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510(k) Data Aggregation

    K Number
    K241232
    Date Cleared
    2025-01-24

    (267 days)

    Product Code
    Regulation Number
    864.3750
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    Galen™ Second Read™

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended 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 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)

    ParameterEstimate95% CIContext
    Slide-Level
    Sensitivity81.0%(69.2%; 92.9%)Ability to correctly identify GT positive slides
    Specificity91.6%(90.9%; 92.3%)Ability to correctly identify GT negative slides
    Case-Level
    Sensitivity80.8%(74.1%; 87.6%)Ability to correctly identify GT positive cases
    Specificity46.9%(39.5%; 54.3%)Ability to correctly identify GT negative cases

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

    ParameterPerformance with Galen Second Read AI AssistancePerformance with Standard of Care (SoC)Difference95% CI (Difference)
    Combined Pathologists (Overall)
    Sensitivity93.9%90.5%3.5%(2.3%; 4.5%)
    Specificity87.9%91.1%-3.2%(-4.3%; -1.9%)
    For Slides Initially Assessed as Benign by Pathologists
    Sensitivity36.3%0% (SoC)36.3%(28.0%; 45.5%)
    Specificity96.5%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.
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