(151 days)
Not Found
Yes
The device description explicitly states that the software is "deep-learning-based" and utilizes a "deep learning algorithm".
No
The device aids in the quantitative characterization of pulmonary nodules and provides information for decision-making, but it explicitly states that it is "not intended for clinical diagnosis of any disease" and "does not replace the role of physician or of other testing in the standard of care." This indicates it is a diagnostic or informational tool, not a therapeutic one.
No
The "Intended Use" states it's for "tracking, assessment, and quantitative characterization of detected pulmonary nodules," and the "Device Description" explicitly states, "The device is not intended for clinical diagnosis of any disease." It assists in measurement and characterization, but not diagnosis itself.
Yes
The device description explicitly states "qCT LN Quant is a software device" and describes its functionality as processing CT scans and providing quantitative characterization and analysis. It interacts with existing hardware (DICOM modality, PACS) but is not described as including any hardware components itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The description clearly states that qCT LN Quant processes CT scans, which are medical images, not biological specimens like blood, urine, or tissue.
- The intended use is for the analysis of imaging data. The device analyzes and quantifies features within lung CT images.
- The device is explicitly stated as "limited to analysis of imaging data" and "not intended for clinical diagnosis of any disease." This further reinforces that it is not performing a diagnostic test on a biological sample.
Therefore, while it is a medical device used in the assessment of potential medical conditions, it falls under the category of medical imaging software rather than an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device. The provided text explicitly indicates "PCCP" as "Not Found."
Intended Use / Indications for Use
qCT LN Quant is a software device used in the tracking, assessment, and quantitative characterization of detected pulmonary nodules. This automatically analyzes user-selected regions within lung CT to provide volumetric, diameter and computer analysis based on morphological characteristics in a single study, or over the time course of several thoracic studies. The system performs the measurements, allowing the preview of lung nodules in 2D and 3D reconstructed views and the respective measurements to be displayed. It is indicated for the evaluation of user detected solid pulmonary nodules.
Product codes
QIH
Device Description
Qure.ai's computed tomography (CT) scan software, the qCT LN Quant, is a deep-learning-based device that can process non-contract CT (NCCT) scans and assists in quantitative characterization of solid lung nodules of size ≥ 6mm on Chest CTs.
qCT LN Quant consists of a cloud module that can interacts with DICOM modality or the user's picture archiving and communication system (PACS) to receive de-identified scans and returns the results to the same destination. In addition, solid nodules are segmented by the user semi-automatically using double seed points on the nodule, followed by interactive fine-tuning of the boundaries. The segmented region is quantitatively characterized by qCT LN Quant and presented to users as an additional overlay by highlighting and labelling respectively. User-assisted segmentation generated by qCT LN Quant can be presented in two ways to the users:
a. PACS-based mode: As a new series (secondary capture) which are returned to the originating PACS system with segmentation burnt on the series. This can be done only at PACS which supports GSPS Output.
b. Web-based mode: On Qure's web application where the segmentation is overlaid on top of the original scan.
qCT LN Quant deep learning algorithm has been trained to quantify the target structures on CT scans and is coupled with pre-and post-processing functionality that allows the device to work directly with the radiology workflow. The user is presented with 2D view and 3D reconstructed view of solid nodule images labelling the quantitative characteristics based on the user-segmented structures. The output consists of information on average diameter and volumes of user identified solid nodules. The additional features include long axis diameter (mm), short axis diameter (mm), Effective diameter (mm), and Mean/Minimum/Max HU (HU) and volume change overtime with matched nodules. In addition, qCT LN Quant consists of a Brock Score - Risk Calculator that uses diameter of the nodule and clinician's input. The Lung-RADS™ calculator feature is based on ACR guideline, which can assist the physician in decision making. qCT LN Quant also provides recommendations based on Fleischner's Society guideline. Thus, qCT LN Quant offers functionality to calculate Brock and LungRADS score as part of integrated or cleared devices with capability to display such output.
qCT LN Quant is limited to analysis of imaging data and results generated are meant for information purposes only. The device is not intended for clinical diagnosis of any disease. It does not replace the role of physician or of other testing in the standard of care for lung abnormalities.
