of safety
and effectiveness. |
| Algorithm methodology | Neural Networks and
Machine Learning | Random Forest | AI-based, safety and
effectiveness are
supported
through performance
assessments. |
| Safety | The software is
intended to be used as
a concurrent read by
clinicians with proper
training in a clinical
setting as an aid for
interpreting prostate
MRI studies. Patient
management decisions
should not be based
solely on the results of
QP-Prostate® CAD. | ProstatID software is
intended for use as a
concurrent reading aid.
Analysis by ProstatID is
not intended as a
replacement for
interpreting prostate
abnormalities using MR
image data consistent
with clinical
recommendations
(including DCE); nor | Same |
| Feature | Proposed device:
QP-Prostate® CAD | Predicate device:
ProstatID™ (K212783) | Comments |
| | | should patient
management decisions
be made solely on the
basis of ProstatID. | |
Table 1: Comparison of key features of QP-Prostate® CAD (Quibim) and the predicate device ProstatID™ (Bot Image, Inc.)
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Model development
QP-Prostate® CAD uses Al-based algorithms trained with biopsy outcomes as ground truth to detect suspicious lesions for csPCa (Gleason score ≥7). The training dataset included cases acquired in the US from multiple centers, with multiple magnetic fields (1.5T, 3T) and vendors (Siemens, GE Medical Systems, Philips Medical Systems). T2w and DWI sequences of these cases were used for training. The dataset contained representation of different Gleason score subgroups, was ethnically diverse, and the age ranged from 43 to 86 years. The fine-tuning of the model aimed to optimize the sensitivity and specificity at a case level for the high and moderate markers. To ensure generalizability and performance of QP-Prostate® CAD, standalone and MRMC performance testing studies have been performed with a dataset sourced from different institutions or sources in the US not part of the model training or development datasets. The description of this performance testing dataset and the results of the standalone and MRMC performance testing studies are presented below.
Performance Data
QP-Prostate® CAD software was developed according to FDA recognized consensus standards for software development. Software verification and validation was performed following V&V plans and protocols verifying that product specifications were met.
Dataset selection
The pivotal dataset included 228 cases collected retrospectively from multiple centers across the US. This dataset was completely independent from the training dataset that was acquired from different institutions. The pivotal dataset included both positive and negative cases in the same proportion. Positive cases were biopsy confirmed (Gleason score ≥ 7); negative cases were either biopsy-confirmed (Gleason score 65), BMI (