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renh@sumhs.edu.cnA total of 289 patient CT datasets were analyzed, and 15 optimal radiomic features were identified using ANOVA, correlation analysis, and random forest ranking, ensuring high discriminative power and clinical interpretability.
The proposed model demonstrated excellent performance (Accuracy: 0.98, Area Under the Curve: 0.99) in training set, demonstrating robust learning capacity and the ability to distinguish lesion subtypes from multidimensional radiomic features.
By leveraging serialized radiomic trends rather than isolated feature analysis, this study provides a new paradigm for early screening and personalized diagnosis of lung adenocarcinoma.
Open Accessrenh@sumhs.edu.cnA total of 289 patient CT datasets were analyzed, and 15 optimal radiomic features were identified using ANOVA, correlation analysis, and random forest ranking, ensuring high discriminative power and clinical interpretability.
The proposed model demonstrated excellent performance (Accuracy: 0.98, Area Under the Curve: 0.99) in training set, demonstrating robust learning capacity and the ability to distinguish lesion subtypes from multidimensional radiomic features.
By leveraging serialized radiomic trends rather than isolated feature analysis, this study provides a new paradigm for early screening and personalized diagnosis of lung adenocarcinoma.
Objective: To develop and validate a Transformer-based radiomics model for classifying lung adenocarcinoma subtypes from computed tomography imaging data. Methods: We retrospectively collected 289 computed tomography images of lung adenocarcinoma, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Correlation-based feature analysis was employed and identified 15 optimal radiomic features. A Transformer-based classification model incorporating multi-head attention and position-wise feed-forward Networks was subsequently constructed. Results: The proposed model achieved a training accuracy of 0.98, test accuracy of 0.914, training recall of 0.942, test recall of 0.874, training F1-score of 0.940, test F1-score of 0.871, training area under the curve of 0.99, and test area under the curve of 0.88. Conclusion: This Transformer-based radiomics model effectively classifies lung adenocarcinoma subtypes, aiding early screening, diagnosis, and personalized treatment strategies to improve patient prognosis.
Keywords: Computed tomography, lung adenocarcinoma subtype analysis, radiomic characteristics, deep learning, transformer network model
ISSN: 2957-5478
Volume 3, Issue 3
September 2025
Pages: 143-201
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