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Limbus Contour is a software-only medical device intended for use by trained radiation oncologists, dosimetrists and physicists to derive optimal contours for input to radiation treatment planning.

Supported image modalities are Computed Tomography and Magnetic Resonance. The Limbus Contour Software assists in the following scenarios:

Operates in conjunction with radiation treatment planning systems or DICOM viewing systems to load, save, and display medical images and contours for treatment evaluation and treatment planning.

Creation, transformation, and modification of contours for applications including, but not limited to: transferring contours to radiotherapy treatment planning systems, aiding adaptive therapy and archiving contours for patient follow-up.

Localization and definition of healthy anatomical structures.

Limbus Contour is not intended for use with digital mammography.

Limbus Contour is a stand-alone software medical device. It is a single purposes cross-platform application for automatic contouring (segmentation) of CT/MRI DICOM images via pre-trained and expert curated machine learning models. The software is intended to be used by trained medical professionals to derive contours for input to radiation treatment planning. The Limbus Contour software segments normal tissues using machine learning models and further post-processing on machine learning model prediction outputs. Limbus Contour does not display or store DICOM images and relies on existing radiotherapy treatment planning systems (TPS) and DICOM image viewers for display and modification of generated segmentations. Limbus Contour interfaces with the user's operating system (importing DICOM image .dcm files and exporting segmented DICOM RT-Structure Set .dcm files).

Here's a summary of the acceptance criteria and the study details for the Limbus Contour device, based on the provided FDA 510(k) submission information:

1. Acceptance Criteria and Reported Device Performance

The acceptance criterion for each contoured structure is that the Limbus DSC (Dice Similarity Coefficient) lower 95% confidence edge must be greater than or equal to the "Test DSC Threshold," which is derived from the mean minus the standard deviation of reference model DSCs from published machine learning autosegmentation models.

All listed structures met their respective acceptance criteria by having their Limbus DSC lower 95% confidence edge exceed or meet the specified Test DSC Thresholds.

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

• Test Set Sample Size: For each structure, a set of at least 10 patient scans was used for initial performance testing. Some structures had larger test sets, as indicated in the table (e.g., Bladder with 21 scans, Bowel with 23 scans, SpinalCord with 28 scans).
• Data Provenance: The test scans were randomly selected from a total pool of patient scans that contained the relevant structure. This pool was selected to reflect the general population of patients receiving radiation treatments. The data provenance details are further described in the "Training and Validation Datasets" section for the training data, implying a similar origin for the test data (multiple clinical sites and countries).

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

• Number of Experts: The ground truth contours were from "multiple experts at multiple institutions." The exact number is not explicitly stated.
• Qualifications of Experts: The ground truth contours were all reviewed by a "board certified radiation oncologist" to ensure consistency with established standards and guidelines for contouring and proper labeling.

4. Adjudication Method for the Test Set

The document does not explicitly describe a specific adjudication method like "2+1" or "3+1" for the ground truth contours in the test set. It states that the ground truth contours were from "multiple experts at multiple institutions" and reviewed by "a board certified radiation oncologist." This implies a form of consensus or expert review process, though the specific protocol for resolving discrepancies (if any) is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned as being performed for this submission. The performance data is focused on the standalone algorithm's accuracy (Dice Similarity Coefficient) against expert-generated ground truth.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study was done. The "Automatic Contouring - Validation Test" as described is a benchtop performance test where the software's outputs are compared to expert-generated ground truth without human intervention in the contouring process of the device itself.

7. The Type of Ground Truth Used

The ground truth used was expert consensus contours. These were human-generated contours reviewed by a board-certified radiation oncologist to ensure they conformed to clinical trial guidelines and established standards.

8. The Sample Size for the Training Set

The total number of unique scans included in training datasets exceeds 10,000 scans. The table provided in the document details the number of training and validation scans for each individual structure model, with training scan counts ranging from tens to over a thousand for each structure.

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

The ground truth for the training set was established through:

• Human-generated contours from a variety of anonymized and pseudo-anonymized datasets.
• These datasets were collected from publicly available clinical trials and the company's clinical and research partners.
• The training dataset ground truth contours were reviewed and edited by in-house clinicians and radiation oncologists to ensure consistency with established standards and guidelines for contouring (e.g., RTOG 1106, RTOG 0848, EMBRACE II, DAHANCA, NRG, ESTRO, ACROP, EPTN).
• To minimize bias, training data included scans from multiple clinical sites, countries (United States, Canada, United Kingdom, France, Germany, Italy, Netherlands, Switzerland, Australia, New Zealand, Singapore), and different makes/models of imaging devices (GE, Siemens, Phillips, Toshiba, Elekta).
• The scans and ground truth contours were from the general patient population receiving radiotherapy, with no restrictions based on age, ethnicity, race, gender, or disease states.

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