Abstract
We did this study to investigate the effect of thick (5mm) and thin (1 or 0.625 mm) slice thickness of CT images on evaluating pulmonary nodules' growth to improve their diagnostic accuracy. The clinical and CT data of 251 patients with lung nodules and two follow-up CTs from October 2016 to October 2019 were analyzed retrospectively. Malignant nodules were confirmed by pathology, and benign nodules were confirmed by pathology or follow-up. Two radiologists double-blindly assessed the CT features (density, shape, lobes, border), maximum diameter, and volume of nodules on the thick (5MM) and thin (≤1MM) images of two follow-up CTs. We use One-way analysis of variance for quantitative data; the X2 test or FISHER exact probability method was used for qualitative data; and the ROC curve was used to analyze the diagnostic power of nodule size, volume, and change in differentiating benign and malignant lesions. Among 251 pulmonary nodules, 117 (46.6%) benign nodules and 134 (53.3%) malignant nodules. During the CT follow-up, the volume measured on the thick-section image, the diameter, and the volume measured on the thin-section image were statistically different in benign and malignant lung nodules (P<0.001). In contrast, the diameter measured on the thick-section image was similar between these two groups (P=0.328). For benign and malignant pulmonary nodules, the diameter, volume, and change measured on the thin-section image were significantly larger than the thick-section image's data (P<0.001). The ROC curve showed that the diagnostic efficiency of volume was higher compared to the diameter. There were significant differences in nodule type, density change, shape, lobulation, and pleural retraction between benign and malignant nodules for CT features. Accurately assessing the volume changes combined with CT characteristics will help improve lung nodules' diagnosis accuracy. Volume measured on thin-section (1mm) CT images is the best quantitative parameter for assessing the change of pulmonary nodules. Combining Volume change with CT characteristics would help to improve the diagnostic accuracy.
Highlights
The pulmonary nodule is a spherical, well-circumscribed, radiographic opacity measuring within 3 cm in diameter and is surrounded by aerated lung
Studies have shown that in patients with malignant tumors outside the lungs, the proportion of lung nodules detected by thin-layer computed tomography (CT) can reach 60-75%.[4]
Measurement of data is expressed by means ± standard deviation or interquartile range (IQR) and analyzed by Wilcoxon test; count data are expressed by rate (%) and analyzed with Pearson chi-square or Fischer exact test
Summary
The pulmonary nodule is a spherical, well-circumscribed, radiographic opacity measuring within 3 cm in diameter and is surrounded by aerated lung. Associated atelectasis, hilar enlargement, or pleural effusion is not present.[1] Among malignant tumors, lung cancer gets the highest ranking, and it is the leading cause of mortality. Early diagnosis of lung cancer is essential.[2] Studies have shown that in patients with malignant tumors outside the lungs, the proportion of lung nodules detected by thin-layer CT can reach 60-75%.[4] If a pulmonary nodule is detected, especially from metastasis, the possibility of being considered malignant is significantly increased, which leads to an increased risk of misdiagnosis to a great extent. Detecting small pulmonary nodules highly suggestive of lung cancer is increased due to widespread
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