Galen™ Second Read™ is a software only device intended to analyze scanned histopathology whole slide images (WSIs) from prostate core needle biopsies (PCNB) prepared from hematoxylin & eosin (H&E) stained formalin-fixed paraffin embedded (FFPE) tissue. The device is intentify cases initially diagnosed as benign for further review by a pathologist. If Galen™ Second Read™ detects tissue morphology suspicious for prostate adenocarcinoma (AdC), it provides case- and slide-level alerts (flags) which includes a heatmap of tissue areas in the WSI that is likely to contain cancer.

Galen™ Second Read™ is intended to be used with slide images digitized with Philips Ultra Fast Scanner and visualized using the Galen™ Second Read™ user interface.

Galen™ Second Read™ outputs are not intended to be used on a standalone basis for diagnosis, to rule out prostatic AdC or to preclude pathological assessment of WSIs according to the standard of care.

Device Description

The Galen Second Read is an in vitro diagnostic medical device software, derived from a deterministic deep convolutional network that has been developed with digitized WSIs of H&E-stained prostate core needle biopsy (PCNB) slides originating from formalin-fixed paraffinembedded (FFPE) tissue sections, that were initially diagnosed as benign by the pathologist.

The Galen Second Read is cloud-hosted and utilizes external accessories [e.g., scanner and image management systems (IMS)] for automatic ingestion of the input. The device identifies WSIs that are more likely to contain prostatic adenocarcinoma (AdC). For each input WSI, the Galen Second Read automatically analyzes the WSI and outputs the following:

Binary classification of the likelihood (high/low) to contain AdC based on a predetermined . threshold of the neural network output.
For slides classified with high likelihood to contain AdC, slide-level findings are flagged . and visualized (AdC score and heatmap) for additional review by a pathologist alongside the WSI.
For slides classified as low likelihood to contain AdC, no additional output is available. .

Galen Second Read key functionalities include image upload and analysis, flag slides of high likelihood to contain AdC and display of all the WSIs uploaded to the system alongside their analysis results. Flagged findings constitute a recommendation for additional review by a pathologist.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information for the Galen™ Second Read™ device, based on the provided text:

Acceptance Criteria and Device Performance

The document does not explicitly state pre-defined acceptance criteria with specific numerical targets. Instead, it presents the device's performance metrics from clinical studies. The implied acceptance criteria are that the device should improve the detection of prostatic adenocarcinoma (AdC) in initially benign cases when assisting pathologists.

Here are the reported device performance metrics from the provided studies:

Table 1: Device Performance (Clinical Study 1 - Standalone Performance)

Parameter	Estimate	95% CI	Context
Slide-Level
Sensitivity	81.0%	(69.2%; 92.9%)	Ability to correctly identify GT positive slides
Specificity	91.6%	(90.9%; 92.3%)	Ability to correctly identify GT negative slides
Case-Level
Sensitivity	80.8%	(74.1%; 87.6%)	Ability to correctly identify GT positive cases
Specificity	46.9%	(39.5%; 54.3%)	Ability to correctly identify GT negative cases

Table 2: Device Performance (Clinical Study 2 - Human-in-the-Loop Performance)

Parameter	Performance with Galen Second Read AI Assistance	Performance with Standard of Care (SoC)	Difference	95% CI (Difference)
Combined Pathologists (Overall)
Sensitivity	93.9%	90.5%	3.5%	(2.3%; 4.5%)
Specificity	87.9%	91.1%	-3.2%	(-4.3%; -1.9%)
For Slides Initially Assessed as Benign by Pathologists
Sensitivity	36.3%	0% (SoC)	36.3%	(28.0%; 45.5%)
Specificity	96.5%	100% (SoC)	-3.5% (approx)	(95.2%; 97.5%)

Study Information:

1. Sample Size and Data Provenance

Analytical Performance Studies (Precision and Localization):

Sample Size: Not explicitly stated as a single number for these studies. The tables show "n/N" values for positive and negative slides. For repeatability, there were 39 positive slides and 38 negative slides in each run (total for repeatability: 3 runs * 39 positive + 3 runs * 38 negative = 231 slide-reads). For reproducibility, it was also based on "39" and "38" slides for each scanner/operator combination.
Data Provenance: Retrospectively collected, de-identified slides.
Country of Origin: Not specified for these analytical studies.