KEY FEATURES
Semi-automatic quantification of solid nodule features
- Average Diameter (mm)
- Volume (mm3)
ADDITIONAL FEATURES
- 3D visualization of segmented nodules
- Manual editing of the solid nodule segmentation contour lines with automatic recalculation of quantitative characteristics post-editing
- Automatic software calculation of the following measurements for each segmented nodule:
- Long Axis- Longest diameter on an axial slice(mm)
- Short Axis- Longest diameter perpendicular to the long axis on the same slice (mm)
- Effective diameter (mm) - a formula-based feature calculated from volume of the nodule
- Measurement of Mean/Minimum/Max HU (HU)
- Volume Doubling Time (VDT) calculated from the measurements between each current scan and the prior scan. This measurement is a formula derived value from existing validated measurements, namely the volume estimate of a region within the current and prior scans. This information is used in a mathematical calculator along with the number of days between the scans to calculate VDT.
- Lung-RADS™ calculation based on information provided by users.
- Brock Score - Risk Calculator tool based on patient and nodule characteristics provided by users. This is used for estimation and alignment of the probability that lung nodules detected on low-dose CT scans are malignant, as reported by McWilliams, Annette, et al. "Probability of cancer in pulmonary nodules detected on first screening CT." New England Journal of Medicine 369.10 (2013): 910-919.
- Reporting based on Fleischner Society guidelines with the aid of user filled patient information.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-Contrast Chest CT scan
Anatomical Site
Lung
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Radiologists and Pulmonologists.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Annotation Process: The dataset used in the study included 216 solid nodules identified from a total of 118 chest CT scans from 104 subjects. Ground Truth was established by three expert radiologists. The truthers independently read the scans and mark out the boundaries of the nodule in all slices
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone Study: Performance of the qCT LN Quant device in quantitative characterization of solid nodules was assessed using the standalone study.
Sample Size: 216 solid nodules identified from a total of 118 chest CT scans from 104 subjects.
Key Results: The device showed good performance and met the predefined success criteria when evaluated against the ground truth (reference standard). The study results also indicated that the outputs of the device are accurate and uniform across a wide range of potential sources of measurement error.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
- Short Axis Diameter Median Absolute Normalized Error %: 14.3 (95% Confidence Interval: 13.95 - 16.67)
- Long Axis Diameter Median Absolute Normalized Error %: 11.1 (95% Confidence Interval: 9.52 - 12.50)
- Volume Median Absolute Normalized Error %: 20.7 (95% Confidence Interval: 17.29 - 22.41)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
0
August 16, 2024
Image /page/0/Picture/1 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Qure.ai Technologies Abishamala Kingsly Senior Regulatory Clinical Affairs Manager Level 7, Commerz II International Business Park Oberoi Garden City, Goregaon (E) MUMBAI, MAHARASHTRA 400 063 INDIA
Re: K240740
Trade/Device Name: qCT LN Quant Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: July 10, 2024 Received: July 10, 2024
Dear Abishamala Kingsly:
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" (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).
1
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 QS 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.
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.
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,
Samal for
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
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Indications for Use
Submission Number (if known)
K240740
Device Name
gCT LN Quant
Indications for Use (Describe)
gCT LN Quant is a software device used in the tracking, assessment, and quantitative characterization of detected pulmonary nodules. This automatically analyzes user-selected regions within lung CT to provide volumetric, diameter and computer analysis based on morphological characteristics in a single study, or over the time course of several thoracic studies. The system performs the measurements, allowing the preview of lung nodules in 2D and 3D reconstructed views and the respective measurements to be displayed. It is indicated for the evaluation of user detected solid pulmonary nodules.
