Keywords:
Retrospective, Dilatation, Chronic obstructive airways disease, Aneurysms, Cost-effectiveness, Computer Applications-Detection, diagnosis, CT, Thorax, Oncology, Artificial Intelligence, Chest, Case-control study, Performed at one institution
Authors:
A. Nikolaev, I. Blokhin, V. Gombolevskiy, P. Gelezhe, V. Chernina, S. Morozov, A. Laipan; Moscow/RU
DOI:
10.26044/ecr2020/C-11091
Methods and materials
Patient population:
During the baseline round of screening, 4762 ultra-LDCT (individuals who met the inclusion criteria for the lung cancer risk group) were performed in 10 medical organizations that provide primary health care to the adult population in Moscow. The studies were performed on a 64-detector unit using specially developed ultra low dose protocols for different patient weight categories with a radiation dose of up to 1 mSv.
Data acquisition:
The study included 254 (4.78%) ultra-LDCTs, which were selected using a random number generator. The results of patients who were routed for additional examinations and consultations based on the results of ultra-LDCT were not considered.
The selected group included the ultra-LDCT results of 142 (56.0%) men and 112 (44.0%) women; the average age was 61 years.
The age distribution was as follows:
-
55-59 years old - 25.2% (64),
-
60-64 years old - 25.2% (64),
-
65-69 years old - 24.8% (63),
-
70-74 years old 24.8% (63).
Data and Statistical analysis:
A retrospective review of the results (images and reports) of chest ultra-LDCT from the Moscow Lung Cancer Screening project in 2017 was carried out to assess the prevalence and character of incidental findings. At the same time, we excluded lung nodules classified according to Lung RADS-2014.
Medical data is depersonalized in accordance with applicable law on the protection of personal data. Image analysis in the DICOM 3.0 standard was carried out using the software “AGFA Agility Enterprise 8.0” and “OsiriX MD (v.5.5.1 64-bit)”.