Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan

Author:

Suzuki MinakoORCID,Fujii Yoshimi,Nishimura Yurie,Yasui Kazuma,Fujisawa Hidefumi

Abstract

AbstractThis study aimed to determine the optimal conditions to measure the percentage of area considered as pneumonia (pneumonia volume ratio, PVR) and the computed tomography (CT) score due to coronavirus disease 2019 (COVID-19) using the Ziostation2 image analysis software (Z2; Ziosoft, Tokyo, Japan), which is popular in Japan, and to evaluate its usefulness in assessing the clinical severity. We included 53 patients (41 men and 12 women, mean age: 61.3 years) diagnosed with COVID-19 using the polymerase chain reaction who had undergone chest CT and were hospitalized between January 2020 and January 2021. Based on the COVID-19 infection severity, the patients were classified as mild (n=38) or severe (n=15). For 10 randomly selected samples, the PVR and CT scores by Z2 under different conditions and the visual simple PVR and CT scores were compared, and the conditions with the highest statistical agreement were determined. The usefulness of the clinical severity assessment based on PVR and CT scores using Z2 under the determined conditions was statistically evaluated. The best agreement with the visual measurement was achieved by the Z2 measurement condition of ≥ –600 HU. The areas under the receiver operating characteristic curves, the Youden index, and the sensitivity, specificity, and p-values of PVR and CT scores by Z2 were as follows: PVR; 0.881, 18.69, 66.7, 94.7, and <0.001, CT score; 0.77, 7.5, 40, 74, and 0.002, respectively. We determined the optimal condition for assessing the PVR of COVID-19 pneumonia using Z2 and demonstrated that the AUC of PVR was higher than that of the CT score in the assessment of clinical severity. The introduction of new technologies is time-consuming and expensive; our method has high clinical utility and can be promptly used in any facility where Z2 has been introduced.

Publisher

Cold Spring Harbor Laboratory

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