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ThinkQA Edition 2 software is used to verify that the dose distribution calculated by a treatment planning system for external beam radiation therapy is consistent with treatment plan parameters.

Based on read-in treatment plan data, ThinkQA Edition 2 re-calculates a dose distribution in a three-dimensional representation of a patient or a phantom and provides dose-volume indicators which compare it to the initial dose distribution calculated by the treatment planning system.

ThinkQA Edition 2 is not a treatment planning system. It is a Quality Assurance software only to be used by qualified and trained radiation therapy personnel.

ThinkQA Edition 2 is a standalone software device used within a radiation therapy clinic which is designed to perform secondary dose calculation based on DICOM RT treatment plan data provided by a treatment planning system.

ThinkQA Edition 2 is only meant for quality assurance purpose. It cannot define or transmit any instructions to a delivery device, nor does it control any other medical device.

ThinkQA Edition 2 performs dose calculation verifications for radiation therapy plans by doing an independent calculation of dose distribution in a three-dimensional representation of a phantom. Dose distribution is initially calculated by a treatment planning system which is a software tool that allows to define and transmit treatment plan parameters that will further be used for treatment delivery. Based on treatment plan parameters, ThinkQA Edition 2 re-calculates dose distributions using a proprietary Collapsed Cone Convolution algorithm. It uses CT images (real patient anatomy) to perform dose computation with Collapsed Cone Convolution.

ThinkQA Edition 2 compares the reference TPS dose distribution with its own calculation using specific indicators such as 3D gamma agreement index on significant volumes. ThinkQA Edition 2 computes Gamma Passing Rate for automatic dose areas and anatomical structures: Planning Target Volumes (PTVs) and Organs at Risk (OARs).

Based on these indicators, ThinkQA Edition 2 displays a pass/fail status that informs the user whether or not the acceptance criteria that he has defined are met. The acceptance criteria does not give in any way information that could be used to determine whether or not the treatment plan is clinically relevant. It just evaluates the consistency between treatment plan parameters and the dose distribution computed by the TPS.

ThinkQA Edition 2 has been designed to be compatible with radiotherapy adaptative workflows. This includes a number of mandatory features:

• User interface design, grouping verifications for adaptive plans under a single primary plan verification; .
• . Automatic computation upon reception of DICOM data from the TPS;
• Sufficient speed of computation, compatible with adaptive workflow with patient waiting on couch.

The performance of ThinkQA Edition 2 makes it suitable for the following photon treatment delivery techniques: Static beams, IMRT Step & Shoot, Dynamic IMRT with fixed gantry and Rotational IMRT (VMAT).

In order to guaranty the independence of the secondary dose check, the beam models are not intended to be adjusted to match the user's reference TPS. The user only provides its actual measured dose rate in reference conditions and HU-density conversion table.

ThinkQA Edition 2 runs on workstations or virtual machines with Linux CentOS 7 operating system. Its web interface is accessible from any system supporting the specified in chapter ThinkQA Edition 2 web application. ThinkQA Edition 2 is able to communicate with other equipment installed on the network complying with the DICOM and DICOM RT industry standards.

The FDA 510(k) summary for ThinkQA (Edition 2) describes a software device for quality assurance in radiation therapy. The document outlines comparisons to a predicate device (MU2net) and evidence for substantial equivalence, including performance evaluations.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a formal "acceptance criteria" table with specific quantitative thresholds that the device had to meet for its performance evaluation, nor does it provide detailed numerical outputs beyond qualitative statements. However, it implicitly defines a performance expectation related to dosimetric evaluation:

2. Sample Size for the Test Set and Data Provenance

• Test Set Sample Size: The document refers to "a large variety of plans" for the dosimetric evaluation, but a specific number is not provided. For the beam modeling unique to this submission, there were "three beam qualities (6 MV, 6 MV FFF and Elekta Unity 7 MV FFF) and two primary TPS (RayStation and Monaco)."
• Data Provenance: The document does not specify the country of origin for the data or whether the studies were retrospective or prospective. It implies the data was generated internally for testing and evaluation purposes. The "measured depth dose curves and profiles" suggest real-world or phantom measurements were performed.

