(142 days)
ROCS TPS (Radiation Therapy Treatment Planning System) is intended to be used for the computation, display, evaluation and output documentation of radiation dose estimations to be submitted for independent clinical review and judgment prior to use. The device provides output data in the form of displays, hardcopy prints and/or plots to guide a physician in selecting the optimum patient treatment plan. It is intended to provide a report to be used by a competent health professional such as a radiation oncologist, medical physicist, radiation therapist or dosimetrist
ROCS treatment planning system is a collection of software modules that execute well known and documented algorithms to produce radiation dose estimations. All data is user controlled and is in a table look-up format. Information is presented graphically on CRT screens and in hardcopy reports. Various models are available based upon the specific features desired by the customer (e.g., asymmetric jaws, electron pencil beam calculations, etc.) to best meet their clinical needs. The software is designed to run on a PC platform utilizing the Microsoft® Windows NT® operating system. ROCS treatment planning system has been designed to be upgradable in both software features and hardware. All dates are four digit numbers so the system is able to handle the year 2000.
The provided text is a 510(k) summary for the ROCS TPS (Radiation Oncology Computer Systems Treatment Planning System). It primarily focuses on describing the device, its intended use, and its substantial equivalence to a predicate device.
Unfortunately, the provided document does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria.
Here's a breakdown of why this information is missing and what is available:
- No Acceptance Criteria or Performance Study: The document is a 510(k) summary, which is typically a premarket submission to demonstrate that a device is "substantially equivalent" to a legally marketed predicate device. For devices like a Radiation Therapy Treatment Planning System (TPS), substantial equivalence is often established through detailed comparisons of technological characteristics, safety, and effectiveness. This usually involves:
- Comparison to a Predicate Device: The document explicitly names
Radiation Oncology Computer Systems Treatment Planning System(K862643) as the predicate device and states that ROCS TPS uses "well known and documented algorithms." - Verification and Validation (V&V): While not explicitly detailed in this summary, a full 510(k) submission would include V&V documentation demonstrating that the software performs as intended. This might involve testing against known physics models, phantom data, or clinical cases, but the results and acceptance criteria are not summarized here.
- Clinical Performance Studies: For a TPS, clinical performance is often assessed through accuracy of dose calculations against established benchmarks or phantom measurements, not necessarily through human reader studies in the same way an imaging AI algorithm would be. The document focuses on the tool's ability to consistently compute and display dose estimations for independent clinical review.
- Comparison to a Predicate Device: The document explicitly names
Therefore, I cannot populate the requested table or answer most of the questions about a performance study using the provided text.
Here's an overview of what I can extract and what remains unanswered based on your query:
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified in the document. | Not specified in the document. |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
- Not specified. The document does not describe a performance study or test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. No test set or ground truth establishment process is described.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not applicable. No test set is described.
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. The document does not describe an MRMC study. This device is a treatment planning system, not an AI for image interpretation that would typically involve human readers improving with or without AI assistance in the context of disease detection or diagnosis.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not explicitly described as a formal "standalone study" in this summary. However, the device's function is inherently "standalone" in calculating dose estimations. Its output is then subject to "independent clinical review and judgment prior to use." The document emphasizes the system "does not provide direct control over any treatment delivery device" and "only provides output data...to guide a physician." This suggests the algorithm's output is the primary function, but the validation of its accuracy (which would be the standalone performance) is not detailed.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not specified. The document does not describe the specific ground truth used for validating the accuracy of its dose estimations. For a TPS, ground truth would typically come from physical measurements (dosimetry, phantom studies) or established theoretical models.
8. The sample size for the training set:
- Not applicable. The document describes a system with "well known and documented algorithms" and "table look-up format." This indicates a deterministic system based on physics models and pre-defined data, rather than a machine learning model that requires a "training set" in the conventional sense.
9. How the ground truth for the training set was established:
- Not applicable. As above, this is not a machine learning system with a training set. The "ground truth" for the algorithms would be derived from fundamental principles of radiation physics and validated experimental data.
§ 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.