Clinical Performance Study 1 (Standalone Performance):

Sample Size: 347 cases (initially diagnosed as benign) with associated whole slide images (WSIs).
Data Provenance: Retrospectively collected samples.
Country of Origin: Three sites, including 2 US sites and 1 Outside the US (OUS) site.

Clinical Performance Study 2 (Human-in-the-Loop Performance):

Sample Size: 772 cases/slides (376 negative cases and 396 positive cases).
Data Provenance: Retrospectively collected slides.
Country of Origin: Four sites, including 3 US sites and 1 OUS site.

2. Number of Experts and Qualifications for Test Set Ground Truth

Analytical Performance Studies:

Number of Experts: Not explicitly stated, but "GT determined as 'positive', or 'benign' by the GT pathologists" implies multiple pathologists.
Qualifications: "GT pathologists" - no specific experience level mentioned.

Clinical Performance Study 1 (Standalone Performance):

Number of Experts: Two independent expert pathologists for initial review, with a third independent expert pathologist for tie-breaking.
Qualifications: "Independent expert pathologists" - no specific experience level mentioned.

Clinical Performance Study 2 (Human-in-the-Loop Performance):

Number of Experts: Not explicitly detailed for the GT determination for this specific study, but it is likely consistent with Study 1's method, as it shares similar retrospective data characteristics.
Qualifications: Not explicitly detailed for the GT determination for this specific study.

3. Adjudication Method for the Test Set

Clinical Performance Study 1 (Standalone Performance):

Adjudication Method: 2+1 (Two independent expert pathologists, with a third independent expert pathologist to review disagreements and determine the majority rule for the final ground truth).

Analytical Performance Studies & Clinical Performance Study 2:

Adjudication Method: Not explicitly detailed, but implied to be expert consensus.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a MRMC comparative effectiveness study was done (Clinical Performance Study 2).
Effect Size of Human Readers Improvement with AI vs. without AI Assistance:
- Sensitivity: The combined sensitivity for pathologists improved by 3.5% (95% CI: 2.3%; 4.5%) with Galen Second Read assistance compared to SoC.
- Specificity: The combined specificity for pathologists decreased by 3.2% (95% CI: -4.3%; -1.9%) with Galen Second Read assistance compared to SoC.
- For slides initially assessed as benign by pathologists (the intended use population), sensitivity increased by 36.3% (from 0% in SoC to 36.3% with Galen Second Read). Specificity for these slides decreased by 3.5% (from 100% in SoC to 96.5% with Galen Second Read).

5. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) was done (Clinical Performance Study 1).
The results are shown in "Table 1: Device Performance (Clinical Study 1 - Standalone Performance)" above.

6. Type of Ground Truth Used

Expert Consensus: For both clinical performance studies, the ground truth for slides was established by expert pathologists via a consensus process (two independent experts, with a third for adjudication in cases of disagreement). The ground truth for cases was derived from the slide-level ground truth.

7. Sample Size for the Training Set

Not provided in the document. The document describes the device as a "deterministic deep convolutional network that has been developed with digitized WSIs...". However, it does not state the specific sample size, origin, or characteristics of the training dataset.

8. How Ground Truth for the Training Set Was Established

Not provided in the document. While it mentions the network was "developed with digitized WSIs," details on how the ground truth for these training images was established are not included in the provided text.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" followed by the words "U.S. FOOD & DRUG" and "ADMINISTRATION" stacked on top of each other.

Ibex Medical Analytics Ltd. Yael Liebes Peer, Ph.D. Official Correspondent 101 Rokach Blvd Tel Aviv. 6153101 Israel

Re: K241232

Trade/Device Name: Galen™ Second Read™ Regulation Number: 21 CFR 864.3750 Regulation Name: Software algorithm device to assist users in digital pathology Regulatory Class: Class II Product Code: QPN Dated: May 2, 2024 Received: May 2, 2024

Dear Dr. Yael Liebes Peer:

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/cdrb/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).

January 24, 2025

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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 and Part 809); 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-safetyreporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (OS) 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 Rule"). 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.

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).

