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Radiance V5 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 V5 shall be clinically qualified radiation therapy staff trained in using the system.

Radiance V5 is a treatment simulation tool with faster Monte-Carlo simulation, multi-modal planning based on modern imaging standard for intraoperative radiotherapy (IORT). 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 , 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.

The provided text, a 510(k) summary for the Radiance V5 device, does not contain detailed information about the specific acceptance criteria and the study proving the device meets those criteria, particularly in the context of performance metrics like accuracy, sensitivity, or specificity.

Instead, the document focuses on:

• Substantial Equivalence: Arguing that Radiance V5 is substantially equivalent to its predicate device (Radiance V4, K171885).
• Technological Characteristics: Highlighting similarities in functionality and workflow, and noting improvements like faster Monte-Carlo simulation (GPU-based Hybrid Monte Carlo) and a redesigned UI.
• Non-Clinical Data: Stating that validation and verification testing indicated the device meets predefined product requirements and standards (IEC 61217, IEC 62083, IEC 62304, IEC 62366).
• Clinical Data (Limited): Referencing a clinical study performed for an even earlier predecessor (Radiance, K112060) to evaluate effectiveness and repeatability of the planning process in IORT. It then claims this data is safely extrapolatable to V5 because the changes in V5 do not modify basic functionality or workflow.
• Software V&V: Stating conformance with IEC 62304 and FDA guidance for software.

Crucially, the document does not provide a table of acceptance criteria with corresponding performance results for Radiance V5 itself, nor does it describe a study specifically designed to prove Radiance V5 meets quantitative performance metrics like those typically required for AI/ML-based diagnostic or treatment optimization tools (e.g., AUC, sensitivity, specificity, or error rates compared to ground truth).

The "Performance Data" section vaguely mentions "predefined products requirements" and "validation and verification testing" for non-clinical data, but does not elaborate on what these requirements or results were.

Given the information provided in the document:

1. A table of acceptance criteria and the reported device performance:

• Acceptance Criteria: Not explicitly detailed in quantitative, verifiable metrics (e.g., specific thresholds for dose accuracy, planning time, or UI usability scores). The document mentions meeting "predefined product requirements" and requirements from standards like IEC 61217, IEC 62083, IEC 62304, and IEC 62366. These standards typically cover aspects like safety, software lifecycle processes, usability engineering, and equipment-specific requirements, but do not necessarily define specific performance thresholds for a treatment planning algorithm's output accuracy against a clinical ground truth.
• Reported Device Performance: The document highlights improvements in "speed of the Hybrid Monte Carlo calculations" as a key performance enhancement over previous versions, making "less precise calculations unnecessary." It also states that validation and verification testing was "carried out on Radiance V5 indicates that it meets its predefined products requirements." No specific numerical performance data (e.g., accuracy, precision) are reported for Radiance V5's core treatment planning capabilities.

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

• Test Set Sample Size: Not specified for Radiance V5. The document only references a clinical study for a predecessor (Radiance, K112060), but does not provide details about its sample size, data provenance (e.g., country of origin), or whether it was retrospective or prospective. It asserts that this prior study's data "can be safely extrapolated and is also valid for Radiance V5."

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

• Not specified. The document does not describe the establishment of a ground truth for performance testing of Radiance V5 or its direct predecessor (V4). The "clinical study" mentioned for the even earlier Radiance (K112060) is vaguely described as evaluating "effectiveness and repeatability of the planning process," but details on ground truth establishment from experts are absent.

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

• Not specified. This level of detail about test set design is not present in the document.

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 study is detailed for Radiance V5. The document focuses on the device as a standalone treatment planning software that assists clinicians, rather than an AI that augments human interpretation in a diagnostic context requiring MRMC studies (which are more common for AI-assisted diagnostic imaging interpretation). The "clinical study" mentioned for the predecessor evaluated the "effectiveness and repeatability of the planning process," not human reading improvement.

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

• The document implies that non-clinical validation and verification testing was done on the software itself to ensure it met "predefined product requirements" and standards. This would effectively be a standalone performance evaluation against defined specifications, but the specifics of what was measured and how (e.g., dose calculation accuracy compared to a gold standard physics model) are not provided. The comparison of Monte Carlo calculations in V5 to less precise calculations in prior versions suggests an internal performance metric, but not a direct standalone clinical performance evaluation against a human or true outcome.

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

• Not specified. For a treatment planning system, ground truth would typically relate to the accuracy of dose calculations compared to a physics model or measurements, or the clinical efficacy and safety of the plans generated. The document only refers to the predecessor's study evaluating "effectiveness and repeatability of the planning process" and "current uncertainties in regard to (manual) treatment planning," which suggests a comparison to manual methods but doesn't define a specific ground truth.

