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510(k) Data Aggregation
(176 days)
ITP, MODEL 1.0
PLATO ITP is intended to optimize multi-leaf collimator (MLC) positions or partial attenuation block shapes for intensity modulated external beam radiation therapy (IMRT) prior to final dosimetry planning on PLATO RTS external beam planning.
Once the optimization is complete, the dose distribution and dose-volume histogram curves are displayed for the user to evaluate. The optimized plan can be saved for final dose calculation and plan output using PLATO RTS.
PLATO ITP as described in this submission is a software package which is used to optimize multi-leaf collimator (MLC) positions or partial attenuation block shapes for intensity modulated external beam radiation therapy (IMRT). ITP is installed and runs on a PLATO radiation therapy planning system workstation.
Nucletron's PLATO ITP software uses previously defined anatomical structures and beams from the PLATO database to construct a 3D patient model used in the optimization process. The 3D patient model is based on CT images and is displayed in single or multiple image windows. The software also uses the treatment machine beam data from the PLATO radiation therapy planning system database.
The user defines the desired dose to be delivered to the target and the maximum dose to be delivered to the surrounding structures. Priority values are entered to weight the optimization calculations according to the importance of reaching the dose objectives for the target and other structures. After the user starts the optimization, the software calculates the required MLC or partial attenuation block shapes needed to achieve the dose objectives. This is done for each beam simultaneously and the resulting dose distribution and DVH are displayed in real-time. Once the optimization is complete, the dose distribution and DVH curves are displayed for the user to evaluate. If the user is not satisfied with the results of the optimization, the input parameters can be modified (dose constraints and priority values) and the optimization repeated.
The optimized plan can be saved for final dose calculation using PLATO RTS. Plans that have not been recalculated using PLATO radiation therapy planning system cannot be used for patient treatment. After final dose calculation thereforming system cannot be exported to a DICOM RT Plan compatible system for treatment delivery.
The provided text does not contain specific acceptance criteria or an explicit study describing how the Nucletron Inverse Treatment Planning (ITP) device meets such criteria. The document is a 510(k) summary for premarket notification, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance study results against predefined acceptance criteria.
However, based on the information provided, we can infer some aspects related to the device's intended function and the general approach to its evaluation for regulatory clearance. Since detailed study data is absent, the following table and subsequent sections highlight what would typically be expected in such a study, along with what can be partially inferred from the provided text.
1. Table of Acceptance Criteria and Reported Device Performance
As specific numerical acceptance criteria and corresponding performance metrics are not explicitly stated in the document, this table outlines the implied functional acceptance criteria based on the device's description and its intended use, and the reported device performance (as inferred from the 510(k) summary).
| Acceptance Criteria (Inferred from Intended Use) | Reported Device Performance (Inferred from 510(k) Submission) |
|---|---|
| Accuracy of Optimization: Ability to calculate MLC/block shapes to achieve user-defined dose objectives for targets and maximum dose to surrounding structures. | "After the user starts the optimization, the software calculates the required MLC or partial attenuation block shapes needed to achieve the dose objectives." "This is done for each beam simultaneously and the resulting dose distribution and DVH are displayed in real-time." |
| Implied Performance: The system successfully computes MLC/block shapes that correspond to the user's dose objectives. | |
| Consistency with User Input: Capability to modify input parameters (dose constraints, priority values) and re-optimize. | "If the user is not satisfied with the results of the optimization, the input parameters can be modified (dose constraints and priority values) and the optimization repeated." |
| Implied Performance: The system responds to user input modifications by re-optimizing the plan accordingly. | |
| Integration with PLATO RTS: Ability to save optimized plans for final dose calculation and export. | "The optimized plan can be saved for final dose calculation using PLATO RTS." "After final dose calculation thereforming system cannot be exported to a DICOM RT Plan compatible system for treatment delivery." |
| Implied Performance: The system successfully integrates with PLATO RTS for subsequent steps and supports DICOM RT output. | |
| Substantial Equivalence: Functional equivalence to predicate devices (Nomos Peacock Plan, GE Target Series 2, Nucletron PLATO RTS) for IMRT optimization. | "The PLATO ITP software is substantially equivalent to the predicate devices." (K940663, K841997, K964206). |
| Implied Performance: The device performs the core function of IMRT optimization in a manner comparable to the established predicate devices. |
2. Sample Size Used for the Test Set and Data Provenance
The provided text does not specify a sample size for any test set or the data provenance (e.g., country of origin, retrospective or prospective). The 510(k) summary primarily focuses on the device's description and its substantial equivalence to predicate devices. For a software device like this, testing typically involves various phantom cases and potentially clinical cases, but these details are not present.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The provided text does not mention the use of experts to establish ground truth for a test set, nor does it specify their number or qualifications. In the context of a treatment planning system, ground truth might involve comparing the system's output (dose distributions, fluences) against established physics principles, dose measurement phantoms, or expert-generated plans. However, these details are absent.
4. Adjudication Method for the Test Set
The provided text does not describe any adjudication method for a test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
The provided text does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done, nor does it mention any effect size for human readers improving with or without AI assistance. This type of study is more common for diagnostic imaging AI rather than a treatment planning optimization tool like PLATO ITP.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The 510(k) summary does not explicitly state if a standalone performance evaluation of the algorithm was conducted. However, given that it's a treatment planning optimization software, its core function is to generate optimal MLC/block shapes based on user-defined objectives. The "user evaluates" the results and can "modify input parameters," indicating that human interaction is an inherent part of its intended use. A standalone evaluation would likely involve analyzing the conformity of the generated plans to dose constraints on a set of reference cases, which is implicitly part of demonstrating its functionality.
7. The Type of Ground Truth Used
The provided text does not explicitly state the type of ground truth used for any testing. For a radiation therapy planning system, ground truth could involve:
- Physics-based calculations: Comparison of the optimized dose distributions against known physical models and algorithms.
- Phantom measurements: Verifying dose distributions with physical phantoms and dosimeters.
- Clinical expert consensus: In the absence of a "true" physical optimum, expert-generated ideal plans might serve as a benchmark.
The substantial equivalence claim suggests that the device's output and functionality were deemed comparable to systems already cleared, implying their outputs served as an indirect form of ground truth or benchmark for functionality.
8. The Sample Size for the Training Set
The provided text does not mention a training set sample size. PLATO ITP is described as a software package that "uses previously defined anatomical structures and beams from the PLATO database" and "treatment machine beam data from the PLATO radiation therapy planning system database." This suggests a rule-based or optimization algorithm that relies on pre-existing data and physics models rather than a machine learning model that requires a dedicated "training set" in the modern AI sense.
9. How the Ground Truth for the Training Set Was Established
Since the document does not indicate the existence of a "training set" in the context of machine learning, it does not describe how ground truth for such a set was established. The system's functionality is based on physics algorithms and optimization techniques, not on learning from labeled data in the way an AI diagnostic tool would.
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