Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.
Authors
Affiliations (8)
Affiliations (8)
- School of Automation, Guangxi University of Science and Technology, Liuzhou, Guangxi, China (J.X., Z.Y., X.D.).
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi, China (B.F., Y.L., S.L.).
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China (X.C., X.C.).
- IFlytek Technology Co., Ltd.Artificial Intelligence Cloud Service Platform R&D Building, No. 666 Wangjiang West Road, High-Tech Zone, Anhui Free Trade Pilot Zone, Hefei, China (F.W.).
- Nuclear medicine, Jiangmen Central Hospital, Jiangmen, Guangdong, China (X.D.).
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China (K.L.).
- Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong, China (W.Z.).
- School of Automation, Guangxi University of Science and Technology, Liuzhou, Guangxi, China (J.X., Z.Y., X.D.). Electronic address: [email protected].
Abstract
The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic information from whole-slide images (WSIs) to predict the epidermal growth factor receptor (EGFR) mutations of primary lung adenocarcinoma in patients with advanced-stage disease. Data from 396 patients with lung adenocarcinoma across two medical institutions were analyzed. The data from 243 cases were divided into a training set (n=145) and an internal validation set (n=98) in a 6:4 ratio, and data from an additional 153 cases from another medical institution were included as an external validation set. All cases included CT scan images and WSIs. To integrate multimodal information, we developed the DL-MFIF framework, which leverages deep learning techniques to capture the interactions between radiomic macrofeatures derived from CT images and microfeatures obtained from WSIs. Compared to other classification models, the DL-MFIF model achieved significantly higher area under the curve (AUC) values. Specifically, the model outperformed others on both the internal validation set (AUC=0.856, accuracy=0.750) and the external validation set (AUC=0.817, accuracy=0.708). Decision curve analysis (DCA) demonstrated that the model provided superior net benefits(range 0.15-0.87). Delong's test for external validation confirmed the statistical significance of the results (P<0.05). The DL-MFIF model demonstrated excellent performance in evaluating and distinguishing the EGFR in patients with advanced lung adenocarcinoma. This model effectively aids radiologists in accurately classifying EGFR mutations in patients with primary lung adenocarcinoma, thereby improving treatment outcomes for this population.