K Number
K133655
Device Name
RADIANCE V2
Date Cleared
2014-01-31

(65 days)

Product Code
Regulation Number
892.5050
Panel
RA
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

Radiance V2 is a software system intended for treatment planning and analysis of radiation therapy administered with devices suitable for intraoperative radiotherapy.

The treatment plans provide treatment unit set-up parameters and estimates of dose distributions expected during the proposed treatment, and may be used to administer treatments after review and approval by the intended user.

The system functionality can be configured based on user needs.

The intended users of Radiance V2 shall be clinically qualified radiation therapy staff trained in using the system.

Device Description

Radiance V2 is a treatment planning system, that is, a software program for planning and analysis of radiation therapy plans. Typically, a treatment plan is created by importing patient images obtained from a CT scanner, defining regions of interest either manually or semi-automatically, deciding on a treatment setup and objectives, optimizing the treatment parameters, comparing alternative plans to find the best compromise, computing the clinical dose distribution. approving the plan and exporting it.

AI/ML Overview

The provided text describes Radiance V2, a radiation treatment planning software, and its substantial equivalence to predicate devices, but it does not contain explicit acceptance criteria or a study proving that the device meets specific performance metrics.

However, it does mention validation and verification testing. Based on the available information, here's what can be extracted:

Acceptance Criteria and Device Performance

The document states that "Validation and Verification Testing carried out on the Radiance V2 indicates that it meets its predefined products requirements and requirements from the following product standards." However, the specific predefined product requirements (acceptance criteria) and the results showing how it met them (reported device performance) are not detailed in this summary.

The summary lists two standards that the device meets:

  • IEC 61217: Radiotherapy equipment - Coordinates, movements and scales
  • IEC 62083: Medical electrical equipment - Requirements for the safety of radiotherapy treatment planning systems

Without the specific product requirements, a table of acceptance criteria and reported performance cannot be generated directly from this document.

Study Information:

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

    • The document states: "The predecessor of Radiance V2 system, i.e., Radiance, has been tested clinically. This Clinical Study evaluated the effectiveness and repeatability of the planning process in IORT with Radiance in regard to the current modalities and the current uncertainties in regard to (manual) treatment planning."
    • No sample size for the test set is provided.
    • No data provenance (country of origin, retrospective/prospective) is provided.
  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):

    • This information is not provided in the document. The clinical study mentioned refers to the "planning process" in IORT and comparison to "manual treatment planning," suggesting expert involvement, but details are not given.
  3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

    • This information is not provided in the document.
  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:

    • An MRMC study per se is not explicitly mentioned. The clinical study cited for Radiance (the predecessor) "evaluated the effectiveness and repeatability of the planning process... in regard to the current modalities and the current uncertainties in regard to (manual) treatment planning." This implies a comparison, but it's not described as an MRMC study with AI assistance in the way typically understood for diagnostic AI. The document does not specify any effect size of human reader improvement with/without AI assistance.
  5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

    • The document focuses on "validation and verification testing" for the new features of Radiance V2 and states that the "computation algorithms and beam modeling tool" do not modify the basic functionality/workflow of the previous clinical study. This implies the core algorithms were tested, but whether a purely standalone performance evaluation (without any human review or interaction) was performed is not explicitly stated or detailed.
  6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

    • The document mentions the clinical study "evaluated the effectiveness and repeatability of the planning process... in regard to the current modalities and the current uncertainties in regard to (manual) treatment planning." This suggests that the "ground truth" for the predecessor's performance comparison was likely based on current clinical practice and manual treatment planning outcomes/judgments, but the specific definition (e.g., expert consensus on dose distribution accuracy, actual patient outcomes) is not detailed.
  7. The sample size for the training set:

    • The document does not mention a separate training set or its sample size. The device is a "radiation treatment planning software" and the focus is on validation and verification against engineering requirements and a prior clinical study. This type of device typically uses physical models and measured data for beam modeling and dose calculation algorithm development, rather than a "training set" in the machine learning sense.
  8. How the ground truth for the training set was established:

    • As no training set is explicitly discussed in the context of machine learning, this information is not applicable/provided. The "ground truth" for dose calculation algorithms is usually established through highly accurate physical measurements (e.g., in water phantoms) and/or sophisticated Monte Carlo simulations. The document mentions "Beam modeling of the treatment unit based on relative measurements and output factors" as a feature.

§ 892.5050 Medical charged-particle radiation therapy system.

(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.