2023 World Conference on Lung Cancer (Posters)-EP04.01. Optimal Predictors for Lung Cancer Using High-Resolution CT

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This study aimed to identify optimal predictors for lung cancer using high-resolution computed tomography (HRCT). The researchers conducted a retrospective study on 609 patients who underwent surgical treatment for non-small cell lung cancer at Tohoku University Hospital between January 2014 and December 2018. The patients were divided into three groups based on the radiological findings on HRCT: solid nodules, part-solid ground-glass nodules (GGNs), and pure GGNs.<br /><br />The results showed that the proportion of lung adenocarcinoma in part-solid GGNs was significantly higher compared to the other two types of nodules. Among the 136 patients with part-solid GGNs, most cases were diagnosed as primary lung cancer, except for 11 cases of benign or metastatic lung tumors. It was also found that a C/T ratio of less than 0.5 and well-defined GGN components with a round or segmental edge were potential predictors for lung cancer.<br /><br />Additionally, the researchers observed that in the 11 patients who did not have lung cancer, the boundary between the GGN portion and normal lung tissue was unclear, and the tumors were evaluated as irregular nodules.<br /><br />The findings suggest that the C/T ratio and morphological features on HRCT can be used as predictors for lung cancer in patients with part-solid GGNs. These predictors can help determine appropriate treatment strategies for such patients.<br /><br />In conclusion, this study identifies specific radiological predictors that can aid in the early detection and treatment of lung cancer using HRCT. These findings have implications for improving the diagnosis and management of patients with part-solid GGNs.

Asset Subtitle

Hirotsugu Notsuda

Meta Tag

Speaker Hirotsugu Notsuda

Topic Screening & Early Detection: Biomarkers/Imaging Technology

Keywords

lung cancer

HRCT

predictors

retrospective study

nodules

adenocarcinoma

C/T ratio

morphological features

early detection

treatment strategies