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Oncentra is a radiation therapy planning software designed to analyze and plan radiation treatments in three dimensions for the purpose of treating patients with cancer. The treatment plans provide estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by qualified medical personnel.

Oncentra Brachy 4.4 is a radiation therapy treatment planning software designed to analyze and plan radiation treatments in three dimensions for the purpose of treating patients with cancer. The treatment plans provide estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by qualified medical personnel.

Oncentra Brachy 4.4 is a brachytherapy treatment planning only version of the Oncentra treatment planning system (Oncentra 4.2- K121448) and does not include Oncentra external beam treatment planning capabilities. Oncentra Brachy 4.4 includes all Oncentra software that is required for brachytherapy treatment planning which includes

• Anatomy Module: The Anatomy Modeling (AM) module is an advanced contouring package for defining structures (ROIs) related to the patient anatomy and target volumes for treatment planning. The AM allows the user to create and edit image registration between image series so that image fusion tools can be utilized.
• Connectivity Module: The Connectivity Module (CM) module handles all forms of DICOM data input to the Oncentra Brachy system from external sources, and data output from the system to external sources.
• Brachy Planning Module: The Brachy Planning (BP) module handles execution of Brachytherapy dose calculations. The software allows reconstruction of the implant from external images, identification of the radioactive sources, optimization methods, displayed dose distributions and output of treatment times.
• Evaluator Module: The Oncentra Evaluator (EVAL) handles the necessary tools for evaluating and comparing multiple treatment plans and dose summation of two or more plans for a selected case. The EVAL is a read-only activity with the exception of the plan approval function.
• Volume Rendering: The Volume Rendering (VR) module handles visualization of plans and their corresponding dose in 3D. No plan related data is modified in this module.
• Collapsed Cone Algorithm (ACE): Is a model based dose calculation that has been implemented in accordance with the AAPM TG-186 recommendations for brachytherapy uniformity. This algorithm takes into account the effects of tissue heterogeneities (normal tissue, air and bone), shields and applicators within finite patient dimensions.

The provided document is a 510(k) summary for a medical device (Oncentra Brachy 4.4), which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study with acceptance criteria and a comprehensive study report in the way a clinical trial might. It describes non-clinical and clinical "testing" at a high level.

Here's an attempt to extract and present the information based on the typical requirements for acceptance criteria and study descriptions, interpreting "testing" as "study" where appropriate:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed device performance metrics in a tabular format as one would expect from a rigorous clinical study. The "acceptance criteria" are implied by the summary statements regarding verification, validation, and clinical agreement.

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

• Test Set Sample Size: Not explicitly stated. The document refers to "test cases for each requirement, identified risk, clinical scenarios and usability issues" for non-clinical testing and "All sites" for clinical testing. No specific number of cases or patients is provided for either.
• Data Provenance: The document does not specify the country of origin for any data. Given that Nucletron B.V. is based in The Netherlands, it is possible some data could originate from there, but this is not confirmed. The nature of the studies (retrospective or prospective) is not explicitly stated. The "clinical scenarios" could imply retrospective data or simulated prospective cases.

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

• Number of Experts: Not specified. The document mentions "All sites were in agreement," implying multiple clinical sites and potentially multiple clinicians/experts, but no specific number or roles are provided.
• Qualifications of Experts: Not specified beyond the implication that they would be "qualified medical personnel" as mentioned in the "Indications for Use" for reviewing and approving treatment plans. There is no mention of their specific experience (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

Not specified. The statement "All sites were in agreement" suggests a consensus was reached, but the process (e.g., 2+1, 3+1, none) is not detailed.

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

• Was it done? No. The document does not describe a comparative effectiveness study involving human readers with and without AI (or in this case, the new calculation algorithm). The study focuses on verifying the software's functionality and alignment with clinical requirements and user expectations.
• Effect size of human readers with AI vs. without AI assistance: Not applicable, as no such study was performed or reported. The device itself is a planning software, not an AI assistance tool for human reading in the typical sense. The "AI" aspect here refers to the advanced dose calculation algorithm (ACE), not an interpretative AI.

6. Standalone (Algorithm Only) Performance Study

• Was it done? Yes, implicitly for the dose calculation algorithm. The document states, "A collapsed cone algorithm has been implemented to meet the AAPM TG 186 recommendations for brachytherapy uniformity; ACE (Advanced Dose calculation Engine) accounts for tissue heterogeneities, backscatter, attenuation, shielding and provides the user with a comparison of AAPM TG-43 and AAPM TG-186 based calculations." This suggests the algorithm's performance was evaluated against the AAPM TG 186 recommendations, which are a standard for standalone dose calculation accuracy. The non-clinical verification and validation would have assessed the algorithm's output independently.