8. The sample size for the training set:

• Not applicable as described. Radiance V5 is described as a "software program for planning and analysis of radiation therapy plans" with a "three dimensional dosimetry engine" and "Hybrid Monte Carlo dose computations." It's not presented as an AI/ML model that would typically have a distinct "training set" of patient data in the modern supervised learning sense. While it's software that performs complex calculations, the term "AI" is not explicitly used, and its "engine" implies a deterministic algorithm rather than a learned model requiring a training dataset.

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

• Not applicable. As noted above, the device is not described as utilizing a training set in the AI/ML context. Its enhancements ("faster Monte-Carlo simulation," "redesigned user interface workflow," "multi-modal multi-image planning") appear to be engineering and algorithmic improvements rather than AI model training on a dataset with established ground truth.

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Radiance V4 is a software system intented 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 V4 shall be clinically qualified radiation therapy staff trained in using the system.

Radiance V4 is a planning tool for intraoperative radiotherapy (IORT), an application that provides a simulation of the dose distribution according to the parameters involved in the procedure. These parameters in IORT include the geometry of the applicator, its orientation and position with respect to the patient and IORT device parameters.

Radiance V4 is designed to analyze and plan radiation treatments in three dimensions for the purpose of treating patients with cancer. The user can adjust parameters to achieve a predicted outcome, rather than make a decision intra-operatively. The created 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.

This document describes the Radiance V4, a software system for radiation therapy treatment planning. The device received 510(k) clearance (K171885) on July 25, 2017, from the FDA.

Here's a breakdown of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state acceptance criteria in a quantitative format with corresponding reported device performance metrics. Instead, it describes a process of design verification testing to assure the changes were "appropriate, safe and effective for the intended use." The performance data is summarized as addressing the "modifications and the impact on performance and safety."

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

The document does not specify a "test set" in the context of patient data or clinical images. The testing described is "design verification testing" related to technical modifications of the software. Therefore, sample size and data provenance in terms of patient data are not applicable here.

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

This information is not provided. The study focuses on design verification and technical performance rather than clinical performance based on expert-established ground truth on patient data.

4. Adjudication Method for the Test Set

This information is not provided. As the testing is design verification, a clinical adjudication method would not be relevant.

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

No MRMC comparative effectiveness study is mentioned. The submission is for a treatment planning software system, and the focus is on the software's ability to accurately calculate dose distributions and incorporate new features, not on improving human reader performance in diagnosis.

6. Standalone Performance

The primary function of Radiance V4 is a standalone planning tool. The "design verification testing" would inherently assess the algorithm's performance in calculating dose distributions and implementing the new features. However, specific standalone performance metrics (e.g., accuracy of dose calculation compared to a gold standard) are not quantitatively reported in this summary.

7. Type of Ground Truth Used

The "ground truth" for the design verification testing would likely involve:

• Technical specifications and requirements: Ensuring the software behaves according to its designed functionality.
• Physics-based models and simulations: Verifying the accuracy of dose calculations based on established radiation physics.
• Comparisons to established physical measurements (e.g., phantom measurements for dose distribution, though not explicitly stated as being performed here).

The document mentions that the changes include "improved accuracy of the dose in the first millimeters to the applicator surface" and "calculation of water equivalent dose." These imply a comparison against a known, accurate dose distribution or calculation method.

8. Sample Size for the Training Set

No information about a "training set" is provided. As a treatment planning software, it's not a machine learning model that requires a labeled dataset for training in the conventional sense. The development would involve engineering and physics principles rather than AI training.

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.

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Radiance V3 is a software system intented 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 V3 shall be clinically qualified radiation therapy staff trained in using the system.

Radiance V3 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.

Radiance V3 improvements over Radiance V2 (K133655) are:

• A Hybrid Monte Carlo dose computation algorithm for photons for the ● INTRABEAM.
• . A beam modeling tool which models and verifies the treatment unit model with measurements for the INTRABEAM.
• . An improved DICOM interface including PACS query&retrieve functionality. Storage SCP and DICOM.RT Structures and Dose exportation.

The list of compatible IOERT/IORT devices are:

• Intrabeam (a Carl Zeiss product) ●
• . NOVAC7 and NOVAC11 (a SIT product)
• . LIAC10 and LIAC12 (a SIT product)
• MOBETRON (an IntraOp Medical product)
• . Conventional LINACs with adapted cylindrical IOERT applicators (telescopic or fixed ones).

Radiance V3 has been tested with Elekta/Precise and Varian/21EX LINACs with particular IOERT cylindrical telescopic applicators.