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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3
K240740
510(k) SUMMARY qCT LN Quant
SUBMITTER 1
Qure.ai Technologies Pvt. Ltd. Level 7, Commerz II, International Business Park Oberoi Garden City, Goregaon (E), Mumbai - 400 063 Country: INDIA Phone: +91-9840580965 Primary Contact Person: Abishamala Kingsly, Senior Regulatory Clinical Affairs Manager Secondary contact person: Bunty Kundnani, Chief Regulatory Affairs Officer
Date Prepared: 29 February 2024
SUBJECT DEVICE 2
| Name of Device: | qCT LN Quant |
|---|---|
| Common or Usual Name: | Medical Image Management and Processing System |
| Classification Name: | Automated Radiological Image Processing Software |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2050 |
| Product Code: | QIH |
3 PREDICATE DEVICES
| Name of Device: | Ninesmeasure |
|---|---|
| Manufacturer: | Nines, Inc. |
| 510(k) Number: | K202990 |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2050 |
| Product Code: | LLZ |
| Name of Device: | Lung Nodule Assessment and Comparison Option (LNA) |
|---|---|
| Manufacturer: | Philips Medical Systems Nederland B.V. |
| 510(k) Number: | K162484 |
| Regulatory Class: | Class II |
| Regulation Number: | 21 CFR 892.2050; 21 CFR 892.1750 |
| Product Code | LLZ, JAK |
4
4 INTENDED USE / INDICATIONS FOR USE:
gCT LN Quant is a software device used in the tracking, assessment and quantitative characterization of detected pulmonary nodules. This automatically analyzes user-selected regions within lung CT to provide volumetric, diameter and computer analysis based on morphological characteristics in a single study, or over the time course of several thoracic studies. The system performs the measurements, allowing the preview of lung nodules in 2D and 3D reconstructed views and the respective measurements to be displayed. It is indicated for the evaluation of user detected solid pulmonary nodules.
ഗ DEVICE DESCRIPTION
Qure.ai's computed tomography (CT) scan software, the qCT LN Quant, is a deep-learning-based device that can process non-contract CT (NCCT) scans and assists in quantitative characterization of solid lung nodules of size ≥ 6mm on Chest CTs.
qCT LN Quant consists of a cloud module that can interacts with DICOM modality or the user's picture archiving and communication system (PACS) to receive de-identified scans and returns the results to the same destination. In addition, solid nodules are segmented by the user semi-automatically using double seed points on the nodule, followed by interactive fine-tuning of the boundaries. The segmented region is quantitatively characterized by qCT LN Quant and presented to users as an additional overlay by highlighting and labelling respectively. User-assisted segmentation generated by qCT LN Quant can be presented in two ways to the users:
a. PACS-based mode: As a new series (secondary capture) which are returned to the originating PACS system with segmentation burnt on the series. This can be done only at PACS which supports GSPS Output.
b. Web-based mode: On Qure's web application where the segmentation is overlaid on top of the original scan.
qCT LN Quant deep learning algorithm has been trained to quantify the target structures on CT scans and is coupled with pre-and post-processing functionality that allows the device to work directly with the radiology workflow. The user is presented with 2D view and 3D reconstructed view of solid nodule images labelling the quantitative characteristics based on the user-segmented structures. The output consists of information on average diameter and volumes of user identified solid nodules. The additional features include long axis diameter (mm), short axis diameter (mm), Effective diameter (mm), and Mean/Minimum/Max HU (HU) and volume change overtime with matched nodules. In addition, qCT LN Quant consists of a Brock Score - Risk Calculator that uses diameter of the nodule and clinician's input. The Lung-RADS™ calculator feature is based on ACR guideline, which can assist the physician in decision making. qCT LN Quant also provides recommendations based on Fleischner's Society guideline. Thus, qCT LN Quant offers functionality to calculate Brock and LungRADS score as part of integrated or cleared devices with capability to display such output.
qCT LN Quant is limited to analysis of imaging data and results generated are meant for information purposes only. The device is not intended for clinical diagnosis of any disease. It does not replace the role of physician or of other testing in the standard of care for lung abnormalities.