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Sincerely,

Shyam Kalavar -S

Shyam Kalavar Deputy Branch Chief Division of Molecular Genetics and Pathology OHT7: Office of In Vitro Diagnostics Office of Product Evaluation and Quality Center for Devices and Radiological Health

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Indications for Use

510(k) Number (if known) K241232

Device Name Galen™ Second Read™

Indications for Use (Describe)

Galen™ Second Read™ is intended to be used with slide images digitized with Philips Ultra Fast Scanner and visualized using the Galen™ Second Read™ user interface.

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

Preparation date:

1/24/2025

Submitter:

Ibex Medical Analytics LTD

Contact person:

Yael Liebes Peer, PhD Ibex Medical Analytics LTD 101 Rokach Blvd, Tel Aviv, 6153101 ISRAEL +972 (52) 350-1487 [phone] yael.libespeer@ibex-ai.com

Device Information:

Device Trade Name: Galen™ Second Read™ Version: 3.1-US Device Class: II Product Code: OPN Classification Regulation: 21 CFR.864.3750 Classification Name: Software algorithm device to assist users in digital pathology Classification Panel: Pathology 510(k) Submission Number: K241232

Predicate device:

Device Trade Name: Paige Prostate DeNovo Number: DEN200080

I Intended Use/Indications for Use:

Galen™ Second Read™ is a software only device intended to analyze scanned histopathology whole slide images (WSIs) from prostate core needle biopsies (PCNB) prepared from hematoxylin & eosin (H&E) stained formalin-fixed paraffin embedded (FFPE) tissue. The device is intended to identify cases initially diagnosed as benign for further review by a pathologist. If Galen™ Second Read™ detects tissue morphology suspicious for prostate adenocarcinoma (AdC), it provides caseand slide-level alerts (flags) which includes a heatmap of tissue areas in the WSI that is likely to contain cancer.

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Galen™ Second Read™ is intended to be used with slide images digitized with Philips Ultra Fast Scanner and visualized using the Galen™ Second Read™ user interface.

Special Conditions for Use Statement(s):

Rx - For prescription use only

For in vitro diagnostic (IVD) use only

Summary of Technological Characteristics: II

Binary classification of the likelihood (high/low) to contain AdC based on a predetermined . threshold of the neural network output.
For slides classified with high likelihood to contain AdC, slide-level findings are flagged . and visualized (AdC score and heatmap) for additional review by a pathologist alongside the WSI.
For slides classified as low likelihood to contain AdC, no additional output is available. .

Galen Second Read is operated as follows:

1. Scanned digital images of PCNB are acquired using the designated digital pathology scanner (Philips IntelliSite Pathology Solution (PIPS) Ultra Fast Scanner (UFS)). Image and other related quality control steps are performed per the scanner instructions for use and any additional user site specifications. Upon availability of the WSI

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file, the scanned digital images are automatically processed (unless contraindicated) by the Galen Second Read in the background.

1. For every slide, the Galen Second Read's algorithm calculates and outputs a tissue ratio (a measure of the amount of tissue in the image), an out-of-focus ratio (a measure of the amount of blurry tissue in the image), and in case both are within predetermined ranges, it also outputs slide-level AdC score (likelihood to contain AdC), and an associated AdC heatmap.
1. Galen Second Read is used for all slides from cases with pathologist's initial benign diagnosis. In case the slide was found by the Galen Second Read to be with high likelihood to contain AdC, the device flags for additional review by the pathologist.
1. All flagged findings are available in the Galen Second Read and the pathologist can select a patient case and open each flagged WSI for an additional review. The available information for review includes AdC score (the likelihood to contain AdC) and AdC heatmap marking the region suggestive to include cancer. The heatmap opacity can be controlled and toggled on/off to allow unobstructed reexamination. The AdC likelihood and the associated heatmap are indicative for AdC. The diagnosis may be documented in another system, e.g., a Laboratory Information System (LIS).
1. A resolution by the reviewing pathologist can be either of the following options: confirm the Galen Second Read result (alter the initial diagnosis from benign to malignant), reject the Galen Second Read result (no additional action is required) or decide that more information is needed to support diagnosis, based on pathology laboratory best practices, outside of the Galen Second Read device.

