ORIGINAL RESEARCH article
Front. Oncol.
Sec. Thoracic Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1700843
This article is part of the Research TopicNovel Immune Markers and Predictive Models for Diagnosis, Immunotherapy and Prognosis in Lung CancerView all articles
To predict the spread through air spaces in lung adenocarcinoma using radiomic features from different regions of part-solid nodules: a multicenter study
Provisionally accepted
Shiyu Cui1Hongzheng Song2Fanxia Lin3Xiaomeng Han1Bo Wang1
Liang Zhang1Feng Hou1Enhao Kang1Jizheng Lin1
Henan Lou1*- 1The Affiliated Hospital of Qingdao University, Qingdao, China
- 2Qingdao Municipal Hospital Group, Qingdao, China
- 3Rizhao People's Hospital, Rizhao, China
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Background: This study aims to explore the value of radiomic features from different regions of part-solid nodules (PSNs) for predicting spread through air spaces (STAS) in lung adenocarcinoma. Methods: This retrospective analysis included 333 patients with PSNs lung adenocarcinoma pathologically confirmed in three hospitals. Data from one institution were utilized for training set (n=223), while the remaining two served as the external test set (n=110). The computed tomography radiomic features were extracted from different areas of the nodule (ground-glass, solid, gross, and perinodular). Three machine learning classifiers (support vector machine, light gradient boosting machine [LightGBM], logistic regression) were used to build predictive models. Model performance was assessed using accuracy and area under the curve (AUC). The DeLong test was used to determine differences in AUC values between models. The clinical benefits of models were assessed using decision curve analysis (DCA). Results: In the external test set, the radiomics model developed using combined features from ground-glass, solid, and perinodular regions with LightGBM classifier achieved an AUC of 0.840 (95% confidence interval [CI]: 0.758–0.921), which was better than the clinical model (AUC = 0.622, 95% CI: 0.494–0.750, P < 0.001) and other radiomics models. DCA indicated that this model has achieved a higher net benefit. Conclusion: The radiomics model developed using radiomic features of distinct solid and ground-glass components of PSNs and the perinodular region can contribute to identifying the STAS status in lung adenocarcinoma.
Keywords: Lung Adenocarcinoma, spread through air spaces, Part-solid nodules, Tomography, X-Ray Computed, Radiomics
Received: 19 Sep 2025; Accepted: 17 Oct 2025.
Copyright: © 2025 Cui, Song, Lin, Han, Wang, Zhang, Hou, Kang, Lin and Lou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Henan Lou, louhenan@qduhospital.cn
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