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Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing the breast ultrasound images of patients with soft tissue breast lesions who are being referred for further diagnostic ultrasound examination. Koios DS for Breast is a machine learning-based decision support system, indicated as an adjunct to diagnostic ultrasound for breast cancer. Koios DS for Breast automatically classifies user-selected region(s) of interest (ROIs) containing a breast lesion into four BI-RADS-aligned categories (Benign, Suspicious, Probably Malignant), and displays a continuous graphical confidence level indicator of where the lesson falls across all categories. Koios DS for Breast also automatically classifies lesion shape and orientation according to BI-RADS descriptors.

The software requires a user to select up to two orthogonal views, that represent a single lesion to be selected and processed. When utilized by an interpreting physician who has completed training, this device provides information that may be useful in rendering an accurate diagnosis. Patient management decisions should not be made solely on the results of the Koios DS for Breast analysis. This device is intended to help trained interpreting physicians improve their overall accuracy as well as reduce inter- and intra-operator variability.

Koios DS for Breast may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.

Limitations: Koios DS for Breast is not to be used on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. Koios DS for Breast is not intended for the primary interpretation of digital mammography images. Koios DS for Breast is not intended for use on mobile devices.

Koios Decision Support (DS) for Breast is a software application designed to assist trained interpreting physicians in analyzing breast ultrasound images. The system provides image derived data via web triggering and remote analysis. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS for Breast application, the user examines selected breast ultrasound DICOM images. The user selects up to two Regions of Interest (ROIs) of two orthogonal views of a breast lesion for processing by Koios DS for Breast. The ROI(s) are transmitted electronically to the Koios DS for Breast server for image processing and the results are returned to the user for review.

The Koios DS for Breast core engine characterizes image features using the ROI data to generate a categorical output that aligns to BI-RADS categories. The engine uses computer vision and machine learning techniques embedded within a software capable of reading, interpreting, analyzing, and generating findings from ultrasound data. The underlying engine draws upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model. The categorical output of the Koios DS for Breast engine is divided into four categories (Benign, Probably Benign, Suspicious, Probably Malignant), separated by three operating points, aligning with or exceeding the sensitivity and specificity of radiologist chosen BI-RADS categorizations. . The output of the system is a digital display to be used as a concurrent read. Koios DS for Breast is intended to support compliance with the ACR BI-RADS ultrasound lexicon classification form. The engine additionally classifies the region of interest on the basis of shape (Oval, Round, Irregular) and orientation (Parallel to Skin, Not Parallel).

Koios DS for Breast results can be saved or transferred in three separate ways: in-transit transmission, PACS saving, and exporting results to third-party reporting software. Intransit transmission may be utilized when users wish to share analyses across viewing workstations. Results can be stored in in-transit memory for a preset period of time defined by a system administrator. These results are never locally cached, written to disk, or otherwise stored outside of in-transit memory. After that preset period of time, all results are wiped from the local memory.

Another method of saving is storing a report in the patient series on the PACS. After single or multiple breast lesion analyses have been performed and ultimately accepted by a trained interpreting physician, Koios DS for Breast can export a summary report to PACS as an addendum to the DICOM series that was selected for processing. This report serves as future reference and aid in comparison of cases requiring follow up. This functionality is strictly reserved for approved users.

Koios DS for Breast also supports exporting results to third-party reporting software to facilitate the reporting process. Saving or exporting preferences can be configured by the system administrator and user.

The Koios DS for Breast software is an ASP.NET web application that is deployed to an IIS Web Server inside a Windows operating system environment. The software does not require any specialized hardware, but the time to process ROIs will vary depending on the hardware specifications. If utilizing the recommended technical specifications, the time to generate and present results for two analyzed ROIs will be

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided FDA 510(k) summary for Koios DS for Breast:

Table of Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Sizes for Test Sets and Data Provenance:

• MRMC Clinical Study:
  • Cases: 900 patient cases. Each case included up to two orthogonal views of a single breast lesion.
  • Data Provenance: Not explicitly stated, but the demographic information for patients (race, age distribution, breast density) is presented in a way that suggests a diverse, likely multi-center, retrospective dataset representative of national rates (Breast Cancer Surveillance Consortium 2006-2009 reference). It is implied to be retrospective as patients "present with a soft tissue breast lesion who are being referred for further diagnostic ultrasound examination" and their ground truth determined by pathology or 1-year follow-up.
• Standalone Performance (Malignancy Risk Classification):
  • Cases: 900 lesions from 900 different patients. Each lesion represented by two orthogonal images (total 1800 images). This data was specifically "set aside from the system's training data" for validation.
  • Data Provenance: Not explicitly stated, but implies similar provenance to the MRMC study's test set as it's a validation set from their overall data.
• BI-RADS Descriptors Bench Testing (Shape and Orientation):
  • Cases: 1300 cases.
  • Data Provenance: Not explicitly stated.