The final determination of diagnosis is made by the pathologist based on the histologic findings and/or additional tests and should not be solely based on the Galen Second Read output. The flagged findings constitute a recommendation for a second pathologist review. Pathologists should follow the SoC to obtain additional information, if needed, to render a final diagnosis.

Interoperable components intended for use with Galen Second Read and minimum system requirements are provided in Table 1 and Table 2, respectively.

Manufacturer	Model
Philips Medical SystemsNederland B.V.	Ultra-Fast Scanner (UFS)
Display	Philips PS27QHDCR, Barco N.V. NV MDPC-8127

Table 1: WSI Scanner and Display

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Workstation component	Specifications
Computer System	RAM: 8.0 GB or higherCPU: 1 GHz or higher
Web Browser	Google Chrome v120 or later
Operating system	Windows v10 or Mac OS v11 or higher
Network	Internet accessAt least 20 Mbps download speed

Table 2: Computer Environment/System Requirements

III Comparison with Predicate(s):

The following table summarizes the similarities and differences between the Galen Second Read and the predicate device, Paige Prostate.

Device &PredicateDevice(s):	K241232	DEN200080
Device TradeName	Galen TM Second ReadTM	Paige Prostate
General Device Characteristic Similarities
Intended Use/Indications For Use	GalenTM Second ReadTM is asoftware only device intended toanalyze scanned histopathologywhole slide images (WSIs) fromprostate core needle biopsies(PCNB) prepared from hematoxylin& eosin (H&E) stained formalin-fixed paraffin embedded (FFPE)tissue. The device is intended toidentify cases initially diagnosed asbenign for further review by apathologist. If GalenTM SecondReadTM detects tissue morphologysuspicious for prostateadenocarcinoma (AdC), it providescase- and slide-level alerts (flags)which includes a heatmap of tissueareas in the WSI that is likely tocontain cancer.GalenTM Second ReadTM is intendedto be used with slide imagesdigitized with Philips Ultra FastScanner and visualized using theGalenTM Second ReadTM userinterface.GalenTM Second ReadTM outputs arenot intended to be used on astandalone basis for diagnosis, torule out prostatic AdC or to preclude	Paige Prostate is a software onlydevice intended to assist pathologistsin the detection of foci that aresuspicious for cancer during thereview of scanned whole slideimages (WSI) from prostate needlebiopsies prepared from hematoxylin& eosin (H&E) stained formalinfixed paraffin embedded (FFPE)tissue. After initial diagnostic reviewof the WSI by the pathologist, ifPaige Prostate detects tissuemorphology suspicious for cancer, itprovides coordinates (X,Y) on asingle location on the image with thehighest likelihood of having cancerfor further review by thepathologist.Paige Prostate isintended to be used with slideimages digitized with Philips UFSand visualized with Paige FullFocusWSI viewing software.Paige Prostateis an adjunctive computer-assistedmethodology and its output shouldnot be used as the primary diagnosis.Pathologists should only use PaigeProstate in conjunction with theircomplete standard of care evaluationof the slide image.

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	region suggestive to include cancer.Based on the device output, thepathologist can reexamine the slideand modify the initial diagnosis toreflect the additional findings, ifrequired.	diagnosis to reflect the additionalfindings.
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Galen Second Read, has similar indications for use and intended use as the predicate device. The technological characteristics differences do not raise new questions of safety and effectiveness. Thus, the Galen Second Read is substantially equivalent.

IV Summary of Performance Validation:

A. Analytical Performance:

Precision and localization studies were performed to assess the Galen Second Read precision (repeatability and reproducibility) in identifying WSI of PCNBs suspicious of cancer, its localization accuracy and its localization precision of detecting the pixels that belong to cancer foci in WSIs from PCNBs of adult male subjects. The slides were retrospectively collected, de-identified and scanned by different PIPS UFS scanners and operators. Slides were processed by Galen Second Read, and results for the slides were either positive or negative. The study has demonstrated that the Galen Second Read identification of WSI of PCNBs suspicious of cancer, is precise (repeatable and reproducible, as demonstrated for within-scanner/operator (Table 3) and betweenscanner/operator (Table 4). Localization accuracy and precision was demonstrated by comparing region of interest (ROI) and an AdC heatmap generated by the device to Ground Truth (GT) determined as "positive", or "benign" by the GT pathologists. Results showed the area marked by AdC heatmap is consistent with the GT, thus the Galen Second Read localization is accurate and precise in detecting tissue area that belong to cancer foci in a population of adult male subjects who have undergone PCNB.