KEY FEATURES
Semi-automatic quantification of solid nodule features
5
- " Average Diameter (mm)
- . Volume (mm3)
ADDITIONAL FEATURES
- l 3D visualization of segmented nodules
- . Manual editing of the solid nodule segmentation contour lines with automatic recalculation of quantitative characteristics post-editing
- l Automatic software calculation of the following measurements for each segmented nodule:
- Long Axis- Longest diameter on an axial slice(mm) O
- O Short Axis- Longest diameter perpendicular to the long axis on the same slice (mm)
- O Effective diameter (mm) - a formula-based feature calculated from volume of the nodule
- o Measurement of Mean/Minimum/Max HU (HU)
- . Volume Doubling Time (VDT) calculated from the measurements between each current scan and the prior scan. This measurement is a formula derived value from existing validated measurements, namely the volume estimate of a region within the current and prior scans. This information is used in a mathematical calculator along with the number of days between the scans to calculate VDT.
- " Lung-RADS™ calculation based on information provided by users.
- l Brock Score - Risk Calculator tool based on patient and nodule characteristics provided by users. This is used for estimation and alignment of the probability that lung nodules detected on low-dose CT scans are malignant, as reported by McWilliams, Annette, et al. "Probability of cancer in pulmonary nodules detected on first screening CT." New England Journal of Medicine 369.10 (2013): 910-919.
- Reporting based on Fleischner Society guidelines with the aid of user filled patient information.
Additional Information on Brock Score - Risk Calculator:
This is a calculator using Brock University Score as described in McWilliams, et al (2013). This model allows estimating the probability that lung nodules detected on baseline CT scans are malignant. The diameter obtained by the device will be prefilled while the other variables required for the calculator will be user added-. Nodules within each scan will be ordered based on this score.
The model's performance was validated using two large population-based prospective studies: the Pan-Canadian Early Detection of Lung Cancer Study (PanCan) and the chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute.
Further details can be found in McWilliams, A., Tammemagi, M.C., Mayo, J.R., Roberts, H., Liu, G., Soghrati, K., Yasufuku, K., Martel, S., Laberge, F., Gingras, M. and Atkar-Khattra, S. Probability of cancer in pulmonary nodules detected on first screening CT. New England Journal of Medicine. 2013; 369(10), 910-919.
Additional Information on Lung-RADS™ Classification:
This is a classification system proposed to aid with findings in low-dose CT screening exams for lung cancer. The goal of the classification system is to standardize follow-up and management decisions.
Further details can be found in Martin MD, Kanne JP, Broderick LS, Kazerooni EA, Meyer CA. RadioGraphics Update: Lung-RADS 2022. Radiographics. 2023; 43(11): e230037. doi: 10.1148/rg.230037. PMID: 37856315.
6
Additional Information on Fleischner's Guidelines:
Fleischner Society guidelines for management of solid or subsolid nodules was created for the purpose of recommendations to reduce the number of unnecessary follow-up examinations while providing greater discretion to the radiologist, clinician, and patient to make management decisions.
MacMahon, Heber, et al. "Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017." Radiology. 2017; 284(1): 228-243.
WARNINGS AND PRECAUTIONS
- Conditions of image quality that diminish chest radiographic sensitivity, such as noise or artifacts, may also diminish the effectiveness of the device
- . qCT LN Quant is intended to be used by certified medical specialists trained and experienced to review and report chest CT scans.
- . qCT LN Quant is not intended to be used in patients with Idiopathic Pulmonary Fibrosis (IPF) and extensive granulomatous disease.
- . qCT LN Quant is limited to analysis of imaging data quantification and should not be used inlieu of full patient evaluation or relied upon to make or confirm diagnosis.
- . The clinician is responsible for reviewing the device findings with the original image for any treatment /transfer initiation.
