Dual-branch attention-enhanced network integrating CT images and clinicoradiographic features for preoperative ternary classification of IASLC grading in lung adenocarcinoma: A multicenter study.
OpenAlex 토픽 ·
Radiomics and Machine Learning in Medical Imaging
Lung Cancer Diagnosis and Treatment
Advanced Radiotherapy Techniques
[BACKGROUND AND OBJECTIVE] Precise preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grade in lung adenocarcinoma (LUAD) is essential for risk stratificatio
APA
Zhichao Zuo, Jinqiu Deng, et al. (2026). Dual-branch attention-enhanced network integrating CT images and clinicoradiographic features for preoperative ternary classification of IASLC grading in lung adenocarcinoma: A multicenter study.. Computer methods and programs in biomedicine, 280, 109337. https://doi.org/10.1016/j.cmpb.2026.109337
MLA
Zhichao Zuo, et al.. "Dual-branch attention-enhanced network integrating CT images and clinicoradiographic features for preoperative ternary classification of IASLC grading in lung adenocarcinoma: A multicenter study.." Computer methods and programs in biomedicine, vol. 280, 2026, pp. 109337.
PMID
41933519
Abstract
[BACKGROUND AND OBJECTIVE] Precise preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grade in lung adenocarcinoma (LUAD) is essential for risk stratification and surgical decision-making. However, existing approaches remain limited in reliably distinguishing among the three IASLC grades. We developed and validated a dual-branch attention-enhanced network that integrates computed tomography (CT) imaging and clinicoradiographic (CR) information for preoperative ternary IASLC grading.
[METHODS] This retrospective multicenter study included 1477 thin-slice chest CT examinations from three independent institutions. The proposed framework, DB-CRN, jointly models a preprocessed 3D nodule subvolume and patient-specific CR features through a two-stage dual-branch architecture equipped with a convolutional block attention module and channel attention blocks. Three comparative models were constructed: DB-NoCRN (image-only ablation), ML-CR (CR-only XGBoost model), and a conventional radiomics pipeline. Model performance was evaluated using the macro-averaged area under the receiver operating characteristic curve (macro AUC) and the Obuchowski index.
[RESULTS] DB-CRN achieved the best overall performance, with a macro AUC of 0.858 and an Obuchowski index of 0.844, outperforming DB-NoCRN (macro AUC 0.837; Obuchowski index 0.826), ML-CR (macro AUC 0.787; Obuchowski index 0.729), and the radiomics model (macro AUC 0.811; Obuchowski index 0.810).
[CONCLUSION] By fusing multiscale CT representations with CR priors under an attention-enhanced dual-branch design, DB-CRN enables robust preoperative ternary classification of IASLC grades in LUAD. This framework offers a noninvasive and reproducible tool to support risk-adapted surgical planning and personalized management, warranting prospective validation.
[METHODS] This retrospective multicenter study included 1477 thin-slice chest CT examinations from three independent institutions. The proposed framework, DB-CRN, jointly models a preprocessed 3D nodule subvolume and patient-specific CR features through a two-stage dual-branch architecture equipped with a convolutional block attention module and channel attention blocks. Three comparative models were constructed: DB-NoCRN (image-only ablation), ML-CR (CR-only XGBoost model), and a conventional radiomics pipeline. Model performance was evaluated using the macro-averaged area under the receiver operating characteristic curve (macro AUC) and the Obuchowski index.
[RESULTS] DB-CRN achieved the best overall performance, with a macro AUC of 0.858 and an Obuchowski index of 0.844, outperforming DB-NoCRN (macro AUC 0.837; Obuchowski index 0.826), ML-CR (macro AUC 0.787; Obuchowski index 0.729), and the radiomics model (macro AUC 0.811; Obuchowski index 0.810).
[CONCLUSION] By fusing multiscale CT representations with CR priors under an attention-enhanced dual-branch design, DB-CRN enables robust preoperative ternary classification of IASLC grades in LUAD. This framework offers a noninvasive and reproducible tool to support risk-adapted surgical planning and personalized management, warranting prospective validation.
MeSH Terms
Humans; Tomography, X-Ray Computed; Lung Neoplasms; Adenocarcinoma of Lung; Retrospective Studies; Male; Female; Neoplasm Grading; Middle Aged; ROC Curve; Aged
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