Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency
Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400874 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849230095787491328 |
|---|---|
| author | Renjie Xu Zhanlue Liang Dan Wang Rui Zhang Jiayi Li Lingfeng Bi Kai Zhang Weimin Li |
| author_facet | Renjie Xu Zhanlue Liang Dan Wang Rui Zhang Jiayi Li Lingfeng Bi Kai Zhang Weimin Li |
| author_sort | Renjie Xu |
| collection | DOAJ |
| description | Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on radiomics and clinical features improve the performance in distinguishing benign and malignant pulmonary nodules is not well studied. We propose an approach based on multimodal GCNNs that integrates patients’ lung computed tomography images with clinical information to differentiate between benign and malignant pulmonary nodules. Leveraging large‐scale and multisource data from multiple hospitals (i.e., 6033/290/524 patients for three hospitals respectively) enhances the diversity of features. Accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristic curve (AUROC) are used to evaluate the performance. We achieved the average accuracy/sensitivity/specificity/AUROC of 0.8612/0.9425/0.6786/0.9025 for the main dataset via the novel GCNN proposed, respectively, maintaining the robustness of the deep learning procedures. Especially for the external testing dataset (hospital 2/hospital 3), the specificity is much higher than comparison methods (0.6250–0.6731 vs. 0.2569–0.2788). The graph neural network‐based deep learning method holds the potential to assist clinicians, aiding in treatment planning, patient management, follow‐up strategies, resource optimization, and overall healthcare decision‐making. |
| format | Article |
| id | doaj-art-8b6af75564f645d48b0c9ee0181bde18 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-8b6af75564f645d48b0c9ee0181bde182025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400874Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity ProficiencyRenjie Xu0Zhanlue Liang1Dan Wang2Rui Zhang3Jiayi Li4Lingfeng Bi5Kai Zhang6Weimin Li7Department of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaInstitute of Advanced Research Infervision Medical Technology Co., Ltd. Shanghai 200032 ChinaDepartment of Pulmonary and Critical Care Medicine West China Hospital Sichuan University Chengdu 610041 Sichuan ChinaGraph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on radiomics and clinical features improve the performance in distinguishing benign and malignant pulmonary nodules is not well studied. We propose an approach based on multimodal GCNNs that integrates patients’ lung computed tomography images with clinical information to differentiate between benign and malignant pulmonary nodules. Leveraging large‐scale and multisource data from multiple hospitals (i.e., 6033/290/524 patients for three hospitals respectively) enhances the diversity of features. Accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristic curve (AUROC) are used to evaluate the performance. We achieved the average accuracy/sensitivity/specificity/AUROC of 0.8612/0.9425/0.6786/0.9025 for the main dataset via the novel GCNN proposed, respectively, maintaining the robustness of the deep learning procedures. Especially for the external testing dataset (hospital 2/hospital 3), the specificity is much higher than comparison methods (0.6250–0.6731 vs. 0.2569–0.2788). The graph neural network‐based deep learning method holds the potential to assist clinicians, aiding in treatment planning, patient management, follow‐up strategies, resource optimization, and overall healthcare decision‐making.https://doi.org/10.1002/aisy.202400874computed tomographydeep learninggraph neural networksmultimodal featurespersonalized diagnosespulmonary nodules |
| spellingShingle | Renjie Xu Zhanlue Liang Dan Wang Rui Zhang Jiayi Li Lingfeng Bi Kai Zhang Weimin Li Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency Advanced Intelligent Systems computed tomography deep learning graph neural networks multimodal features personalized diagnoses pulmonary nodules |
| title | Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency |
| title_full | Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency |
| title_fullStr | Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency |
| title_full_unstemmed | Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency |
| title_short | Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency |
| title_sort | classification of pulmonary nodules using multimodal feature driven graph convolutional networks with specificity proficiency |
| topic | computed tomography deep learning graph neural networks multimodal features personalized diagnoses pulmonary nodules |
| url | https://doi.org/10.1002/aisy.202400874 |
| work_keys_str_mv | AT renjiexu classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT zhanlueliang classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT danwang classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT ruizhang classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT jiayili classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT lingfengbi classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT kaizhang classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency AT weiminli classificationofpulmonarynodulesusingmultimodalfeaturedrivengraphconvolutionalnetworkswithspecificityproficiency |