- . The device shall not be used for scans outside of the compatibility as defined in Section 1.8. If used may reduce performance of the quantification device.
- When performing follow-up studies, it is important to use the same parameters as the original CT to get a consistent quantitative characteristics comparison between current and previous studies. If the recommended parameters are not used the accuracy of the results cannot be assured.
- . The user has to verify the correctness of the segmentation and labels. If changes are needed, they have to be manually corrected using the correction tools provided by the application.
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6 COMPARISON WITH PREDICATE DEVICE
| Table 1 Comparison between gCT LN Quant and the Predicate Devices |
|---|
| ------------------------------------------------------------------- |
| Primary Predicate Device | Secondary Predicate Device | Subject Device | |
|---|---|---|---|
| Device Name | NinesMeasure | Lung Nodule Assessment and | |
| Comparison Option (LNA) | qCT LN Quant | ||
| 510(k) Number | K202990 | K162484 | K240740 |
| Device Class | Class II | Class II | Class II |
| Device Classification | |||
| Name | System, Image processing, | ||
| Radiological | System, Image processing, Radiological | Automated Radiological Image | |
| Processing Software | |||
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 | |
| 21 CFR 892.1750 | 21 CFR 892.2050 | ||
| Regulation Description | Radiological Image Processing System | Radiological Image Processing Software | Medical Image Management and |
| Processing System | |||
| Product Code | LLZ | LLZ, JAK | QIH |
| Manufacturer | Nines, Inc | Philips Medical Systems Nederland B.V. | Qure.ai Technologies Pvt. Ltd. |
| Intended use / Indications | |||
| for Use | NinesMeasure is a semi-automatic | ||
| tool indicated for use by trained | |||
| radiologists to aid in the analysis and | |||
| review of adult thoracic CT images. | |||
| NinesMeasure provides quantitative | |||
| information about pulmonary nodule | |||
| size on a single study or over the time | |||
| course of several thoracic studies by | |||
| providing long and short axis diameter | |||
| measurements in the axial plane. | |||
| Based on analysis of DICOM images | |||
| and provided input from a radiologist, | |||
| indicating the location of the | |||
| pulmonary nodule, the device uses | |||
| artificial intelligence algorithms to | The Philips Medical Systems Lung Nodule | ||
| Assessment and Comparison Option is | |||
| intended for use as a diagnostic patient- | |||
| imaging tool. | |||
| It is intended for the review and analysis | |||
| of thoracic CT images, providing | |||
| quantitative and characterizing | |||
| information about nodules in the lung in | |||
| a single study, or over the time course | |||
| of several thoracic studies. | |||
| Characterizations include diameter, | |||
| volume and volume over time. The | |||
| system automatically performs the | |||
| measurements, allowing lung nodules | |||
| and measurements to be displayed. | qCT LN Quant is a software device used | ||
| in the tracking, assessment, and | |||
| quantitative characterization of detected | |||
| pulmonary nodules. This automatically | |||
| analyzes user-selected regions within | |||
| lung CT to provide volumetric, diameter | |||
| and computer analysis based on | |||
| morphological characteristics in a single | |||
| study, or over the time course of several | |||
| thoracic studies. The system performs | |||
| the measurements, allowing the preview | |||
| of lung nodules in 2D and 3D | |||
| reconstructed views and the respective | |||
| measurements to be displayed. It is | |||
| Primary Predicate Device | Secondary Predicate Device | Subject Device | |
| Device Name | NinesMeasure | Lung Nodule Assessment and | |
| Comparison Option (LNA) | qCT LN Quant | ||
| automatically perform the | |||
| measurements, and allows the axial | |||
| measurements to be displayed and | |||
| reviewed. NinesMeasure is limited for | |||
| use on solid pulmonary nodules. The | |||
| device is intended to be used as a | |||
| measurement tool by a trained | |||
| radiologist and is limited to analysis of | |||
| imaging data and should not be used | |||
| in-lieu of full patient evaluation or | |||
| relied upon to make or confirm a | |||
| diagnosis. The device does not alter | |||