2. Number of Experts Used to Establish Ground Truth for Test Sets and Qualifications:

• MRMC Clinical Study: The ground truth for the 900 cases in the MRMC study was "determined by pathology or 1-year follow-up for cases that were not biopsied" (stated under "Malignancy Risk Classification" for the standalone test, but applies to the overall definition of malignancy/benignancy for the test set). No human experts were used to establish the ground truth for the malignancy outcome itself for either the MRMC study or standalone performance validation.
• BI-RADS Descriptors Bench Testing (Shape and Orientation):
  • Number of Experts: Three MQSA certified radiologists.
  • Qualifications: All with over 20 years of experience and at least 3000 images read per year.

3. Adjudication Method for the Test Set:

• MRMC Clinical Study & Standalone Malignancy: Ground truth was established by pathology or 1-year follow-up. This is an objective ground truth, so no adjudication among experts was needed for disease status.
• BI-RADS Descriptors Bench Testing: Ground truth for shape and orientation was established by majority decision of the three radiologists. For the second test comparing agreement (Cohen's Kappa), "majority agreement was not enforced and all cases were analyzed for reader and reader-system agreement," implying individual assessments were used for pairwise comparisons.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size:

• Yes, an MRMC comparative effectiveness study was done.
• Study Objective: To determine the impact on Interpreting Physician (Reader) performance (measured by AUC) when Koios DS for Breast and an ultrasound examination are combined (USE + DS), compared to USE Alone.
• Effect Size: The study showed a mean AUC shift of +0.037 (from 0.836 for USE Alone to 0.873 for USE + DS). This was found to be statistically significant.
• Inter-reader Variability: The average Kendall Tau-B coefficient significantly increased from 0.5404 (USE Alone) to 0.6797 (USE + DS), indicating a reduction in inter-reader variability with AI assistance.
• Intra-reader Variability: The class switching rate (a measure of intra-reader variability) was statistically significantly reduced from 13.6% (USE Alone) to 10.8% (USE + DS), demonstrating improvement.

5. If a Standalone (algorithm only without human-in-the-loop performance) was done:

• Yes, standalone performance was evaluated for malignancy risk classification and BI-RADS descriptors.
  • Malignancy Risk Classification (algorithm only): Achieved an AUC of 88.2% on a validation set of 900 lesions.
  • BI-RADS Descriptors (algorithm only): The system's accuracy for shape (87.62%) and orientation (91.12%) classification was compared to and found to be statistically equivalent to human radiologists' performance based on majority decisions. The agreement (Cohen's Kappa) between the system and readers was also found to be not statistically different from agreement between pairs of readers.

6. Type of Ground Truth Used:

• For Malignancy Risk Classification (both MRMC and standalone): Ground truth was determined objectively by pathology or 1-year clinical follow-up for cases not biopsied. This is a robust form of ground truth based on definitive outcomes.
• For BI-RADS Descriptors (Shape and Orientation): Ground truth was established by expert consensus (majority decision) of three highly experienced, MQSA-certified radiologists.

7. Sample Size for Training Set:

• The document states that the underlying engine "draws upon knowledge learned from a large database of known cases" but does not specify the exact sample size of the training set. It only mentions that the 900 cases for standalone validation were "set aside from the system's training data."

8. How Ground Truth for Training Set was Established:

• The document states that the engine "draws upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model." While not explicitly detailed, this implies that the ground truth for training data was also established using definitive outcomes like pathology or clinical follow-up, similar to the validation set, to ensure accurate "known cases." For the BI-RADS descriptors, it's mentioned that Koios DS for Breast and cCAD (its predecessor) "share a similar algorithm set, training data, and validation approach for automated shape and orientation assessment," which generally suggests the use of expert annotations for these subjective descriptors.

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