Characteristics of radiance include:

• 1. Image manipulation and visualization
• 2. IORT applicator simulation
• 3. Contouring manual and interpolation tools
• 4. Dose calculation algorithms, including:
  • a. For Intrabeam, Dose Painting for a fast (a few seconds) interpolation of PDD around the applicator or Hybrid Monte Carlo for a good combination of computation time (a few minutes) and accuracy.
  • b. For IOERT, pencil beam for a fast (less than one minute) calculation of the dose or Monte Carlo for a good combination of computation time (between 1-10 minutes in most of the cases) and accuracy.
• 5. Reporting.
• 6. DICOM & DICOM.RT compatibility

Here's an analysis of the acceptance criteria and study information for the Radiance V3 device, based on the provided text:

Important Note: The provided document is a 510(k) summary, which often focuses on demonstrating substantial equivalence to a predicate device rather than presenting extensive de novo clinical trial data. Therefore, detailed performance metrics and statistical analyses typically found in full clinical study reports are not explicitly detailed here. The information below is extracted and inferred from the available text.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (e.g., specific accuracy thresholds for dose calculation). Instead, it focuses on verifying that the device meets its predefined product requirements and relevant industry standards. The performance is reported as meeting these requirements and passing various tests.

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

• Test Set Sample Size: The document does not specify a "test set" in terms of number of cases or patients. Instead, it refers to "Over 150 tests" that were executed, including verification tests for new functionality, risk mitigation, and regression tests. These tests involved simulated clinical workflows and algorithm testing with simulated setups. It's not clear if these 150+ tests refer to individual "cases" or different functional aspects.
• Data Provenance: The testing involved "simulated clinical workflows" and "algorithm testing using a simulated clinical setup." This implies that the data was not derived from real patient studies but rather from synthetic or phantom data to simulate various treatment scenarios. Therefore, there is no country of origin for real patient data, and it is a simulated/retrospective (in the sense of being pre-designed scenarios) evaluation rather than prospective clinical data.

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

The document does not provide information on the number of experts or their qualifications used to establish ground truth for the test set. Given that the testing involved "simulated clinical workflows" and "algorithm testing using a simulated clinical setup," the "ground truth" likely refers to established physical models, reference dose calculations (e.g., from more precise scientific methods or established phantoms), or mathematically derived correct outputs for the simulated scenarios.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method. Since the ground truth for the test set seems to be derived from physical/mathematical models or established reference calculations in simulated environments, a human adjudication process by multiple experts is not directly applicable in the same way it would be for interpreting medical images. The evaluation of results against the expected simulated outcomes would be done internally by the engineering and testing teams.

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

No MRMC comparative effectiveness study was done. The document explicitly states: "Clinical trials were not performed as part of the development of this product." Therefore, there is no information on the effect size of human readers improving with or without AI assistance, as human reader performance was not evaluated.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance evaluation was done. The entire "Performance Data" section (Section 7) describes verification and validation testing performed on the Radiance V3 system itself. This included:

• Validation and Verification Testing against predefined product requirements and industry standards.
• Algorithm testing to validate the accuracy of dose calculation functions using a simulated clinical setup.
• Over 150 tests executed for new functionality, risk mitigation, and regression.

This testing addresses the performance of the algorithm and software in a standalone capacity, without direct human-in-the-loop performance measurement.

7. Type of Ground Truth Used

The ground truth used was primarily based on:

• Predefined product requirements: The system's output was compared against its own specified functionalities and performance targets.
• Industry standards (IEC 61217, 62083, 62366): Compliance with these standards served as a form of ground truth for safety, design, and usability.
• Simulated clinical setups and reference dose calculations: For dose calculation accuracy, the "ground truth" would have been established by known physical principles, validated models, or highly accurate computational methods for the simulated scenarios, confirming whether the algorithm's output matched the expected physical reality.
• Predicate device comparison: The substantial equivalence argument relies on the Radiance V3 performing comparably or better than the predicate (Radiance V2), particularly with the new Hybrid Monte Carlo algorithm offering "a more accurate simulation."

8. Sample Size for the Training Set

The document does not provide information on the sample size for the training set. Treatment planning software like Radiance V3, while complex, typically relies on established physics models and algorithms rather than machine learning models that require explicit "training sets" in the sense of labeled data for supervised learning. The algorithms are built upon scientific principles and validated against known physical phenomena, not "trained" on a dataset of cases. Therefore, a training set in the typical AI/ML context is not applicable here.

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

As noted above, the concept of a "training set" and associated "ground truth" in the machine learning sense is not applicable to this type of physics-based treatment planning software. The algorithms are based on physics equations and models validated through established scientific methods and comparisons to physical measurements (e.g., beam modeling and output factors for INTRABEAM measurements mentioned in the "Beam modeling tool" section).

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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.

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.

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.

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