DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer
Abstract Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-08-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01204-2 |
| _version_ | 1797272238922137600 |
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| author | Liye Jia Xueting Ren Wei Wu Juanjuan Zhao Yan Qiang Qianqian Yang |
| author_facet | Liye Jia Xueting Ren Wei Wu Juanjuan Zhao Yan Qiang Qianqian Yang |
| author_sort | Liye Jia |
| collection | DOAJ |
| description | Abstract Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients’ survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives. |
| first_indexed | 2024-03-07T14:25:28Z |
| format | Article |
| id | doaj.art-ad046ff35c1c476b92f8cb5779ed9174 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-07T14:25:28Z |
| publishDate | 2023-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-ad046ff35c1c476b92f8cb5779ed91742024-03-06T08:06:58ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-08-011011115113010.1007/s40747-023-01204-2DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancerLiye Jia0Xueting Ren1Wei Wu2Juanjuan Zhao3Yan Qiang4Qianqian Yang5College of Information and Computer, Taiyuan University of TechnologyCollege of Information and Computer, Taiyuan University of TechnologyDepartment of Physiology, Shanxi Medical UniversityCollege of Information and Computer, Taiyuan University of TechnologyCollege of Information and Computer, Taiyuan University of TechnologyJinzhong College of InformationAbstract Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients’ survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives.https://doi.org/10.1007/s40747-023-01204-2Imaging genomicsDeep featuresConvolution cascadeAttention fusionSurvival prediction |
| spellingShingle | Liye Jia Xueting Ren Wei Wu Juanjuan Zhao Yan Qiang Qianqian Yang DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer Complex & Intelligent Systems Imaging genomics Deep features Convolution cascade Attention fusion Survival prediction |
| title | DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| title_full | DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| title_fullStr | DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| title_full_unstemmed | DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| title_short | DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| title_sort | dccafn deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer |
| topic | Imaging genomics Deep features Convolution cascade Attention fusion Survival prediction |
| url | https://doi.org/10.1007/s40747-023-01204-2 |
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