	Agreement with Ground Truth by Slide Type
	Positive Slides	Negative Slides
Operator / Scanner /Run	Percent CorrectCalls,% (n/N)	95% CI	Percent ofCorrect Calls% (n/N)	95% CI
Run 1	97.4% (38/39)	(86.8%, 99.5%)	86.8% (33/38)	(72.7%, 94.2%)
Run 2	97.4% (38/39)	(86.8%, 99.5%)	92.1% (35/38)	(79.2%, 97.3%)
Run 3	97.4% (38/39)	(86.8%, 99.5%)	86.8% (33/38)	(72.7%, 94.2%)
Overall Average	97.4%	(92.7%, 99.1%)	88.6%	(81.5%, 93.2%)

	Table 3: Repeatability (Within-Scanner Precision): Percent of Correct Calls

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	Agreement with Ground Truth by Slide Type
	Positive Slides		Negative Slides
	Percent ofCorrect Calls,% (n/N)	95% CI	Percent ofCorrect Calls,% (n/N)	95% CI
Operator / Scanner	97.4% (38/39)	(86.8%; 99.9%)	86.8% (33/38)	(72.7%; 94.2%)
Scanner 1/Operator 1	97.4% (38/39)	(86.8%; 99.9%)	86.8% (33/38)	(72.7%; 94.2%)
Scanner 2/Operator 2	94.9% (37/39)	(83.1%;98.6%)	86.8% (33/38)	(72.7%; 94.2%)
Scanner 3/Operator 3	97.4% (38/39)	(86.8%; 99.5%)	84.2% (32/38)	(69.6%; 92.6%)
Overall Average	96.6%	(91.5%, 98.7%)	86.0%	(78.4%; 91.2%)

Table 4: Reproducibility (Between-Scanner/Operator) Precision: Percent of Correct Calls

B. Clinical Performance:

Clinical validation studies using the Galen Second Read demonstrated the device's ability to improve clinical performance. The Galen Second Read demonstrated an increase in detection of prostatic AdC in PCNBs, enabling the identification of positive cases (cancer, including AdC, ASAP (atypical small acinar proliferation) and other rare cancer subtypes) that may have been missed at the initial read by the pathologist.

Two clinical studies were conducted to assess the performance of the Galen Second Read.

In the first study, the performance of the Galen Second Read in identifying prostatic adenocarcinoma cases (subjects) missed by Standard of Care (SoC) in a population of subjects who have undergone PCNB was assessed. This study was performed with retrospectively collected samples, and it was conducted at three sites [2 US sites and 1 Outside the US (OUS)]. The device analyzed scanned histopathology whole slide images (WSIs) of hematoxylin and eosin (H&E) from 347 cases who were initially diagnosed as benign based on the prostate core needle biopsies. The WSIs were scanned with a Philips scanner at 40x magnification. Slides from 347 cases were then processed by Galen Second Read and each slide was either "Flag" (Positive) or "No Flag" (Negative).

In the study, Ground Truth (GT) determination with regard to GT for a slide and GT for a case (subject) was performed in a following way: the GT determination for a slide was performed by two independent expert pathologists; slides where the pathologists disagreed, a third independent expert pathologist was asked to review the slide and the majority rule determined the GT for the slide. Slides with prostatic AdC or other cancer ASAP were considered GT positive slides.

For slide-level analysis, the definitions of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) are as follows:

	Definition
TP slide	Slide is GT positive and Flagged by device
FP slide	Slide is GT negative and Flagged by device

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FN slide	Slide is GT positive and Not Flagged by device
TN slide	Slide is GT negative and Not Flagged by device

For case-level analysis, the definitions of TP, FP, TN, and FN are as follows:

	Definition
TP case	Case has only one GT positive slide and this slide is Flagged by the deviceCase has more than one GT positive slide and at least one of these GT positiveslides is Flagged by device
FP case	Case has all slides GT Negative and at least one slide is Flagged by the device
FN case	Case has one or multiple GT positive slides and all these GT positive slides areNot Flagged (missed) by the device
TN case	Case has all slides GT negative and all slides are not Flagged by the device

The Galen Second Read performance (sensitivity and specificity) is provided at the level of a slide (Table 5) and at the level of a case (subject) (Table 6).