3. Number of Experts and Qualifications

• Number of Experts: Not explicitly stated. The studies were likely conducted by the manufacturer's internal team, including physicists and engineers specialized in medical physics and radiation therapy.
• Qualifications of Experts: Not explicitly stated, but the context implies expertise in radiation oncology physics, treatment planning systems, and dose calculation algorithms ("qualified and trained radiation therapy personnel"). The mention of "AAPM working group 219" recommendations suggests adherence to professional standards in radiation oncology physics.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable in the traditional sense of human interpretation of results. The device's performance was evaluated against physical measurements and established dosimetric metrics (e.g., gamma index passing rate, dose difference). The comparison to the predicate device acted as a form of "adjudication" for decision consistency.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This device is a quality assurance software that performs an objective, mathematical comparison of dose distributions rather than aiding human readers in diagnosis or interpretation that would necessitate an MRMC study. Its purpose is to verify consistency based on defined parameters, not to improve human diagnostic performance.

6. Standalone (Algorithm Only) Performance

• Standalone Performance: Yes, the described performance evaluation (dosimetric evaluation, beam modeling) is a standalone assessment of the algorithm's ability to calculate dose distributions and compare them to reference data. The device's output (gamma passing rate, pass/fail status) is based solely on its internal calculations and comparisons, without human intervention in the calculation or the determination of the result itself. Human users then interpret this output.

7. Type of Ground Truth Used

• Ground Truth Type:
  • Measured Data: For beam modeling, the ground truth was "corresponding measured depth dose curves and profiles," indicating physical measurements.
  • Reference Treatment Planning System (TPS) Dose Distribution: For the clinical performance evaluation and comparison, the ground truth was implicitly the dose distribution calculated by the "reference TPS" (RayStation and Monaco), against which ThinkQA's calculations were compared using metrics like the gamma index.
  • Predicate Device Output: For consistency checks, the "decision" (validation or rejection) of the predicate device (MU2net) served as a comparative ground truth.

8. Sample Size for the Training Set

• Training Set Sample Size: The document does not explicitly discuss a separate "training set" in the context of a machine learning model, as the dose calculation for ThinkQA Edition 2 is based on a "proprietary Collapsed Cone Convolution algorithm" and beam models, rather than a data-driven machine learning approach that would necessitate a distinct training phase with labeled data in the same way. The beam modeling process involves systematic adjustments and evaluations, which could be considered an iterative tuning or "training" specific to dose calculation, but a specific "training set size" is not applicable in the typical AI/ML sense.

9. How Ground Truth for the Training Set Was Established

• Ground Truth for Training Set Establishment: Since the core dose calculation algorithm (Collapsed Cone Convolution) is a physics-based model, it does not rely on labeled training data in the way a machine learning algorithm would. The "beam modeling process" involved:
  • Comparison of computed depth dose curves and profiles against "measured depth dose curves and profiles." These physical measurements serve as the ground truth for calibrating and validating the accuracy of the beam models within the CCC algorithm for different beam qualities and TPS.
  • The goal was for the beam models to be independently accurate and not necessarily "adjusted to match the user's reference TPS" to maintain calculation independence.

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ThinkQA is a radiation therapy dosimetry Quality Assurance (QA) device consisting of a software framework intended to contain a suite of modules to verify that radiation dose actually delivered to the patient is as intended.

The Epibeam module contained in ThinkQA is intended to be used as follows:

Epibeam is a standalone software tool independent of the linear accelerator, the TPS and the Record-and-Verify system. It is intended to assist in reducing the clinical risk in the delivery of radiotherapy treatments and does not alter the treatment delivery. It is to be used by a radiation oncology medical professional as a guide to provide pretreatment plan delivery verification.

The software is to be used for the purposes of detecting errors in the delivery of radiation therapy prior to treatment, like corruption of the transferred plan data to the treatment unit, inappropriate multileaf collimator sequence or beam output malfunctioning. The software acquires data from the Electronic Portal Imaging Device (EPID) during a blank fraction dedicated to the pretreatment verification without the patient and subsequently processes it. The processed data is compared with data calculated by the Epibeam system. The comparison is derived on one hand, from the application of dose conversion to the EPID data and on the other hand, from the computation of a predicted dose image under ideal conditions of functioning. A gamma-index analysis is then performed according to the dose difference and distance-to-agreement criteria provided by the user.

Epibeam is not a treatment planning system and cannot be used to generate radiotherapy treatment plans. It provides an independent means of checking the reliability of the dose delivery for each beam in reference to TPS data.

