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AI analysis for Chest x-ray

Insight CXR

AI analysis for Mammography

Insight MMG

AI analysis for Tissue Slides

Insight SCOPE
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For Breast Cancer Detection
Lunit INSIGHT MMG capture

Background

Screening mammography is the only single modality proven to improve breast cancer survival, and is the main test for breast cancer screening.1 However, screening breast cancer by mammography is far from ideal. False negative interpretations have been reported to range from 10 to 30%,2-4 and among the average 10% of subjects recalled for follow-up on initial screening mammogram, less than 5% are eventually diagnosed with cancer, representing a 95% false positive rate.5

Product description

Developed using Lunit’s cutting-edge deep learning technology,6 Lunit INSIGHT MMG accurately detects lesions suspicious of breast cancer in the form of diagnostic support tool. The AI solution generates (1) location information of detected lesions in the form of heatmaps and (2) abnormality scores reflecting the probability that the detected lesion is malignant. The solution is indicated to be directly involved in the primary interpretation process of radiologists.

Primary value proposition

  • Assist both breast specialists and general radiologists (non-specialists) to increase cancer detection rate and decrease recall rate when interpreting screening mammograms.
  • Enable general radiologists to perform at specialist level.

Training & Validation

  • Trained with a large-scale (>200,000 total cases, >50,000 cancer cases), high-quality (biopsy-proven cases) training set.
  • Demonstrated to perform at a standalone accuracy of 96% in ROC AUC.7
  • Clinically validated to significantly improve the interpretive capabilities of radiologists upto 10%.8
  • Currently in preparation for regulatory approval in various markets worldwide, including FDA, CE, MFDS.

Example cases

example case1example case1

CASE #1. A mass in the right breast, diagnosed as invasive ductal carcinoma, is shown to be properly detected with a malignancy score of 98%.

example case2example case2

CASE #2. A lesion with microcalcifications in the right breast, diagnosed as ductal carcinoma in situ, is shown to be properly detected with a malignancy score of 58%.

Journals & Conference
Abstracts

  • Kim EK et al. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study, Sci Rep. 2018 Feb 9;8(1):2762.
  • Kim EK et al. Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography - Reader Study, RSNA 2018
  • Advanced Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography, RSNA 2017

References

1 Myers ER, Moorman P, Gierisch JM, et al. Benefits and harms of breast cancer screening: a systematic review. JAMA 2015;314:1615-34.

2 Thurfjell EL, Lernevall KA, Taube A. Benefit of independent double reading in a population-based mammography screening program. Radiology 1994;191:241-4.

3 Yankaskas BC, Klabunde CN, Ancelle-Park R, et al. International comparison of performance measures for screening mammography: can it be done? J Med Screen 2004;11:187-93.

4 Ciatto S, Ambrogetti D, Risso G, et al. The role of arbitration of discordant reports at double reading of screening mammograms. J Med Screen 2005;12:125-7.

5 http://breastscreening.cancer.gov.

6 Lunit’s high-end deep learning technology has been demonstrated in various international competitions - won World #1 in MICCAI TUPAC 2016, and CAMELYON 2017; Recognized as one of the world's top 100 AI startups by CB Insights in 2017.

7 ROC AUC Area Under the Receiver Operating Characteristic Curve

8 Severance Hospital, Observer performance study, 2017