5 research outputs found
Deep learning algorithms for tumor detection in screening mammography
Population-wide mammography screening was fully implemented in Sweden in 1997.
The implementation has helped to identify breast cancer at earlier stages and thereby
lowered mortality by 30-40%. However, it still has its limitations, many studies have
shown a discrepancy between radiologist when assessing mammographic examinations.
Additionally, women with very dense breasts have a lower mammographic sensitivity
and cancers are easily missed. There is also a shortage on breast radiologists and the
workload is increasing due to more women being screened. These challenges could be
addressed with the help of artificial intelligence systems. The artificial intelligence
system can serve both as an assistant to replace one radiologist in a double-reading
setting and as a tool to triage women with a high risk of breast cancer for additional
screening using other modalities.
In this thesis we used data from two cohorts: the cohort of screen aged women (CSAW)
and the ScreenTrust MRI cohort. The primary objectives were to establish performance
benchmarks based on radiologists recorded assessments (study I), compare the
diagnostic performance of various AI CAD systems (study II), investigate differences and
similarities in false assessments between AI CAD and radiologists (study III), and
evaluate the potential of artificial intelligence in triaging women for complementary MRI
screening (study IV). The data for studies I-III were obtained from CSAW, while the data
for study IV were obtained from the MRI ScreenTrust cohort. CSAW is a collection of
data from Stockholm County between the years of 2008 and 2015.
Study I was a retrospective multicenter cohort study that examined radiologist
performance benchmarks in screening mammography. Operating performance was
assessed in terms of abnormal interpretation rate, false negative rate, sensitivity, and
specificity. Measures were determined for each quartile of radiologists classified
according to performance, and performance was evaluated overall and by different
tumor characteristics. The study included a total of 418,041 women and 1,186,045 digital
mammograms, and involved 110 radiologists, of which 24 were defined as high-volume
readers. Our analysis revealed significant differences in performance between highvolume
readers, as well as a variability in sensitivity based on tumor characteristics. This
study was presented during the 2019 annual meeting of the Radiological Society of
North America, and was awarded the Trainee research prize that same year.
Study II was a retrospective case-control study that evaluated the performance of
three commercial algorithms. We performed an external evaluation of these algorithms
and compared the retrospective mammography assessments of radiologists with those
of the algorithms. Operating performance was determined in terms of abnormal
interpretation rate, false negative rate, sensitivity, specificity and the AUC. The study
included 8,805 women, of whom 740 women had cancer, and a random sample of 8,066
healthy controls. There were 25 radiologists involved. For a binary decision, the cutpoint
was defined by the mean specificity of the original first-reader radiologists
(96.6%). Our findings showed that one AI algorithm outperformed the other AI algorithm
and the original first-reader radiologists. This study was presented during the 2020
annual meeting of the European Society of Radiology.
Study III was a retrospective case-control study that evaluated the differences and
similarities in false assessments between an artificial intelligence system and a human
reader in screening mammography. In this study we included 714 screening
examinations for women diagnosed with breast cancer and 8,003 randomly selected
healthy controls. The abnormality threshold was predefined from study II. We examined
how false positive and false negative assessments by AI CAD and the first radiologist,
were associated with breast density, age and tumor characteristics. Our findings
showed that AI makes fewer false negative assessments than radiologists. Combining AI
with a radiologist resulted in the most pronounced decrease in false negative
assessments for high-density women and women over the age of 55. This study was
presented at the 2021 annual meeting of the Radiological Society of North America.
Study IV is a randomized clinical trial that aims to investigate the effect of applying
deep learning methods to select women for MRI-based breast cancer screening. The
study examines how effectively AI can identify women who should be offered a
complementary MRI screening based on their likelihood of having cancer that is not
visible on regular mammography. The results reported in this thesis are preliminary and
based on examinations from April 1, 2021 to December 31, 2022. During the indicated
time period, 481 MRI examinations have been completed, and 28 cancers have been
detected, yielding a cancer detection rate of 58.2 per 1,000 examinations. Although, the
trial is still ongoing, the inter-rim results suggest that using AI-based selection for
supplemental MRI screening can lead to a higher rate of cancer detection than that
reported for density-based selection methods.
In conclusion, we have shown that the use of AI for breast cancer detection can increase
precision and efficiency in mammography screening
2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC).
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