문의하기 아이콘
문의하기 텍스트
top 아이콘
coreline logo
close icon
Cookie Settings Information

When you visit our website, we store cookies on your browser to collect information. The information collected may relate to you, your device, or your preferences, and is primarily used to ensure the website functions properly and to provide a more personalized web experience.

However, you may choose to disallow certain types of cookies, which could affect your user experience and the services we are able to offer. You can click on each category below to learn more and adjust your default settings.

Please note that Strictly Necessary Cookies are essential for the basic functioning of the website and cannot be disabled (e.g., maintaining login sessions, remembering settings). For more detailed information about cookies, please refer to our [Privacy Policy].

Manage Consent Preferences
+Strictly Necessary CookiesAlways Active
These cookies are essential for the website to function properly and cannot be switched off in our systems. They are usually set only in response to actions you take, such as setting your privacy preferences, logging in, or filling out forms. You can set your browser to block or alert you about these cookies, but some parts of the site may not function properly as a result. These cookies do not store any personally identifiable information.
+Targeting Cookies
These cookies may be set through our site by our advertising partners. They are used to build a profile of your interests and show you relevant advertisements on other sites. These cookies do not directly store personal information but operate based on unique identification of your browser and device. If you do not allow these cookies, you will experience less targeted advertising.
+Performance
These cookies allow us to aggregate the number of visitors and traffic sources in order to measure and improve the performance of our website. They help us understand which pages are the most popular and how visitors navigate through the site. All information collected is aggregated and therefore anonymous. If you do not allow these cookies, we will not be able to monitor the performance of our site or know when you have visited it.
Save My Choices

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas

Authors
Young Joo Suh, Kyunghwa Han, Yonghan Kwon, Hwiyoung Kim, Suji Lee, Sung Ho Hwang, Myung Hyun Kim, Hyun Joo Shin, Chang Young Lee, and Hyo Sup Shim
Journal
Yonsei Medical Journal
Related Product

Research

Date Published
2024.02
Summary

Suh et al. evaluated CT radiomics for predicting Spread Through Air Spaces (STAS) in early-stage lung adenocarcinoma using preoperative CT scans. They retrospectively analyzed 521 patients (550 lesions) who underwent surgical resection. To build a predictive model, they extracted radiomics features from CT images using **AVIEW Research** software for semi-automated 3D tumor segmentation, ensuring precision by excluding large vessels and bronchioles. A radiomics score (Rad-score) was developed from selected features and combined with conventional clinical variables (e.g., lesion type, solid portion size) to create a comprehensive prediction model. Results showed that the combined model outperformed conventional clinical models, achieving higher AUC values in predicting STAS, particularly in temporal validation datasets. High Rad-scores were also associated with lower recurrence-free survival, indicating a poorer prognosis. This study highlights radiomics' added predictive value for STAS, with AVIEW aiding in accurate segmentation for robust preoperative assessment and potentially more personalized surgical planning.

Contact

Please leave your inquiry if you have any questions regarding our products, recruitment, investment, or any other matters.

Contact us