7. Type of Ground Truth Used

• Non-clinical testing: Likely based on simulated data, phantom data, and known input-output relationships for software function verification and dose calculation accuracy based on physics principles and established standards (like AAPM TG 186).
• Clinical testing: "Clinical requirements and user expectations" likely imply a subjective assessment of the reasonableness and utility of the treatment plans generated by the software when applied to real or realistic patient cases, with "agreement" among sites/experts serving as a form of expert consensus or validation against clinical practice. It doesn't appear to be based on pathology, or long-term patient outcomes data.

8. Sample Size for the Training Set

Not applicable. This device is a treatment planning software that implements a specific dose calculation algorithm (ACE/AAPM TG 186). It is not described as a machine learning or AI-driven diagnostic system that typically requires a "training set" in the context of supervised learning. The algorithm is based on established physics models.

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

Not applicable, as there is no mention of a training set for a machine learning model. The algorithm's "ground truth" would be the underlying physics principles and the AAPM TG 186 recommendations it was designed to adhere to.

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The Oncentra system is a radiation treatment planning software designed to analyze and plan radiation treatments in three dimensions for the purpose of treating patients with cancer. The treatment plans provide estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by qualified medical personnel.

The Oncentra system is a radiation treatment planning software designed to analyze and plan radiation treatments in three dimensions for the purpose of treating patients with cancer. The treatment plans provide estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by qualified medical personnel. The system is intended for use by qualified medical personnel in radiotherapy clinics, suitably trained by Nucletron staff (or other competent people) in using the system. The Oncentra system should be configured locally and maintained by radiation physicists. It is a requirement that the person responsible for the local configuration has been suitably trained in configuring and maintaining the system. Oncentra 4.2 uses externally acquired medical images and user input. The Oncentra 4.2 software is based on a modular client/server design, with the treatment planning functions divided into "Activities".

This document describes the Oncentra 4.2 radiation treatment planning software. However, the provided text does not contain sufficiently detailed information about "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the format requested.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Oncentra MasterPlan 3.1, K081281) for regulatory purposes. It mentions "dosimetric validation" for brachytherapy and external beam calculations, which are relevant to performance, but it does not specify quantitative acceptance criteria or a formal study design that would typically be expected for a detailed evaluation of device performance against specific targets.

Here's an analysis based on the available information, highlighting what is and is not present:

Missing Information:
The document does not provide:

• A table of quantitative acceptance criteria with corresponding device performance metrics.
• Sample sizes for a dedicated test set.
• Data provenance for a test set (country of origin, retrospective/prospective).
• Number of experts or their qualifications for establishing ground truth for a test set.
• Adjudication method for a test set.
• Any information about a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or human reader improvement with AI assistance.
• Details about a standalone (algorithm only) performance study against specific, pre-defined metrics.
• The sample size for a training set (as this is not an AI/ML device in the modern sense, but rather a treatment planning software).
• How the ground truth for a training set was established.

Information on "Acceptance Criteria" and "Study" (based on interpretation of "Dosimetric Validation"):

The closest the document comes to describing an "acceptance criterion" and a "study" is in sections 5.1 and 5.2 regarding Dosimetric Validation.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated as a "test set" in the context of a formal study with specific case counts. The validation refers to comparisons (with other software, phantom measurements, and published data).
• Data Provenance: Not specified for a particular test set. The validation is internal ("evaluated by the physics team").

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

• Experts: "Evaluated by the physics team." Specific number and qualifications are not detailed beyond "physics team."
• Qualifications: Not specified.

4. Adjudication method for the test set:

• Not described.

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 MRMC comparative effectiveness study was done as this is a radiation treatment planning software, not an AI diagnostic aid for human readers. The document predates widespread discussion of "human readers improve with AI."

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

• The "dosimetric validation" described for both brachytherapy and external beam calculations focuses on the algorithms' output (dose calculations). This can be considered a standalone assessment of the algorithms' accuracy in generating dose distributions. However, it's not presented as a standalone study against a defined ground truth in the way a modern AI/ML device might be evaluated for a specific diagnostic task. It's more of a verification of calculation consistency and agreement with established methods.

7. The type of ground truth used:

• Brachytherapy: Comparisons were made against:
  • Results from the Plato version 14.3.5 planning system (another dose calculation algorithm).
  • Phantom measurements (physical validation data).
  • Data published in the TG-43 report (consensus-based physics model for brachytherapy dose calculations).
• External Beam: Comparisons were made against results from earlier versions of Oncentra (verifying consistency, implying the previous version's calculations served as a reference).

8. The sample size for the training set:

• This is not an AI/ML device in the context of machine learning training data. Therefore, a "training set" in that sense is not applicable or discussed. The software is based on physics models and algorithms, not data-driven machine learning.

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

• Not applicable for the reasons stated above.

In summary, the provided document offers a high-level overview of dosimetric validation performed to support the substantial equivalence claim for Oncentra 4.2. It emphasizes consistency with prior versions and established methods, rather than presenting a detailed performance study against explicit quantitative acceptance criteria for a novel AI/ML device.

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