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Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis, the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population.

Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.

Lunit INSIGHT DBT is a computer-assisted detection/diagnosis (CADe/x) Software as a Medical Device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images. The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.

For each DBT case, Lunit INSIGHT DBT generates artificial intelligence analysis results that include the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer. This peripheral information intends to augment the physician's workflow to better aid in detection and diagnosis of breast cancer.

Here's an analysis of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) Clearance Letter for Lunit INSIGHT DBT (V1.2):

1. Acceptance Criteria and Reported Device Performance

Secondary Endpoints and Performance:

2. Sample Size for Test Set and Data Provenance

• Sample Size: 3,277 DBT exams of female adults.
• Data Provenance: Collected from multiple imaging facilities in US healthcare institutions to broadly cover the US population and maintain balanced demographic and cancer characteristic distributions. Data included patient demographics (age, ethnicity, race) and previous breast cancer history from the United States. DBT images were obtained from Hologic, GE Healthcare, Siemens, and FujiFilm 3D mammography equipment. The data contained various clinical subgroups and confounders (breast composition, BI-RADS categories, lesion type, cancer type, slice thickness).

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

• Number of Experts:
  • In datasets where three ground truthers were involved: 3 qualified breast imaging radiologists.
  • In datasets where two ground truthers were involved: 2 qualified breast imaging radiologists.
• Qualifications of Experts: Described as "expert breast imaging radiologists" and "qualified breast imaging radiologists." Within these groups, there was a hierarchy: one "final truther" or "most experienced" radiologist who made the ultimate decision.

4. Adjudication Method for the Test Set

The adjudication method varied based on the dataset:

• For datasets with three ground truthers: Two ground truthers independently performed the initial review, and the final truther (most experienced) determined the final reference standard, considering the results of the other two. This implies a 2+1 adjudication process in practice.
• For datasets with two ground truthers: The first truther independently completed the review, and the final truther (more experienced) made the final decision considering the results of the other truther. This also implies an ad-hoc 2-expert adjudication with a more experienced tie-breaker.

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

• Yes, an MRMC comparative effectiveness study was done.

• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: The study compared AI standalone performance against the average unaided reader performance.

  • AI standalone AUROC: 0.9430
  • Average reader AUROC: 0.8983
  • Difference: 0.0446 (95% CI: [0.0115, 0.0777], p = 0.0083)

  This indicates that the AI standalone AUROC was significantly superior to the average unaided reader AUROC by 0.0446.

  Further specific comparisons:

  • At the highest threshold (Score 60): AI standalone sensitivity (86.2%) was equivalent to the average reader's sensitivity (85.4%, p = 0.8912).
  • At the highest threshold (Score 60): AI standalone specificity (95.9%) was significantly improved over the average reader's specificity (77.3%, p < 0.001).

  The MRMC study involved a testing dataset of 258 cases (128 negative, 65 benign, 65 cancer) with 4 views, and a reading panel of 15 American Board of Radiology and MQSA-certified radiologists. The Obuchowski–Rockette (OR) method for a single-treatment, random-reader random-case (RRRC) CAD-vs-radiologist design was used.

6. Standalone Performance Study

• Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The primary test to demonstrate substantial equivalence was a standalone performance test using 3,277 DBT exams. The acceptance criteria described in section 1 (ROC AUC > 0.903) were based on this standalone performance. The results of this standalone test are detailed in section 1 of this response.

7. Type of Ground Truth Used

• The ground truth used was expert consensus combined with clinical supporting data and pathology reports.
  • Expert breast imaging radiologists ("Ground Truthers") classified each DBT exam as non-cancer or cancer and annotated malignant lesion locations.
  • This process involved reviewing collected study exams using "relevant clinical supporting data such as radiology reports and pathology reports acquired from the investigational institution."
  • For biopsy-proven cancer exams, ground truthers specifically referred to "relevant pathology report containing the cancer characteristic information (i.e., cancer location, size, shape, presence of calcification, pathologic results, etc.) for the ground truthing."

8. Sample Size for the Training Set

• The document states, "The test set used for the clinical validation was completely independent from the datasets used for training, tuning, or calibrating the algorithm." However, the sample size for the training set is not explicitly provided in the given text.

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

• The document implies that training data was used ("training, tuning, or calibrating the algorithm") but does not describe how the ground truth for the training set was established.

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