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I2C is used with a charged particle or photon radiation therapy system for localization of the patient position with respect to the therapy equipment and to provide correction feedback to the radiation therapy device.

For clinical use, l2C must be integrated into a radiation therapy system. I2C will interact with components of the radiation therapy center. I2C supports the acquisition of 2D, 2D stereoscopic and 3D images using 2D detectors. I2C will be used by the clinical therapist to verify by imaging that the treatment target position received from the treatment control applicative laver is 'valid', i.e. that it brings the center of the treatment target volume at the isocenter of the therapy equipment with required accuracy. If it is not, InC will propose a correction shift - or correction vector - that will be exported to the radiation therapy system.

Here's a summary of the acceptance criteria and study information for the I2C device:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a distinct "test set" sample size in the traditional sense. Instead, it describes various verification and validation activities:

• Simulated Clinical Environment: The X-Ray imaging equipment was installed on a test bench with a phantom to represent different configuration setups and simulate gantry rotation.
• Communication Testing: A second test environment was used to verify communication with different third-party software configurations (Elekta Mosaiq, Varian Aria).
• Additional Performance Tests: Conducted on a stand-alone system using:
  • Appropriate datasets collected from simulated treatments.
  • Radiographs of phantoms acquired in IBA treatment centers.
  • Anonymized patient data provided by IBA treatment centers.
• User Evaluation: Intermediate releases were distributed to a group of "a-users" (reference users in proton therapy) to assess usability.

The data provenance for the additional performance tests includes:

• Simulated treatments.
• Phantom data acquired in IBA treatment centers.
• Anonymized patient data (from IBA treatment centers).
  The document does not explicitly state the country of origin for the patient data, but "IBA treatment centers" suggests it likely comes from facilities where IBA technology is used. The data appears to be retrospective in nature for these tests, as it mentions "anonymised patient data provided by IBA treatment centers."

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

The document does not specify the number or qualifications of experts used to establish ground truth for the test set. The validation primarily relies on performance metrics derived from physical phantoms, simulated scenarios, and anonymized patient data.

4. Adjudication Method for the Test Set

No specific adjudication method (e.g., 2+1, 3+1) is mentioned for the test set. The evaluation seems to be based on direct measurement of performance metrics against predefined technological specifications and comparison to predicate devices, rather than a consensus-based expert review for individual cases.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study is not mentioned. The document focuses on the technical performance of the device itself and its equivalence to predicate devices, not on the improvement of human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, standalone performance tests were done. The document states: "Third, additional performance tests were done on a stand-alone system with appropriate datasets collected from simulated treatments and radiographs of phantom acquired in IBA treatment centres, and from anonymised patient data provided by IBA treatment centers."

7. The Type of Ground Truth Used

The ground truth used for these non-clinical tests appears to be primarily:

• Physical measurements/known values from phantoms: For accuracy, resolution, contrast, and uniformity tests.
• Simulated treatment parameters: For evaluating the device's ability to process and generate correction vectors in controlled scenarios.
• Anonymized patient data: Used as input for the standalone system, likely comparing its output (e.g., calculated shifts) against expected or clinically established values, though the exact method of ground truth for patient data isn't detailed.

8. The Sample Size for the Training Set

The document does not specify a separate "training set" or its sample size. The focus is on the verification and validation of the developed system, suggesting that the algorithm's training (if any involving machine learning) was either done prior to these V&V activities or is not detailed in this summary.

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

As no training set is explicitly mentioned or detailed, the method for establishing its ground truth is not provided in this document.

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