Table 5: Slide-Level Device Performance

Parameter	Estimate	95% CI
Sensitivity	81.0%	(69.2%; 92.9%)
Specificity	91.6%	(90.9%; 92.3%)

Table 6: Case-Level Device Performance

Parameter	Estimate	95% CI
Sensitivity	80.8%	(74.1%; 87.6%)
Specificity	46.9%	(39.5%; 54.3%)

The study demonstrated that the Galen™ Second Read™ effectively identifies prostate cancer cases, including ASAP, missed by the SoC in a population of subjects who have undergone a PCNB. The study demonstrated that sensitivity of the Galen Second Read is higher than the sensitivity of the SoC read (sensitivity of SoC was 0% because all the slides were diagnosed initially as benign by SoC), both at the slide-level and case-level. In the study there was a decrease in specificity of the Galen Second Read compared to the specificity of SoC (specificity of SoC was 100% because all the slides were diagnosed initially as benign by SoC) and it can be managed by mitigation measures such as use of additional stains to confirm if the slide/case is positive.

In the second study, difference in performances of a pathologist supported by the Galen Second Read vs GT result and a pathologist with SoC vs GT result in a set of retrospectively collected slides (772 cases/slides: 376 negative cases and 396 positive

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cases). The study was conducted at 4 sites (3 US and 1 OUS) with 3 pathologists at each site, for a total of 12 pathologists. The study consisted of two arms:

· Arm A: a SoC arm, in which slides were read digitally using the routine lab practice.

· Arm B: a Galen Second Read workflow arm, in which after reading slides digitally, the pathologist reviewed images flagged by the Galen Second Read and decided on a final read determination.

All cases and associated slides were read in both Arms of the study (Arm A and Arm B), each case was read 3 times, once per pathologist, with a washout period of two weeks between Arms to minimize recall bias.

Sensitivity and specificity for each pathologist were estimated and results are presented in Table 7 and Figure 1.

Pathologist	Sensitivity			Specificity
	With Galen	SoC	Difference	With Galen	SoC	Difference
1	96.9%	94.9%	2.0%	88.8%	91.8%	-3.1%
2	96.9%	92.9%	4.1%	72.4%	76.5%	-4.1%
3	89.8%	87.8%	2.0%	89.8%	91.8%	-2.0%
4	94.4%	94.4%	0.0%	89.2%	89.2%	0.0%
5	95.3%	95.3%	0.0%	90.3%	91.4%	-1.1%
6	89.7%	84.1%	5.6%	100%	100%	0.0%
7	96.5%	84.9%	11.6%	89.0%	97.8%	-8.8%
8	90.7%	84.9%	5.8%	85.9%	89.1%	-3.3%
9	85.9%	84.7%	1.2%	95.7%	96.7%	-1.1%
10	98.1%	96.2%	1.9%	82.8%	86.0%	-3.2%
11	92.4%	91.4%	1.0%	91.4%	91.4%	0.0%
12	99.0%	91.4%	7.6%	80.6%	92.5%	-11.8%
Combined	93.9%	90.5%	3.5%	87.9%	91.1%	-3.2%

Table 7. Sensitivity and Specificity Presented by Pathologist

Sensitivity and specificity for the combined data were calculated and presented in Table 8.

{13}------------------------------------------------

	Sensitivity
	Estimate	(n/N)	95%CI
With Galen	93.9%	(1115/1187)	(92.2%; 95.8%)
SoC	90.5%	(1074/1187)	(88.5%; 92.6%)
Difference	3.5%	(41/1187)	(2.3%; 4.5%)
	Specificity
With Galen	87.9%	(991/1127)	(85.8%; 90.4%)
SoC	91.1%	(1027/1127)	(89.3%; 93.2%)
Difference	-3.2%	(-36/1127)	(-4.3%; -1.9%)

Table 8. Sensitivity and Specificity of Pathologists with Galen Device vs SoC

This clinical study demonstrated a statistically significant improvement in sensitivity of 3.5% with 95%CI: (2.3%; 4.5%) and statistically significant decrease in specificity of -3.2% with 95%CI: (-4.3%; -1.9%).