| the original medical image. | indicated for the evaluation of user | ||
| detected solid pulmonary nodules. | |||
| Intended User | Radiologists | Radiologists and Technologist | Radiologists and Pulmonologists. |
| Modality | Thoracic CT scan | Thoracic CT scan | Non-Contrast Chest CT scan |
| Target clinical conditions | Lung Nodule | Lung Nodule | Lung Nodule |
| Algorithm for pre- | |||
| specified critical findings | |||
| detection | Image processing algorithms for | ||
| nodule measurement | Image processing algorithms for lung | ||
| nodule measurements | Image processing algorithms for lung | ||
| nodule measurements | |||
| Input format | DICOM | DICOM | DICOM |
| Performance level - | |||
| accuracy of classification | Normalized error on long axis | ||
| diameter – [95% Cl] 0.113 [Upper | |||
| Bound 0.124]. Normalized error on | |||
| short axis diameter - [95% Cl] 0.131 | |||
| [Upper Bound 0.143] | Not Available | Median Absolute Normalized Average | |
| Diameter Error [95% CI]: 11.1 (9.1-11.1) | |||
| Median Absolute Normalized Volume | |||
| Error [95% Cl]: 20.7 (17.6-22.6) |
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9
7 TESTING
Software:
Software verification and validation testing was executed, and documentation was provided as recommended by FDA's Guidance for the Content of Premarket Submission for Device Software Functions (June 2023). During all verification and validation tests carried out for the qCT LN Quant software, which included evaluating both the algorithmic functionality and the overall performance of the software and its components, qCT LN Quant functioned as designed and successfully met the anticipated performance criteria.
Verification, validation, and testing activities were conducted to establish the performance, functionality, and reliability characteristics of the device. Unit Test, Integration Test, Regression Test and User Acceptance test were carried out to account towards the device's performance nonclinically. Functional testing is done to assess functional requirements of the device passed all the tests based on determined acceptance criteria. Standards Regulatory references Used are ISO 13485: 2016 and IEC 62304:2006+A1:2015.
Clinical Performance Testing:
Annotation Process: The dataset used in the study included 216 solid nodules identified from a total of 118 chest CT scans from 104 subjects. Ground Truth was established by three expert radiologists. The truthers independently read the scans and mark out the boundaries of the nodule in all slices -
Testing Summary: Performance of the qCT LN Quant device in quantitative characterization of solid nodules was assessed using the standalone study. The device showed good performance and met the predefined success criteria when evaluated against the ground truth (reference standard). The study results also indicated that the outputs of the device are accurate and uniform across a wide range of potential sources of measurement error.
| Measurements | Median Absolute
Normalized Error % | 95% Confidence
Interval |
|---------------------|---------------------------------------|----------------------------|
| Short Axis Diameter | 14.3 | 13.95 - 16.67 |
| Long Axis Diameter | 11.1 | 9.52 - 12.50 |
| Volume | 20.7 | 17.29 - 22.41 |
Table 2 Standalone Performance Testing Results for qCT LN Quant
8 CONCLUSION
The comparison in Table 1 as well as the software & performance testing presented above demonstrate that the qCT LN-Quant device is substantially equivalent to the predicate devices. Both the subject and predicate devices are medical image analyzers intended to read chest CT scans to quantitatively characterize user identified lung nodules. The algorithms function similarly and with the same purpose of quantitative characterization of lung nodules. The new device does not introduce
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fundamentally new scientific technology, and the clinical tests demonstrate that the device is safe and effective as predicate.
The qCT LN Quant is a software only device with similar indications, technological characteristics, and principles of operation as the predicate devices. The comparison of intended purpose, technological characteristics and performance demonstrates that the qCT LN-Quant device performs as intended and can be considered as substantially equivalent to the predicate devices, Lung Nodule Assessment and Comparison Option (LNA) (K162484) and NinesMeasure (K202990).