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Vision BioSystems Progesterone Receptor Clone 16 (PGR Clone 16) Mouse Monoclonal antibody is intended for laboratory use to qualitatively identify by light microscopy, progesterone receptor (PGR) antigen in sections of formalin fixed, paraffin embedded tissue. PGR Clone 16 specifically binds to the PGR antigen located in the nucleus of PGR positive normal and neoplastic cells.

PGR Clone 16 is indicated as an aid in the management, prognosis and prediction of therapy outcome of breast cancer. The clinical interpretation of any staining or its absence should be complemented by morphological studies using proper controls and should be evaluated within the context of the patient's clinical history and other diagnostic tests by a qualified pathologist.

Novocastra™ antibodies are intended for manual use. Origin™ antibodies are optimized for use with the Ventana® Medical Systems, NexES® and BenchMark™ Immunohistochemistry Staining Systems in combination with Ventana® Defection Kits. Bond™ Ready-to-Use Primary Antibodies are optimized for use on the Vision BioSystems Bond-max™ system.

PGR Clone 16 is a monoclonal mouse antibody that detects a human progesterone receptor epitope located in the nucleus of PGR positive cells.

This looks like a 510(k) premarket notification for an immunohistochemistry reagent, not a typical AI/ML-powered medical device. The provided text describes the device (Vision BioSystems Progesterone Receptor Clone 16), its intended use, and its substantial equivalence to a predicate device.

Therefore, the requested information about acceptance criteria, study data, sample sizes, ground truth, experts, and AI-specific performance metrics (like MRMC studies) are not typically found in this type of submission for a an IHC reagent. These details are much more relevant for diagnosing, detecting or predicting disease with AI/ML systems.

I cannot extract the detailed information requested in your prompt because it is not present in the provided document. The document describes a traditional in-vitro diagnostic device (an antibody for pathology) and not an AI/ML-based device that would have the kind of performance studies and statistical analyses you're asking for.

However, I can provide a general interpretation based on the nature of 510(k) submissions for such devices:

Likely "Acceptance Criteria" for such a device would relate to:

• Analytical Performance:
  • Specificity: The antibody should bind only to the progesterone receptor (PGR) antigen and not to other irrelevant antigens. This is typically assessed through staining of known positive and negative tissues.
  • Sensitivity: The antibody should detect PGR wherever it is present in the tissue.
  • Reproducibility/Repeatability: Consistent staining results when tested multiple times by the same operator or different operators, and across different lots of the reagent.
  • Stability: The reagent maintains its performance characteristics over its shelf life.
• Clinical Performance (in comparison to predicate):
  • Concordance: The results obtained with the new device (PGR Clone 16) should be substantially equivalent (highly concordant) with those obtained using the legally marketed predicate device (DAKO Corporation Mouse Monoclonal Progesterone Receptor PgR 636) in terms of positive and negative staining, and often, the intensity and pattern of staining.

The "Study That Proves the Device Meets Acceptance Criteria" would fundamentally be:

• A "Bridging Study" or "Comparative Study" against the predicate device. This involves testing a set of clinical tissue samples (often breast cancer tissues, given the intended use) with both the new device and the predicate device.
• Internal Verification/Validation Studies: These are conducted by the manufacturer to establish the analytical performance characteristics mentioned above.

Given the limitations of the provided text, I can only fill in the table and address the other points based on what is typically done for such a device, rather than explicit details from this particular 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified in the provided text. For IHC antibody 510(k)s, comparison studies against a predicate typically involve tens to hundreds of clinical cases, covering a range of positive and negative expressions.
• Data Provenance: Not specified. Would typically involve anonymized tissue samples from pathology archives, likely retrospective since it's a validation study against an existing predicate. Country of origin not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified. For a comparative study, results for both the candidate and predicate device would ideally be read by at least one, and often two or more, qualified pathologists. For concordance, disagreements might be resolved by consensus or a third expert.
• Qualifications of Experts: Must be qualified pathologists ("evaluated by a qualified pathologist" is mentioned in the Intended Use, implying this for clinical interpretation). Typically board-certified pathologists with experience in breast pathology.

4. Adjudication method for the test set

• Adjudication Method: Not specified. If multiple pathologists were involved in reading the comparative slides, common methods include consensus review, majority vote (e.g., 2 out of 3 agree), or an independent third pathologist for discordant cases.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No. This is not an AI-powered device. It is a laboratory reagent (an antibody). MRMC studies, especially those comparing human readers with and without AI assistance, are not applicable here.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: No. This is a manual or automated (with specific staining systems) laboratory reagent that requires interpretation by a human pathologist. There is no AI algorithm involved in its primary function of detecting the antigen.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Ground Truth: For comparative studies, the "ground truth" is often established by:
  • Predicate Device Results: The results from the legally marketed predicate device (DAKO PgR 636) would serve as the primary reference for comparison for the new device.
  • Pathology Review/Expert Consensus: Confirmation of the presence/absence and classification of PGR status by qualified pathologists reviewing H&E (hematoxylin and eosin) stained slides and potentially the predicate IHC stain.
  • Clinical Diagnosis/Outcomes: While specific outcomes data might not be the direct "ground truth" for assay performance, the clinical utility is linked to established correlations between PGR status and breast cancer management/prognosis/therapy outcome.

8. The sample size for the training set

• Training Set Sample Size: Not applicable in the context of an antibody reagent. Antibodies are not "trained" like AI algorithms. They are developed and validated. The development process would involve extensive analytical testing on various tissues, but there isn't a "training set" in the AI sense.

9. How the ground truth for the training set was established

• Ground Truth for Training Set: Not applicable. For antibody development, quality control (QC) references (known positive/negative tissues) are used to assess the binding characteristics, but this is part of analytical validation, not "training."

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