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Monoclonal Mouse Anti-Human Estrogen Receptor, Clone 1D5 may be used in the semi-quantitative detection of human estrogen receptor in tissue sections of human breast cancer by immunohistochemistry. The information gained by this assay can aid in assessing the likelihood of response to therapy as well as in the prognosis and management of breast cancer patients.

The clinical interpretation of any positive staining or its absence should be complemented by morphological and histological studies with proper controls. Evaluations should be made within the context of the patient's clinical history and other diagnostic tests by a qualified individual having knowledge of all the potential antibody reactivities.

1. Monoclonal Mouse Anti-Human Estrogen Receptor, Clone 1D5, Device Code No. M7047) is a mouse anti-human monoclonal antibody (Product) produced as a tissue culture supernatant. The antibody is supplied in Description: 0.05M Tris-HCl buffer, pH 7.2, containing fotal bovine serum and 15mM sodium azide. (1mL total volume).

2. Monoclonal Mouse Anti-Human Estrogen Receptor. Clone 105 Ready-to-Use Antibody and Negative Control (Product Code No. N1576) consists of a mouse anti-human monoclonal antibody produced as a tissue culture supernatant and pre-diluted in 0.05M Tris-HCI buffer, pH 7.6, containing fotal bovine serum and 15mM sodium azide (7mL total volume). The primary antibody is packaged with a negative control reagent consisting of a cocktail of purified mouse immunoglobulins (IgG, IgG, IgGz, IgGz, IgGz and IgM) in 0.05M Tris-HCI buffer, pH 7.6 and 15mM sodium azide (5mL total volume).

The provided text describes DAKO's Monoclonal Mouse Anti-Human Estrogen Receptor, Clone 1D5 (anti-ER, 1D5) and its comparison to a predicate device, Abbott ER-ICA Monoclonal, clone H222. The studies performed focused on demonstrating substantial equivalence, rather than establishing specific quantitative acceptance criteria for the anti-ER, 1D5 device alone against predefined thresholds.

However, based on the comparative effectiveness studies presented, we can infer performance and the methods used to demonstrate the device's utility.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly state quantitative "acceptance criteria" for the anti-ER, 1D5 antibody against a specific clinical outcome. Instead, it demonstrates performance by comparing it to an already approved predicate device (Abbott ER-ICA H222) and by citing published studies on its use. The primary goal was to establish substantial equivalence.

The table below summarizes the reported performance of anti-ER, 1D5 and its comparison to the predicate device and clinical outcomes, where available.

2. Sample Size Used for the Test Set and the Data Provenance

The "test set" here refers to the various studies cited in the submission for comparative effectiveness and clinical utility. There isn't a single "test set" but rather a collection of published research:

• Normal Tissue Testing: A "required panel of normal tissues" as specified by FDA guidance. The exact number is not explicitly stated in the summary but indicates a prospective testing approach.
• Reproducibility Testing: Eight serial sections from each of three different paraffin-embedded blocks of human breast carcinoma. This was an internal prospective study.
• Comparative Effectiveness and Clinical Utility Studies (Table 1): This table summarizes findings from various published articles.
  • Mauri, et al.: 374 samples.
  • Goulding, et al.: 90 samples.
  • Nedergaard et al.: 83 samples.
  • Pertschuk et al.: 74 samples (all had Stage IV disease; included White, AfroAm, Hispanic, Asian demographics).
  • Leong and Milos.: 31 samples.
  • Pellicer and Sundblad: 300 patients with 5-year follow-up.
  • Hopkins et al.: 51 samples.
  • Hendricks and Wilkinson: 20 samples.

Data Provenance for Published Studies: The text does not explicitly state the country of origin for each study, but given the journal names and author affiliations typically found in such publications, it's highly likely to be a mix of international (including US and European) retrospective cohort studies. The studies are consistently referred to as "Published Immunoreactivity."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

The text does not specify the number or qualifications of experts used to establish ground truth for the test set utilized in the submission. The studies cited (Table 1) often compare the anti-ER, 1D5 results to the predicate device (H222) or to clinical outcomes like response to tamoxifen.

• In the case of comparison to H222 (predicate device), the ground truth for H222 results would have been established by trained pathologists.
• For clinical outcome data (e.g., tamoxifen response, survival), the ground truth is derived from patient medical records and follow-up data, generally managed and interpreted by clinicians.
• The scoring systems mentioned (e.g., percentage of stained tumor nuclei, H scores, 0-3+ intensity) imply evaluation by trained individuals, likely pathologists, but their number and specific qualifications are not detailed in this summary.

4. Adjudication Method for the Test Set

The submission does not explicitly describe an adjudication method (like 2+1 or 3+1) for the test set examples presented. The performance metrics are reported from individual published studies, which would have had their own internal methods for slide interpretation and data analysis. These methods often involve interpretation by one or more pathologists, but explicit adjudication rules are not provided in this summary.

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

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done involving AI assistance. This submission describes an antibody for immunohistochemical staining, not an AI-powered diagnostic device. The comparisons are between two different antibodies (DAKO's 1D5 and Abbott's H222) and their correlation with clinical outcomes.

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

No, a standalone (algorithm-only) performance study was not conducted. This is not an AI device. The device is a diagnostic reagent (antibody) that is interpreted by human observers (pathologists) under a light microscope. The text mentions "image analysis systems" and "automated stainers" as being used in efforts for greater objectivity but notes they "have yet to be validated in the literature," implying they are not part of the core performance claims for this specific 510(k) submission.

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

The ground truth used across the various studies cited is a combination of:

• Pathology: Evaluation of tissue sections by pathologists for the presence and quantity of estrogen receptor staining (both for the predicate device H222 and for anti-ER, 1D5). This includes semiquantitative scoring systems (e.g., percentage of positive cells, staining intensity, H scores).
• Clinical Outcomes Data: Patient follow-up data related to response to therapy (e.g., tamoxifen), overall survival, and relapse-free survival.
• Expert Consensus (Implicit): While not explicitly stated as a formal "expert consensus" process for establishing ground truth within the context of the submission's own testing, the predicate device (Abbott ER-ICA H222) had previously been approved by the FDA based on its established utility and, presumably, expert agreement on its diagnostic accuracy. The studies comparing anti-ER, 1D5 to H222 are effectively using H222 results (interpreted by pathologists) as a reference.

8. The Sample Size for the Training Set

The concept of a "training set" is not applicable in the context of this device, as it is not an AI/machine learning algorithm. The studies described are for validation and comparison of a biological reagent (antibody).

9. How the Ground Truth for the Training Set Was Established

As this is not an AI/machine learning device, there is no "training set" for which ground truth would be established. The information gathered about the antibody's characteristics and performance came from its development, internal testing, and independent published research.

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