Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer
This magnetic resonance imaging multimodal fusion study aims to automate the staging of endometrial cancer using deep learning and to compare the diagnostic performance of deep learning with that of radiologists in the staging of endometrial cancer. This study retrospectively investigated 122 patien...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2932 |
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| author | Ziyu Zheng Ye Liu Longxiang Feng Peizhong Liu Haisheng Song Lin Wang Fang Huang |
| author_facet | Ziyu Zheng Ye Liu Longxiang Feng Peizhong Liu Haisheng Song Lin Wang Fang Huang |
| author_sort | Ziyu Zheng |
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| description | This magnetic resonance imaging multimodal fusion study aims to automate the staging of endometrial cancer using deep learning and to compare the diagnostic performance of deep learning with that of radiologists in the staging of endometrial cancer. This study retrospectively investigated 122 patients with pathologically confirmed early EC from January 1, 2025 to December 31, 2021. Of these patients, 68 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA, and 54 were in FIGO stage IB. Based on the Swin transformer model and its proprietary SW-MSA (shift window multiple self-coherence) module, magnetic resonance imaging (MRI) images in each of the three planes (sagittal, coronal, and transverse) are cropped, enhanced, and classified, and fusion experiments in the three planes are performed simultaneously. Selecting one plane for the experiment, the accuracy of IA and IB classification was 0.988 in the sagittal, 0.96 in the coronal, and 0.94 in the transverse position, and classification accuracy after the fusion of three planes reached 1. Finally, the automatic classification method based on the Swin transformer has an accuracy of 1, a recall of 1, and a specificity of 1 for early EC classification. In this study, the multimodal fusion approach accurately classified early EC. It was comparable to what a radiologist would perform and simpler and more precise than previous methods that required segmenting followed by staging. |
| format | Article |
| id | doaj-art-e0ef0cc6c51d4b30bec1aaf43e213fb4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e0ef0cc6c51d4b30bec1aaf43e213fb42025-08-20T02:31:08ZengMDPI AGSensors1424-82202025-05-01259293210.3390/s25092932Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial CancerZiyu Zheng0Ye Liu1Longxiang Feng2Peizhong Liu3Haisheng Song4Lin Wang5Fang Huang6Informatization Construction and Management Department, Huaqiao University, Quanzhou 362021, ChinaSchool of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Medicine, Huaqiao University, Quanzhou 362021, ChinaSchool of Engineering, Huaqiao University, Quanzhou 362021, ChinaSchool of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, ChinaDepartment of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, ChinaRadiology Department, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, ChinaThis magnetic resonance imaging multimodal fusion study aims to automate the staging of endometrial cancer using deep learning and to compare the diagnostic performance of deep learning with that of radiologists in the staging of endometrial cancer. This study retrospectively investigated 122 patients with pathologically confirmed early EC from January 1, 2025 to December 31, 2021. Of these patients, 68 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA, and 54 were in FIGO stage IB. Based on the Swin transformer model and its proprietary SW-MSA (shift window multiple self-coherence) module, magnetic resonance imaging (MRI) images in each of the three planes (sagittal, coronal, and transverse) are cropped, enhanced, and classified, and fusion experiments in the three planes are performed simultaneously. Selecting one plane for the experiment, the accuracy of IA and IB classification was 0.988 in the sagittal, 0.96 in the coronal, and 0.94 in the transverse position, and classification accuracy after the fusion of three planes reached 1. Finally, the automatic classification method based on the Swin transformer has an accuracy of 1, a recall of 1, and a specificity of 1 for early EC classification. In this study, the multimodal fusion approach accurately classified early EC. It was comparable to what a radiologist would perform and simpler and more precise than previous methods that required segmenting followed by staging.https://www.mdpi.com/1424-8220/25/9/2932endometrial cancertransformerdeep learningMRI multi-positionearly staging |
| spellingShingle | Ziyu Zheng Ye Liu Longxiang Feng Peizhong Liu Haisheng Song Lin Wang Fang Huang Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer Sensors endometrial cancer transformer deep learning MRI multi-position early staging |
| title | Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer |
| title_full | Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer |
| title_fullStr | Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer |
| title_full_unstemmed | Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer |
| title_short | Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer |
| title_sort | multimodal mri image fusion for early automatic staging of endometrial cancer |
| topic | endometrial cancer transformer deep learning MRI multi-position early staging |
| url | https://www.mdpi.com/1424-8220/25/9/2932 |
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