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

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A study conducted by researchers at Tohoku University in Japan aimed to identify optimal predictors for lung cancer using high-resolution CT scans. The study focused on part-solid ground-glass nodules (GGNs), which often indicate lung cancer and require surgical treatment.<br /><br />The researchers conducted a retrospective study, analyzing data from 609 patients treated for non-small cell lung cancer (NSCLC) at Tohoku University Hospital between 2014 and 2018. The patients were categorized based on their HRCT radiological findings, including solid nodules, part-solid GGNs, and pure GGNs.<br /><br />The study explored the clinicopathological traits and lung cancer proportions for each category and assessed diagnostic predictors in 136 patients with part-solid GGNs. It also examined the relationship between consolidation-to-tumor ratio (CTR) and different types of lung tumors.<br /><br />The findings of the study revealed that part-solid GGNs with a CTR of 0.5 or less and clear tumor borders could be diagnosed as primary lung cancer based on CT imaging alone, without the need for preoperative histopathological diagnosis.<br /><br />These radiological predictors are recommended to be used when determining the treatment strategies for patients with part-solid GGNs. The study highlights the importance of early identification and treatment of lung cancer, especially small, low-density lesions. It also suggests that advances in low-dose HRCT have enabled better detection of lung cancer and influenced treatment choices for part-solid GGNs.

Asset Subtitle

Hirotsugu Notsuda

Meta Tag

Speaker Hirotsugu Notsuda

Topic Screening & Early Detection: Biomarkers/Imaging Technology

Keywords

lung cancer

high-resolution CT scans

part-solid ground-glass nodules

surgical treatment

non-small cell lung cancer

HRCT radiological findings

solid nodules

pure GGNs

consolidation-to-tumor ratio

preoperative histopathological diagnosis