2023 World Conference on Lung Cancer (Posters)-P1.16. Artificial Intelligence-Assisted Quantitative CT parameters in Predicting the Degree of Risk of Solitary Pulmonary Nodules

P1.16. Artificial Intelligence-Assisted Quantitative CT parameters in Predicting the Degree of Risk of Solitary Pulmonary Nodules - PDF(Abstract)

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The study presented in this document focuses on using artificial intelligence (AI) to predict the degree of invasiveness of lung cancer in solitary pulmonary nodules (SPN). The early detection of lung cancer is important for effective treatment and patient survival. CT scans are currently used to diagnose SPN, but accurately determining the degree of invasiveness of lung cancer can be challenging. The study aims to investigate the usefulness of AI-assisted quantitative CT parameters in predicting the degree of invasiveness of lung cancer in SPN and improving patient outcomes.<br /><br />The study population consisted of patients with non-small cell lung cancer who underwent surgical resection. Preoperative CT scans were performed, and the histologic subtypes of the SPNs were determined by pathologists. AI was used to measure quantitative CT parameters, and statistical analysis was performed using predictive models.<br /><br />The results of the study showed that the CT value mean and CT findings were independently correlated with high-risk SPNs. Additionally, the results suggest that these parameters can guide therapeutic regimens, particularly the extent of surgical resection. However, further validation of these findings is necessary to determine their clinical applicability.<br /><br />In conclusion, the study highlights the potential of using AI-assisted quantitative CT parameters in predicting the degree of invasiveness of lung cancer in SPN. These findings could have implications for improving the accuracy of diagnosis and guiding treatment decisions for patients with SPN.

Asset Subtitle

Long Jiang

Meta Tag

Speaker Long Jiang

Topic Screening & Early Detection: Nodule Management

Keywords

artificial intelligence

lung cancer

solitary pulmonary nodules

early detection

CT scans

invasiveness

quantitative CT parameters

patient outcomes

non-small cell lung cancer

surgical resection