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syngo CT Oncology is a self-contained, non invasive image analysis software package designed to support the physician in routine diagnostic oncology, staging and follow-up. Flexible layouts and automated image registration facilitate the synchronous display and navigation of multiple datasets for viewing multi-phase CT data and easy follow-up comparison. The application provides a range of interactive tools specifically designed for segmentation and volumetric analysis of lesions and includes dedicated tools to support the physician in lung, liver and lymph node assessment in CT data. Dedicated workflowsupport and integrated, accumulative reporting help to track lesions and their changes in e.g. size, shape and enhancement pattern over time. syngo CT Oncology also facilitates functional imaging offering fusion with other modalities such as PET data. It also features syngo LungCAD for detecting small lung nodules (PMA-approved).

syngo CT Oncology is a self-contained, non invasive image analysis software package designed to fast-track routine diagnostic oncology, staging and follow-up. Flexible layouts and automated image registration facilitate the synchronous display and navigation of multiple datasets for viewing multi-phase CT data and easy follow-up comparison. The application provides a range of automated tools specifically designed to support physicians in the segmentation and volumetric evaluation of suspicious lesions including dedicated tools for lung, liver and lymph node assessment in CT data. Dedicated workflow-support and integrated, accumulative reporting allow to track lesions and their changes in e.g. size, shape and enhancement pattern over time. syngo CT Oncology also facilitates functional imaging offering fusion with other modalities such as PET data. It also features syngo LungCAD for detecting small lung nodules (PMA-approved).

Here's an analysis of the provided text regarding the acceptance criteria and study information for "syngo CT Oncology - Software Package":

Acceptance Criteria and Device Performance Study: syngo CT Oncology - Software Package (K071310)

The provided 510(k) summary for syngo CT Oncology (K071310) does not contain specific acceptance criteria or a detailed study designed to prove the device meets such criteria in terms of quantitative performance metrics (e.g., sensitivity, specificity, accuracy against a gold standard).

Instead, the submission primarily focuses on establishing substantial equivalence to existing legally marketed predicate devices. The "safety and effectiveness information" presented is geared towards demonstrating that the new device is as safe and effective as the predicate devices, rather than quantifying its performance against predefined benchmarks.

The document states:

• "The device labeling contains instructions for use and any necessary cautions and warnings, to provide for safe and effective use of the device."
• "Risk management is ensured via a hazard analysis, which is used to identify potential hazards. These potential hazards are controlled via software development, verification and validation testing."
• "To minimize electrical, mechanical, and radiation hazards, Siemens adheres to recognized and established industry practice and standards."

These statements describe the general approach to ensuring safety and effectiveness through good manufacturing practices, risk management, and adherence to standards, but they do not define specific performance-based acceptance criteria for the image analysis software's diagnostic capabilities.

Because the submission relies on substantial equivalence and general safety/effectiveness claims rather than a detailed performance study with defined criteria, much of the requested information cannot be extracted directly from the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided document, specific quantitative acceptance criteria and reported device performance (e.g., sensitivity, specificity, or volumetric accuracy against a ground truth) are NOT explicitly stated.

The document emphasizes general safety and effectiveness through risk management and adherence to standards. The substantial equivalence argument implies that the device performs at least as well as the predicate devices for its intended use, but no quantitative performance metrics are given for either the new device or the predicates in this summary.

• Instructions for safe and effective use
• Hazard analysis and risk control
• Adherence to industry practice and standards | None explicitly stated as quantitative performance metrics. The device is deemed "substantially equivalent" to predicate devices, implying comparable safety and effectiveness for its intended use. |

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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

• MRMC Study Done?: No, an MRMC comparative effectiveness study is not mentioned or described in this 510(k) summary for the syngo CT Oncology software package itself. The document mentions "syngo LungCAD for detecting small lung nodules (PMA-approved)", which might have undergone such studies separately under its PMA, but this 510(k) is for the broader CT Oncology package.
• Effect Size of Human Reader Improvement with AI vs. without AI Assistance: Not applicable, as no MRMC study for this specific device is described here.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study Done?: Not described. The submission focuses on the device as a "software package designed to support the physician" (human-in-the-loop) and establishing substantial equivalence rather than independent algorithmic performance.

7. Type of Ground Truth Used

• Type of Ground Truth: Not specified. Since no specific performance study is detailed, the method for establishing ground truth is not mentioned.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not specified. The document does not describe the development or training of an AI algorithm in terms of data sets.

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

• Ground Truth Establishment for Training Set: Not specified.

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