According to the data of the Ministry of Health and
Welfare, lung cancer has the highest mortality rate for
past 10 years, and is one of the deadliest cancers at
present. Since early screening is not easy for lung
cancer, nearly 70% of patient belongs to advance stage
when they were diagnosed, which is hard to be treated.
Furthermore, lung cancer tumors are easy to metastasize,
cause the mortality rate remains very high. In recent
years, many early screening methods have been proposed,
including the use of Low Dose Computed Tomography (LDCT)
instead of traditional X-ray films for lung cancer
screening, and the corresponding screening reporting
system such as Lung-RADS (Lung CT Screening Reporting
and Data System) guidelines had been proposed to
standardize LDCT lung cancer screening diagnostic
procedures. However, the interpretation of LDCT images
is quite time-consuming. In the foreseeable future, lung
cancer screening will continue to be popularized. By
then, a larger number of images will need to be viewed
by doctors. If automated screening technology can be
introduced, the cost of screening manpower can be
greatly reduced. In previous studies, in addition to
discussing the automatic algorithm of each step in the
screening process, algorithm integration study based on
lung imaging reporting system has also been gradually
developed.
In this study, an automatic
lung cancer screening process based on the Lung- RADS
guidelines has been proposed, which aims to
significantly save the time of
doctors. The automatic
screening process is automated by using machine
learning, radiomics and some other methods. The trained
model can directly detect and
segment pulmonary
nodules from CT images, and can warn the doctors about
the nodules with controversial categories, which is more
in line with practical diagnosislogic than previous
studies. Afterward, a practical system which ensemble
the whole process is developed as a software
application. At last, basic usability test is performed
on the software, and the software was demonstrated to
professional doctors and we collected their opinions,
providing a reference for future productization.
Keyword：Lung-RADS, Lung Cancer
Screening, Lung Nodules, Machine Learning, Radiomics
