SEOUL, South Korea,
Feb. 10, 2020 /PRNewswire/ -- A
new study, published in Lancet Digital Health, shows the added
value of AI-aided breast cancer detection from mammography
The study, conducted by Korean academic hospitals and Lunit, a
medical AI company specializing in developing AI solutions for
radiology and oncology, features large-scale data of over 170,000
mammogram examinations from five institutions across South Korea, USA, and the UK, consisting of Asian and
Caucasian female breast images. The dataset includes over 36,000
biopsy-proven, independent cancer positive cases—the largest scale
of cancer data among mammography-related AI studies.
"It is an unprecedented quantity of data with accurate ground
truth--especially the 36,000 cancer cases, which is seven times
larger than the usual number of datasets from resembling studies
conducted previously," said Hyo-Eun
Kim, the first author of the study and Chief Product Officer
at Lunit. "The quality of data has also been assured, with ethnic
diversity, covering various imaging devices and scanning
conditions. The marriage between the diversity of the dataset and
the uniqueness of our algorithm, designed in interaction with one
another, has been key to years of development of Lunit INSIGHT MMG
since early 2016."
The study shows a significant improvement in the performance of
radiologists, before and after using AI. According to the study,
the AI alone showed 88.8% sensitivity in breast cancer detection,
whereas radiologists alone showed 75.3%. When radiologists were
aided by AI, the accuracy increased by 9.5% to 84.8%.
One of the major findings also shows that AI, in comparison to
the radiologists, displayed better sensitivity in detecting cancer
with mass (90% vs 78%) and distortion or asymmetry (90% vs 50%).
The AI was better in the detection of T1 cancers, which is
categorized as early-stage invasive cancer. AI detected 91% of T1
cancers and 87% of node-negative cancers, whereas the radiologist
reader group detected 74% for both.
Breast density is also an important factor in diagnosing
mammograms, as dense breast tissues, mostly from the Asian
population, are harder to interpret since dense tissue is more
likely to mask cancers in mammograms. The findings show that the
diagnostic performance of AI was less affected by breast density,
whereas radiologists' performance was prone to density, showing
higher sensitivity for fatty breasts at 79.2% compared to dense
breasts at 73.8%. When aided by AI, the radiologist's sensitivity
when interpreting dense breasts increased by 11%.
"One of the biggest problems in detecting malignant lesions from
mammography images is that to reduce false negatives—missed
cases—radiologists tend to increase recalls, casting a wider safety
net, which brings an increased number of unnecessary biopsies,"
said Prof. Eun-Kyung Kim, the
corresponding author of the study and a breast radiologist at
Yonsei University Severance Hospital.
"It requires extensive experience to correctly interpret breast
images, and our study showed that AI can help find more breast
cancer with lesser recalls, also detecting cancers in its early
stage of development."
The study has been published online on 6
February 2020, in Lancet Digital Health. Lunit
INSIGHT MMG is commercially available and is being used clinically,
approved by Korea Ministry of Food and Drug Safety and pending
approval by European CE within the first quarter and FDA clearance
by later this year. It is available for free online demo at
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