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DCM is a three-dimensional radiotherapy dose engine for radiation dose planning of patients undergoing external beam treatment in the oncology clinic.

Based on quality assured radiation therapy input data Dose Calculation Module is used to plan radiation treatment with:

• Linear accelerators with X-ray energies from 4 to 50MV and electron energies from 6 . to 25 MeV
• Cobalt-60 units .

DCM will calculate dose for 3D radiotherapy treatment approaches of combined modality plans, asymmetric and non-coplanar fields; total body irradiation; multi-leaf collimators;

motorized and dynamic wedges; customized blocking and bolus.

DCM 2.0 is intended for calculations of dose plans, the calculations are based on DICOM objects and other information conveyed by a control communication protocol from the THERAPLAN treatment planning system.

DCM is a three-dimensional radiotherapy dose engine for radiation dose planning of patients undergoing external beam treatment in the oncology clinic.

Based on quality assured radiation therapy input data Dose Calculation Module (DCM) is used to plan radiation treatment with:

• Linear accelerators with X-ray energies from 4 to 50MV and electron energies from 6 . to 25 MeV
• . Cobalt-60 units

DCM will calculate dose for 3D radiotherapy treatment approaches of combined modality plans, asymmetric and non-coplanar fields; total body irradiation; multi-leaf collimators; motorized and dynamic wedges; customized blocking and bolus.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study conducted for the DCM 2.0 device:

Important Note: The provided text is a 510(k) summary and FDA clearance letter. It describes the device, its intended use, and its substantial equivalence to predicate devices, but does not contain detailed information about specific acceptance criteria or a comprehensive study report as would be found in a clinical trial or validation study. The FDA clearance process for this type of device typically relies on demonstrating substantial equivalence to a legally marketed predicate, often through performance testing that is not fully detailed in this summary. Therefore, many of the requested fields below will indicate "Not explicitly stated in the provided text."

Acceptance Criteria and Device Performance

Study Details

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • Sample Size: Not explicitly stated in the provided text. The submission focuses on substantial equivalence based on technological characteristics and intended use.
   • Data Provenance: Not explicitly stated.
   • Retrospective or Prospective: Not explicitly stated.

2. 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):

   • Not explicitly stated in the provided text. The ground truth for dose calculation systems is typically against established physics models, experimental measurements, or other gold-standard dose calculation software, rather than expert human interpretation in the same way an image analysis AI would be evaluated.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable/explicitly stated. This type of adjudication is typically for subjective assessments (e.g., image interpretation), not direct dose calculation comparisons.

4. 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:

   • Not applicable. DCM 2.0 is a dose calculation system, not an AI-assisted diagnostic tool that humans interact with for interpretation.

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

   • Yes, implicitly. As a dose calculation engine, DCM 2.0's primary function is to perform calculations independently based on input parameters. The validation for such a device would focus on the accuracy of these calculations compared to established standards (e.g., experimental measurements, other validated physics models). The text states it uses the Voxel Monte Carlo (VCM++) algorithm for electron calculations, indicating an algorithmic, standalone function.

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

   • Not explicitly stated, but for a dose calculation engine, the ground truth would typically be established through:
     • Physics-based models: Comparison to highly accurate physics simulations (e.g., full Monte Carlo simulations).
     • Experimental measurements: Physical measurements using phantoms and dosimeters (e.g., ion chamber, film, TLDs) in controlled environments.
     • Comparison to predicate device: Implicit in the 510(k) process, the device's calculations are compared to those of the legally marketed predicate device (DCM 1.0 and Helax-TMS v 5.1).

7. The sample size for the training set:

   • Not applicable/explicitly stated. Dose calculation algorithms like Voxel Monte Carlo are typically based on deterministic physics principles or experimentally derived beam models, rather than machine learning models that require "training sets" in the conventional sense. While beam models might be "tuned" or "configured" using experimental data, this is different from a machine learning training set.

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

   • Not applicable, as a training set in the machine learning sense is not described or implied for this type of device. The "ground truth" for developing the underlying Voxel Monte Carlo algorithm and electron beam model would be physical measurements and established physics principles.

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