Image /page/13/Figure/3 description: The image is a scatter plot titled "Improvement in Sensitivity for 12 Pathologists". The x-axis is labeled "Sensitivity (%) of Pathologists by SoC" and ranges from 82 to 98. The y-axis is labeled "Improvement in Sensitivity (%)" and ranges from 0 to 14. The data points are scattered across the plot, showing the relationship between the sensitivity of pathologists and the improvement in sensitivity.

Improvement in Sensitivity for 12 Pathologists

Figure 1. Improved Sensitivity for the pathologists assisted by the Galen™ Second Read™ as compared with pathologists reviewing in SoC.

Sensitivity and specificity for the slides initially assessed by a pathologist as benign (the intended use population of the device) are also calculated and presented in Table 9.

{14}------------------------------------------------

	Sensitivity		Specificity
Pathologist	With Galen	(n/N)	With Galen	(n/N)
1	40.0%	(2/5)	96.7%	(87/90)
2	57.1%	(4/7)	94.7%	(71/75)
3	16.7%	(2/12)	97.8%	(88/90)
4	0.0%	(0/6)	100%	(83/83)
5	0.0%	(0/5)	98.8%	(84/85)
6	35.3%	(6/17)	100%	(93/93)
7	76.9%	(10/13)	91.0%	(81/89)
8	38.5%	(5/13)	96.3%	(79/82)
9	7.7%	(1/13)	98.9%	(88/89)
10	50.0%	(2/4)	96.3%	(77/80)
11	11.1%	(1/9)	100%	(85/85)
12	88.9%	(8/9)	87.2%	(75/86)
Combined	36.3%	(41/113)	96.5%	(991/1027)
	95%CI: (28.0%; 45.5%)		95%CI: (95.2%; 97.5%)

Table 9. Sensitivity and Specificity for the Slides Initially Assessed as Benign vs GT

This study demonstrated that the sensitivity of the pathologists using the Galen Second Read for the cases/slides which were initially diagnosed as benign was 36.3% with 95%C1: (28.0%; 45.5%) (sensitivity without the device was 0% because all the slides were diagnosed initially as benign by SoC), yielding increase in sensitivity of 36.3%.

Specificity of the pathologist using the Galen Second Read for the cases/slides which were initially diagnosed as benign was 96.5% with 95%CI: (95.2%; 97.5%) (specificity without the device was 100%), yielding decrease in specificity of 3.5%. The decrease in specificity can be managed by mitigation measures such as use of additional stains to confirm if the slide/case is positive.

V Substantial Equivalence Determination:

The Galen™ Second Read™ device is substantially equivalent to the predicate device.

Regulation Number and Section

§ 864.3750 Software algorithm device to assist users in digital pathology.

