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OncoStudio provides deep-learning-based automatic contouring to organs at risk in DICOM-RT format from CT images. This software could be used as an initial contouring for the clinicians to be confirmed by the radiation oncology department for treatment planning or other professions where a segmented mask of organs is needed.

• Deep learning contouring from Head & Neck, Thorax, Abdomen, and Pelvis
• Generates DICOM-RT structure of contoured objects
• Manual Contouring
• Receive, transmit, store, retrieve, display, and process medical images and DICOM objects

OncoStudio is a standalone software that provides deep-learning-based automatic contouring to organs at risk in DICOM-RT format from CT images. This software could be used as an initial contouring for the clinicians to be confirmed by the radiation oncology department for treatment planning or other professions where a segmented mask of organs is needed.

• Deep learning contouring from Head & Neck, Thorax, Abdomen, and Pelvis
• Generates DICOM-RT structure of contoured objects
• Manual Contouring
• Receive, transmit, store, retrieve, display, and process medical images and DICOM objects
  It also has the following general functions:
• Patient management;
• Review of processed images;
• Open and Save of files.

Based on the provided text, here's a description of the acceptance criteria and the study that proves the device meets those criteria for OncoStudio (OS-01):

The submission details a standalone performance test conducted to demonstrate the contouring capabilities of OncoStudio, an AI-powered software for automatic organ at risk contouring from CT images. The primary evaluation metric for acceptance was the Dice coefficient (DSC).

1. Acceptance Criteria and Reported Device Performance

The text explicitly states: "For the structures being compared, the mean Dice coefficient (DSC) of structures for each anatomical region (Head & Neck, Thorax, Abdomen, and Pelvis) should meet the established criteria." However, the specific numerical established criteria for the mean Dice coefficient for each anatomical region (Head & Neck, Thorax, Abdomen, and Pelvis) are not reported in the provided document. Similarly, the actual reported device performance (the mean DSC achieved for each region) is not explicitly stated in the visible sections.

To fully answer this, a table would look like this, but with missing data based on the provided text:

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

• Test Set Sample Size: 310 CT images.
  • 140 images from Yonsei Severance Hospital (Republic of Korea)
  • 121 images from OneMedNet (U.S.A.)
  • 49 images from University Hospital Basel (Switzerland)
• Data Provenance: The data is from South Korea, U.S.A., and Switzerland. The text specifies it was "collected from Yonsei Severance Hospital (Republic of Korea), OneMedNet (U.S.A.), and University Hospital Basel (Switzerland)". The data from OneMedNet is a "purchased set of CT data, mainly comprised of U.S.A. population." Yonsei Severance Hospital is in South Korea, and the Basel data is known as the TotalSegmentator dataset.
• Retrospective or Prospective: Not explicitly stated, but the description of data collection "from the years 2012, 2016, and 2020 from the University Hospital Basel through picture archiving and communication system (PACS)" implies a retrospective collection for at least part of the dataset.

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

• Number of Experts: Three radiation oncologists established the ground truth segmentations for the test set.
• Qualifications of Experts (for Yonsei Severance Hospital and OneMedNet data): The radiation oncologists had "3-20 years of clinical practice," and included "associate professor, assistant professor, and radiation oncologist resident from two institutions (Yonsei Cancer Center, Samsung Seoul Hospital)."
• Qualifications of Experts (for University Hospital Basel data): The ground truth segmentation was "supervised by two physicians with 3 (M.S.) and 6 years (H.B.) of experience in body imaging, respectively." (Note: this refers to the public dataset from Basel, which was used for training, but the text states for the test set that "Ground truth segmentations were established by three radiation oncologists following international clinical guidelines" without distinguishing the origin for the test set ground truth specifically in terms of expert type, likely implying the former expert group applied to the test set as well for consistency).

4. Adjudication Method for the Test Set

The ground truthing process for the Yonsei Severance Hospital and OneMedNet data (which largely comprises the test set) was:

• "First, the 1 radiation oncologist manually delineated the organs."
• "Second, segmentation results generated by 1 radiation oncologist are sequentially edited and confirmed by 2 radiation oncologists. In this editing process, the first radiation oncologist makes corrections, and the corrected results are received and finalized by another radiation oncologist."

This indicates a sequential review and confirmation process rather than a strict 2+1 or 3+1 consensus, with an initial delineator and then two subsequent reviewers/editors, likely leading to a consensus by the end of the process.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance was not mentioned in the provided text. The study described is a standalone performance test of the algorithm.

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

Yes, a standalone performance test was done. The text explicitly states: "A standalone performance test was conducted to compare the contouring capabilities of OncoStudio."

7. The Type of Ground Truth Used

The ground truth used was expert consensus/manual annotation by radiation oncologists/physicians following international clinical guidelines (RTOG and clinical guidelines).

8. The Sample Size for the Training Set

• Total Training Data: 2,128 images.
  • 731 images from Yonsei Severance Hospital (Republic of Korea)
  • 194 images from OneMedNet (U.S.A)
  • 1203 images from University Hospital Basel (Switzerland)

The total collected data was 2,438 datasets (315 US, 871 Korea, 1252 Europe). From this, 310 data were allocated for the test dataset, and the remaining 2,128 were used for training.

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

The ground truth for the training set was established similarly to the test set:

• For Yonsei Severance Hospital (Korea) and OneMedNet (U.S.) data: Established by three radiation oncologists with 3-20 years of clinical practice following RTOG and clinical guidelines using manual annotation. The process involved initial manual delineation by one radiation oncologist, followed by sequential editing and confirmation by two other radiation oncologists.
• For University Hospital Basel (Europe) data (TotalSegmentator dataset): This is public data where ground truth was established by manual segmentation and refinement supervised by two physicians with 3 and 6 years of experience in body imaging.

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