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Ventana Medical Systems' ER Primary Antibody (Clone 6F11) may be used in immunohistochemical methods to aid in the identification of estrogen receptor (ER) antigen in normal and neoplastic cells as an aid in the diagnosis of ER positive tumors. The anti-ER (Clone 6F11) is intended for laboratory use to qualitatively stain ER antigen in sections of formalin fixed, paraffin embedded tissue on a Ventana automated slide staining device. It is further intended as an aid in the assessment of prognosis in the evaluation of breast cancer. Detection chemistries used by Ventana to detect ER are DAB, AEC, Alkaline phosphatase red and blue. Light microscopy is used to detect the staining of cell components. It is indicated for use in the management, prognosis and prediction of therapy outcome of breast cancer.

Ventana's ER Primary Antibody (clone 6F11) is a monoclonal antibody which specifically binds to estrogen receptor antigen located in the nuclear region of a variety of normal and neoplastic tissues. The dispenser contains approximately 5 ug (50 test) or 25 ug (250 test) of mouse anti-human ER monoclonal antibody. The total protein concentration of the reagent is approximately 1 mg/ml. Specific antibody concentration is approximately 1 ug/ml. Clone 6F11 is immunoglobulin class IgG, light chain kappa. There is no known irrelevant antibody in the preparation.

{
  "1. A table of acceptance criteria and the reported device performance": {
    "Acceptance Criteria": "The acceptance criteria are not explicitly stated as numerical targets in the provided document. However, the study aims to demonstrate substantial equivalence to the predicate device and good agreement with a chemical cytosol receptor assay (SBA/DCC) for ER, along with acceptable sensitivity and specificity.",
    "Reported Device Performance": {
      "Agreement (with ER SBA/DCC at >=11% tumor cell nuclei positive)": "93% concordant (95% CI: 86-97%)",
      "Relative Sensitivity (Resolved, with ER SBA/DCC at >=11% tumor cell nuclei positive)": "98.6% (71/72) (95% CI: 92.5-100%)",
      "Relative Specificity (Resolved, with ER SBA/DCC at >=11% tumor cell nuclei positive)": "78.6% (22/28) (95% CI: 59.1-91.7%)",
      "Tissue Specificity (Normal Tissues)": "Appropriate staining patterns, with some positive nuclear staining in expected tissues (breast, cervix, endometrium, ovary) and negative in others. Unexpected negative staining in one case of poorly preserved breast tissue and one endometrium case.",
      "Tissue Specificity (Neoplastic Tissues)": "Positive nuclear staining in 100% of tumor cells in both ovarian cancer cases and in surrounding normal tissue of two cervical cancer cases. All other neoplastic tissues negative."
    }
  },
  "2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)": {
    "Test Set Sample Size": "100 cases of breast cancer for the comparison with SBA/DCC. 76 normal tissues and 19 neoplastic tissues for tissue specificity.",
    "Data Provenance": "Retrospective study using archived tissue samples embedded in paraffin blocks from one investigative site. The country of origin is not specified, but the review is for a US FDA submission, implying the data is likely US-based or accepted for US regulatory purposes."
  },
  "3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)": {
    "Number of Experts": "A \"qualified pathologist\" evaluated the staining patterns for tissue specificity. It is stated that \"The pathologist scoring the IHC was masked to the results of the ER SBA/DCC\" for the validation study, implying at least one pathologist was involved in the scoring for the main validation study.",
    "Qualifications of Experts": "Described as a \"qualified pathologist\" in the tissue specificity section. No further details on years of experience or specific subspecialty are provided."
  },
  "4. Adjudication method (e.g. 2+1, 3+1, none) for the test set": "The document describes a \"Discrepancy Resolution\" method: \"Because SBA/DCC cannot distinguish between positivity of pathological and benign elements, the protocol allowed for the resolution of false negative ER IHC to true negative if benign elements were positive and pathological elements did not stain.\" This suggests a form of adjudication or re-evaluation based on specific criteria, but not a typical multi-reader consensus model (e.g., 2+1). The primary comparison was against SBA/DCC results, with IHC scores being used to refine or resolve discrepancies by considering tissue elements.",
  "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. This device is an immunohistochemistry (IHC) antibody test, not an AI-powered diagnostic tool, and therefore, the concept of human readers improving with AI assistance is not applicable.",
  "6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done": "Yes, a standalone performance study was done for the device (the antibody itself). The performance characteristics (sensitivity, specificity, agreement) were evaluated by comparing the IHC staining results, read by a pathologist, against a chemical cytosol receptor assay (SBA/DCC). While a pathologist reads the stained slides, the 'device' being cleared is the antibody and its staining method, and its performance is evaluated in this 'standalone' sense against an established reference.",
  "7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)": "The primary ground truth for the validation study was the results from an \"FDA approved chemical cytosol receptor assay for ER, serum binding assay/ dextran coated charcoal (SBA/DCC)\". For tissue specificity, the ground truth was established by a qualified pathologist's interpretation of staining patterns.",
  "8. The sample size for the training set": "The document does not explicitly mention a separate 'training set' or its sample size for the development of the antibody or in the context of machine learning. The study described is a clinical validation study of a pre-developed antibody. If 'training' refers to the optimization of the staining protocol, it's not quantified in terms of a specific sample size for a training set.",
  "9. How the ground truth for the training set was established": "Not applicable as a distinct 'training set' with ground truth establishment in the context of device development or machine learning is not described. The validation study itself uses SBA/DCC as the reference standard."
}

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