@article{TLCR120025,
author = {Jiading Xie and Shiqing Wang and Junjie Cheng and Qizheng Wei and Haitang Yang and Feng Yao},
title = {Development and validation of a dual-attention deep learning prediction model for noninvasive differentiation of pulmonary invasive mucinous adenocarcinoma from inflammatory pulmonary nodules},
journal = {Translational Lung Cancer Research},
volume = {15},
number = {6},
year = {2026},
keywords = {},
abstract = {Background: Pulmonary invasive mucinous adenocarcinoma (PIMA) is a rare subtype of lung adenocarcinoma that often mimics benign inflammatory pulmonary nodules (IPNs) on chest computed tomography (CT), leading to diagnostic challenges. We aimed to develop and internally validate a deep learning (DL)-based prediction model for noninvasive differentiation of PIMA from IPN.Methods: This retrospective single-center study included 443 patients with pathologically confirmed PIMA or IPN between January 2021 and December 2023. The reference standard was postoperative histopathology. A total of 1,409 CT slices were used for model development. The dataset was randomly divided at the patient level into training (80%) and validation (20%) cohorts for internal validation. A dual-attention deep learning model (SE-DAS ResNet) integrating spatial and channel-wise attention mechanisms was developed. Model performance was evaluated using the area under the curve (AUC), sensitivity, and specificity with their 95% confidence intervals (CIs), as well as accuracy and F1 score.Results: In the internal validation cohort, the SE-DAS ResNet achieved an AUC of 0.990 (95% CI: 0.978–1.000), an accuracy of 96.6%, a sensitivity of 100% (95% CI: 90.7–100%), and a specificity of 94.1% (95% CI: 84.1–98.4%). The model significantly outperformed a standard ResNet50 baseline (AUC 0.914, P},
issn = {2226-4477}, url = {https://tlcr.amegroups.org/article/view/120025}
}