Journal of thoracic disease · 2025
Semi-quantitative software evaluation of COVID-19 CT examinations-correlation with clinical parameters.
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Trif Nicoleta, Kloth Christopher, Büttner Susanne Martina, Egenrieder Nadine, Wolf Daniel, Schmidt Stefan Andreas, Vogele Daniel, Sollmann Nico, Flack Franziska, Peters Lynn, Grüner Beate, Jungwirth Bettina, Stenger Steffen, Stamminger Thomas, Beer Meinrad
Аннотация
BACKGROUND: Software-guided semi-quantitative analysis of coronavirus disease 2019 (COVID-19) pneumonia in lung computed tomography (CT) datasets for severity assessment. Further to correlate imaging findings with the need of intensive care medicine and clinical parameters.
METHODS: This single-center retrospective study analyzed 66 consecutive patients (31 females, mean age 64.6±16.2 years) with lung CT datasets from 12/2020 to 05/2021 and confirmed COVID-19 pneumonia. Lung CT datasets were evaluated using a semi-quantitative software for segmentation and quantification. Correlation with underlying diseases, laboratory parameters and further course were assessed, including intubation and need for intensive care.
RESULTS: Total lung volume was 3,903.65±1,185.67 mL, mean volume of opacities was 866.52±829.29 mL, reflecting 23.54%±21.92% of total lung volume. Volume of high opacities was 186.88±208.15 mL reflecting 0.06%±0.07% of total lung volume. Overall, 12 patients died (18.2%), 10 patients (15.2%) required intubation and in 27 cases (40.9%) intensive care was necessary. In patients who died volume of opacities and high opacities were significantly higher (P<0.05). Significant differences with a risk for needing intensive care medicine were extensive pulmonary opacities, volume of high opacities, and percentage of high opacities (P<0.001 each).
CONCLUSIONS: COVID-19 pneumonia may be semi-quantified using an artificial intelligence (AI)-based software approach. Quantitative methods could provide precise information on the volume of opacities and may allow detecting connections to patient therapy, including the need for intensive care.
METHODS: This single-center retrospective study analyzed 66 consecutive patients (31 females, mean age 64.6±16.2 years) with lung CT datasets from 12/2020 to 05/2021 and confirmed COVID-19 pneumonia. Lung CT datasets were evaluated using a semi-quantitative software for segmentation and quantification. Correlation with underlying diseases, laboratory parameters and further course were assessed, including intubation and need for intensive care.
RESULTS: Total lung volume was 3,903.65±1,185.67 mL, mean volume of opacities was 866.52±829.29 mL, reflecting 23.54%±21.92% of total lung volume. Volume of high opacities was 186.88±208.15 mL reflecting 0.06%±0.07% of total lung volume. Overall, 12 patients died (18.2%), 10 patients (15.2%) required intubation and in 27 cases (40.9%) intensive care was necessary. In patients who died volume of opacities and high opacities were significantly higher (P<0.05). Significant differences with a risk for needing intensive care medicine were extensive pulmonary opacities, volume of high opacities, and percentage of high opacities (P<0.001 each).
CONCLUSIONS: COVID-19 pneumonia may be semi-quantified using an artificial intelligence (AI)-based software approach. Quantitative methods could provide precise information on the volume of opacities and may allow detecting connections to patient therapy, including the need for intensive care.