Epibeam therefore provides an added level of treatment quality assurance, thus giving clinicians confidence especially when complex treatment techniques are employed (gantry-fixed and rotational intensity modulated radiation therapy).

Epibeam is intended to support decision making in relation to the delivery of treatment plan to the patient with every clinical linear accelerators equipped with an EPID, but does not alter the existing Indications for Use of the treatment unit.

ThinkQA is a modular software suite composed of the module Epibeam which is a quality assurance tool dedicated to Patient Specific QA for pretreatment verification of irradiation beams.

The EPIbeam verification module integrated to the ThinkOA software platform is a Quality Assurance tool in external beam radiation therapy, used in combination with the electronic portal imaging device (EPID) and dedicated to the irradiation beam pre-treatment verifications, particularly for IMRT and VMAT techniques.

EPIbeam principle is based on the comparison of two images expressed in terms of absolute dose: on the one hand, a RT Plan defined in the TPS is used for the acquisition of a real portal image (test image) with the EPID directly irradiated (without attenuating medium); on the same RT Plan is used to compute a theoretical portal image (reference image). Specific models and algorithms are applied to express both images in the same absolute dose terms.

The dose images obtained from the same RT Plan, one by the conversion model of the acquired raw EPID images and the other by the prediction model, can be quantitatively compared through dose difference mappings or 2D gamma-index. Both models are based on dosimetric data provided from the TPS.

Here's a summary of the acceptance criteria and study details for the ThinkQA Epibeam device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of numerical acceptance criteria. Instead, it describes functional requirements and states that "acceptance criteria were met." The performance is generally framed as demonstrating substantial equivalence to the predicate device.

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

The document does not specify a distinct "test set" with a particular sample size from real patient data. The validation seems to be based on "clinically representative conditions" and "test cases" rather than a specific patient cohort for a validation study.

• Sample Size for Test Set: Not explicitly stated as a separate patient-based test set. The testing involved "unit, integration and system tests" and "validation of the system under clinically representative conditions."
• Data Provenance: Not specified regarding country of origin or whether it was retrospective or prospective. Given the nature of a software release, it's likely synthetic or internally generated test cases reflecting various clinical scenarios, and potentially retrospective clinical data for "clinically representative conditions."

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

The document does not mention the use of external experts to establish a "ground truth" for a specific test set. The ground truth for the device's function appears to be established through:

• Comparison of acquired EPID data with data calculated by the Epibeam system itself, based on TPS plans and prediction models.
• The assumption that the TPS data and the device's prediction model represent the "ideal conditions of functioning" or "reference."

4. Adjudication Method for the Test Set

Not applicable/not mentioned. There's no indication of an adjudication method involving multiple human readers for establishing a ground truth or resolving discrepancies in a test set.

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

No, an MRMC comparative effectiveness study was not done. The document focuses on the standalone performance and substantial equivalence of the software tool.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

Yes, a standalone study was done. The entire premise of the "Performance Testing - Bench" section describes the testing of the ThinkQA software's functionalities and algorithms independently. The Epibeam module is described as a "standalone software tool independent of the linear accelerator, the TPS and the Record-and-Verify system." The performance testing demonstrates that the software itself "meets the requirements of the device."

7. Type of Ground Truth Used

The ground truth for the comparison performed by the Epibeam module is based on:

• Predicted dose image: Calculated by the Epibeam system under ideal conditions, derived from the RT Plan defined in the Treatment Planning System (TPS).
• Dose conversion of acquired EPID data: The software converts raw EPID images into dose terms.
• TPS data: The models and algorithms used by Epibeam are based on dosimetric data provided by the TPS, which serves as a reference for the planned dose.

Essentially, the "ground truth" for the device's internal comparison is the expected dose distribution as calculated by the validated Treatment Planning System and through the device's own prediction models.

8. Sample Size for the Training Set

The document does not explicitly mention a "training set" or its size. As a "software framework" and a "Quality Assurance tool," its development likely involved conventional software engineering practices, potentially including internal data for model development and calibration, but a specific "training set" like in deep learning models is not detailed.

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

Since a "training set" is not explicitly mentioned, the method for establishing its ground truth is also not described. The device's foundational data relies on the principles of radiation dosimetry and verified TPS data.

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