(a)
Identification. A software algorithm device to assist users in digital pathology is an in vitro diagnostic device intended to evaluate acquired scanned pathology whole slide images. The device uses software algorithms to provide information to the user about presence, location, and characteristics of areas of the image with clinical implications. Information from this device is intended to assist the user in determining a pathology diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) The intended use on the device's label and labeling required under § 809.10 of this chapter must include:
(i) Specimen type;
(ii) Information on the device input(s) (
e.g., scanned whole slide images (WSI), etc.);(iii) Information on the device output(s) (
e.g., format of the information provided by the device to the user that can be used to evaluate the WSI, etc.);(iv) Intended users;
(v) Necessary input/output devices (
e.g., WSI scanners, viewing software, etc.);(vi) A limiting statement that addresses use of the device as an adjunct; and
(vii) A limiting statement that users should use the device in conjunction with complete standard of care evaluation of the WSI.
(2) The labeling required under § 809.10(b) of this chapter must include:
(i) A detailed description of the device, including the following:
(A) Detailed descriptions of the software device, including the detection/analysis algorithm, software design architecture, interaction with input/output devices, and necessary third-party software;
(B) Detailed descriptions of the intended user(s) and recommended training for safe use of the device; and
(C) Clear instructions about how to resolve device-related issues (
e.g., cybersecurity or device malfunction issues).(ii) 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, medical history, user experience, and scanning equipment, as applicable.
(iii) Limiting statements that indicate:
(A) A description of 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), including any limitations in the dataset used to train, test, and tune the algorithm during device development;(B) The data acquired using the device should only be interpreted by the types of users indicated in the intended use statement; and
(C) Qualified users should employ appropriate procedures and safeguards (e.g., quality control measures, etc.) to assure the validity of the interpretation of images obtained using this device.
(3) Design verification and validation must include:
(i) A detailed description of the device software, including its algorithm and its development, that includes a description of any datasets used to train, tune, or test the software algorithm. This detailed description of the device software must include:
(A) A detailed description of the technical performance assessment study protocols (e.g., regions of interest (ROI) localization study) and results used to assess the device output(s) (e.g., image overlays, image heatmaps, etc.);
(B) The training dataset must include cases representing different pre-analytical variables representative of the conditions likely to be encountered when used as intended (e.g., fixation type and time, histology slide processing techniques, challenging diagnostic cases, multiple sites, patient demographics, etc.);
(C) The number of WSI in an independent validation dataset must be appropriate to demonstrate device accuracy in detecting and localizing ROIs on scanned WSI, and must include subsets clinically relevant to the intended use of the device;
(D) Emergency recovery/backup functions, which must be included in the device design;
(E) System level architecture diagram with a matrix to depict the communication endpoints, communication protocols, and security protections for the device and its supportive systems, including any products or services that are included in the communication pathway; and
(F) A risk management plan, including a justification of how the cybersecurity vulnerabilities of third-party software and services are reduced by the device's risk management mitigations in order to address cybersecurity risks associated with key device functionality (such as loss of image, altered metadata, corrupted image data, degraded image quality, etc.). The risk management plan must also include how the device will be maintained on its intended platform (
e.g. a general purpose computing platform, virtual machine, middleware, cloud-based computing services, medical device hardware, etc.), which includes how the software integrity will be maintained, how the software will be authenticated on the platform, how any reliance on the platform will be managed in order to facilitate implementation of cybersecurity controls (such as user authentication, communication encryption and authentication, etc.), and how the device will be protected when the underlying platform is not updated, such that the specific risks of the device are addressed (such as loss of image, altered metadata, corrupted image data, degraded image quality, etc.).(ii) Data demonstrating acceptable, as determined by FDA, analytical device performance, by conducting analytical studies. For each analytical study, relevant details must be documented (e.g., the origin of the study slides and images, reader/annotator qualifications, method of annotation, location of the study site(s), challenging diagnoses, etc.). The analytical studies must include:
(A) Bench testing or technical testing to assess device output, such as localization of ROIs within a pre-specified threshold. Samples must be representative of the entire spectrum of challenging cases likely to be encountered when the device is used as intended; and
(B) Data from a precision study that demonstrates device performance when used with multiple input devices (e.g., WSI scanners) to assess total variability across operators, within-scanner, between-scanner and between-site, using clinical specimens with defined, clinically relevant, and challenging characteristics likely to be encountered when the device is used as intended. Samples must be representative of the entire spectrum of challenging cases likely to be encountered when the device is used as intended. Precision, including performance of the device and reproducibility, must be assessed by agreement between replicates.
(iii) Data demonstrating acceptable, as determined by FDA, clinical validation must be demonstrated by conducting studies with clinical specimens. For each clinical study, relevant details must be documented (e.g., the origin of the study slides and images, reader/annotator qualifications, method of annotation, location of the study site(s) (on-site/remote), challenging diagnoses, etc.). The studies must include:
(A) A study demonstrating the performance by the intended users with and without the software device (e.g., unassisted and device-assisted reading of scanned WSI of pathology slides). The study dataset must contain sufficient numbers of cases from relevant cohorts that are representative of the scope of patients likely to be encountered given the intended use of the device (e.g., subsets defined by clinically relevant confounders, challenging diagnoses, subsets with potential biopsy appearance modifiers, concomitant diseases, and subsets defined by image scanning characteristics, etc.) such that the performance estimates and confidence intervals for these individual subsets can be characterized. The performance assessment must be based on appropriate diagnostic accuracy measures (e.g., sensitivity, specificity, predictive value, diagnostic likelihood ratio, etc.).
(B) [Reserved]