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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Pulmonary adenocarcinomas with ground-glass attenuation on thin-section CT: quantification by three-dimensional image
Hiromitsu Sumikawa1, Takeshi Johkoh, Tomofumi Nagareda
1Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0825, Japan. h-sumikawa@radiol.med.osaka-u.ac.jp
European Journal of Radiology
|May 1, 2007
Summary
New software for calculating the percentage of solid tumor in lung nodules showed good reproducibility. While requiring further refinement, it is a promising tool for assessing malignancy in small lung cancers.
Area of Science:
- Pulmonary medicine
- Medical imaging
- Oncology
Background:
- Ground-glass opacity nodules in the lungs can be challenging to assess for malignancy.
- Accurate measurement of the solid component ratio (%solid) is crucial for determining tumor grade.
- Existing manual methods for %solid calculation can be time-consuming and subject to inter-observer variability.
Purpose of the Study:
- To evaluate novel software for automated 3D calculation of whole tumor volume and %solid in pulmonary nodules.
- To compare the software's performance against manual measurements by two observers.
Main Methods:
- 49 patients with small ( < 2 cm) lung adenocarcinomas were included.
- Software-based automated %solid calculation was compared to manual measurements using four parameters.
- Intra- and inter-observer agreement was assessed using Spearman's rank correlation and Bland-Altman methods.
Main Results:
- The software demonstrated better observer agreement (mean difference -0.3%) compared to manual methods.
- Correlation with histological subtypes and vessel invasion was weaker with the software (r=0.487) than manual methods (r=0.534-0.557).
Conclusions:
- The software provides a reproducible method for %solid calculation in pulmonary nodules.
- Further improvements are needed for volumetric analysis, but the software shows promise for grading small lung cancers.

