Aims and objectives
The demonstration of a 20% reduction in lung cancer mortality in the USA National Lung Screening Trial (NLST) [1]and the subsequent decision by the U.S.
Centers for Medicare and Medicaid Services to provide Medicare coverage for lung cancer screening has paved the way for nationwide lung cancer screening in the USA.Additionally,
results of the NELSON trial also confirmed the value of low-dose CT screening with decreased mortality by 26% in high-risk men and 61% in high-risk women over a 10-year period.
[2]
Lung cancer screening...
Methods and materials
Data preparation:
In this retrospective study,
low-dose chest CTs were taken from the NLST dataset.
1245 CT scans were taken for training and 350 CT scans were taken for validation.
Pathologically proven malignancy status of lung cancers was taken as ground-truth.
Lung nodule annotations from 4 radiologists were taken from 888 CT scans from the publicly available LIDC-IDRI [3] dataset.
CT scans with slice-thickness > 2.5mm were excluded to avoid partial-volume effect as recommended by Ginneken et al [4] and Setioet al[5].
Nodule detection is...
Results
The results of radiologists on the Likert scales 1 and 2 wereconsidered as negative for malignancy and 3,4 and 5 were considered to be positive for the presence of malignancy.
For the AI,
a predicted probability > 0.25 was considered to be positive for the presence of malignancy.
On the 96 chest CT scans reviewed by the radiologists,
they had AUCs of 0.82,
0.81,
0.83 and 0.83 for predicting the risk of malignancy whereas the AI had an AUC of 0.91.
Individually,
radiologists’ accuracy varied...
Conclusion
The deep learning system shows better performance than experienced radiologists,
individually and in aggregate,
in predicting the presence of malignant nodules on the 96 CT scans obtained from the NLST dataset.
The difference in the interpretation of radiologists were not found to be statistically significant.
Clinically,
as low-dose CT scans are non-contrast scans,
the classically described contrast enhancementcharacteristics for diagnosing malignant nodules cannot be used to assess the risk of malignancy in these cases.
The availability of a highly sensitive nodule characterization tool will...
References
National Lung Screening Trial Research Team,
Aberle DR,
Adams AM,
Berg CD,
et al (2011): Reduced lung-cancer screening mortality with low-dose computed tomographic screening.
New England Journal of Medicine,
365,
395-409.
Koning,
Harry & Aalst,
Carlijn & Haaf,
Kevin & Oudkerk,
M.
(2018).
PL02.05 Effects of Volume CT Lung Cancer Screening: Mortality Results of the NELSON Randomised-Controlled Population Based Trial.
Journal of Thoracic Oncology.
13.
S185.
10.1016/j.jtho.2018.08.012.
Armato III,
Samuel G.,
McLennan,
Geoffrey,
Bidaut,
Luc,
McNitt-Gray,
Michael F.,
Meyer,
Charles R.,
Reeves,
Anthony P.,
…...